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Metis

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Metis

Avg Rating:4.71 ( 18 reviews )

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Recent Metis Reviews: Rating 4.71

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4 Campuses

New York City

Python, Data Science, MongoDB, Git, Hadoop, SparkIn PersonFull Time24 Seats

Upon graduating, you will be comfortable with the iterative design process, data communication and visualization, and you will have had introductory exposure to modern big data tools and architecture.

Application Deadline:May 22, 2017

Course Details

Deposit
$1500
Financing
Partner with Skills Fund.

Payment Plan
Up-front or in three equal monthly installments.
Rebate
.
Scholarship
$3,000 scholarship for women, underrepresented minority groups, and veterans or members of the U.S. military.
Interview
Yes
Minimum Skill Level
Applicants must have some previous experience programming (writing code) and studying or using statistics
Placement Test
Yes

Data science has become the central approach to tackling data-heavy problems in both the business and academic worlds today. The intent of this course is to expose students to the data scientific approach to thinking about and solving problems, and to help students learn to think about data-heavy problems that they’ll encounter in the future. Students learn how data science is done in the wild, including data acquisition, cleaning, and aggregation, exploratory data analysis and visualization, feature engineering, and model creation and validation. Students will use the Python scientific stack to work through examples that illustrate all of these concepts, with real-life use cases. Concurrently, students will learn some of the statistical and mathematical foundations that power the data scientific approach to problem solving. WHY TAKE AN INTRO TO DATA SCIENCE COURSE? The practice of data science involves both a collection of skills and a mindset for tackling data-intensive problems (or problems looking in need of data-intensive solutions). Working through this course will give students the tools and necessary background to think about datasets that they encounter in meaningful ways, and will provide enough knowledge to continue their own data science learning in a vast, exciting, and rapidly evolving field. WHO IS THIS COURSE FOR? This course is intended for people with a basic understanding of data analysis techniques, and those who are interested in improving their ability to tackle problems involving multi-dimensional data in a systematic, principled way. They want to glean actionable, data-driven insights from that data. A familiarity with some programming language is helpful but unnecessary if the pre-work for the course is completed. No prior advanced mathematical training (beyond an introductory statistics course) is necessary.

Course Details

Minimum Skill Level
Students should have some experience with Python and have a passing familiarity with basic statistical and linear algebraic concepts.
Python, Data ScienceOnlinePart Time

Explore Data Science online training introduces common data science theory and techniques to help programmers, mathematicians, and other technical professionals expand their data science expertise. Participants go at their own pace during an overall 40-hour exploration of frequently used data science techniques. Explore Data Science is in the form of an interactive game, with participants advancing from one planet to another as they're actively engaged with real datasets and interactive tasks. They earn points, awards, and badges for completing missions along the way. Python is used to complete the interactive challenges. There’s no need to fuss with environment and package setup. Explore Data Science has code execution right in the browser, meaning that users get real results, in real time. OUTCOMES Upon completing Explore Data Science, participants will have: - A working conceptual understanding of the field of data science - An understanding of using data science to achieve a variety of analytic goals, including next-generation predictive and advisory approaches - Experience with a wide variety of data science techniques, from Distance Metrics to Genetic Algorithms and lots more in between.

Data Science, JavaScript, HTML, Git, CSSIn PersonPart Time6 Hours/week20 Seats

Data Visualization With D3.js is a part-time evening course was developed and is currently taught by Kevin Quealy, Graphics Editor at The New York Times. This is a course about data visualization and D3, the powerful Javascript library frequently used to create data visualizations on the web. We use the library extensively at the Times – its creator, Mike Bostock, was a colleague for three years. D3 can be challenging to learn, and lots of the difficulty comes with learning syntax and understanding data joins, probably the most fundamental aspect of D3. On top of that, there are the headaches associated with what Hadley Wickham has called the "fiddly bits" of charts: scales, axes, labels, annotations and the like. These basically come for free with software like Excel, R, Chartbuilder or Data Wrapper, but with D3 you're in charge of every pixel, which makes it incredibly powerful. Ultimately, the reason to learn D3 instead of or in addition to those other (great!) programs is because it enables you to tell stories and communicate information interactively in ways that are simply not possible outside a web browser. Making these kinds of applications is worth a little bit of extra headaches up front. WHO IS THIS COURSE FOR? This is a course for anyone who wants to be proficient in the use of D3 and seeks expertise visualizing quantitative information.

Course Details

Financing


Minimum Skill Level
Open to beginners, but students should have experience writing HTML, CSS and basic JavaScript.
Data ScienceIn PersonFull Time6 Hours/week22 Seats

OVERVIEW From robotics, speech recognition, and analytics, to finance and social network analysis, machine learning comprises one of the most useful scientific toolsets of our age. This course provides an overview of the core principles of machine learning using a hands-on, project-based curriculum. There is an intense focus on implementing popular machine learning algorithms to solve real problems using real data. WHO IS THIS COURSE FOR? This is designed for people working in any number of data-intensive fields, including consulting, finance, IT, healthcare, and logistics, as well as for recent college graduates and entrepreneurs interested or specializing in those fields. PREREQUISITES Firm knowledge of the Python programming environment. Basic understanding of vector and matrix algebra (how to add and multiply vectors/matrices), as well as basic understanding of the notion of a mathematical function (e.g., understanding what f(x)=x^2 or f(x) = sin(x) means). Basic calculus and linear algebra is helpful but not required (e.g., how to take derivatives, what a linear system of equations is, etc.). A quick refresher on these topics will be provided. (Note: Knowledge of statistics is not required for this course.) OUTCOMES Upon completion of the Machine Learning course, students have: An understanding of the basic principles of machine learning from both an intuitive and practical level. An intuitive understanding of common feature design principles for image, text, and speech data. An understanding of how to use popular machine learning and deep learning software packages in Python, as well as know how to implement several popular machine learning algorithms from scratch. Extensive experience applying machine learning algorithms to real data sets.

Application Deadline:June 27, 2017

OVERVIEW This course will serve as an introduction to basic statistical principles that are often used by data scientists and applied statisticians. Many of the concepts will be reinforced by using the statistical programming language R, one of the two most popular languages for Data Science. The intent of this course is to expose students to common statistical issues and teach them how to avoid statistical fallacies. We begin with a high-level overview of probability and common statistical estimates and then proceed to move advanced topics like multiple hypothesis testing, independence, sample size and power calculations as well as bootstrapping. By the end of the course, students will have a fundamental understanding of many of the statistical principles that underlie machine learning and data science. PREREQUISITES This course is open to beginners, but students should have some experience with coding (Python or R preferable but not required) and have a basic understand of calculus, linear algebra and probability. A brief review will be provided but prior experience would be very helpful. Students may opt to skip the pre-work if they: Have taken an introductory course to statistics or probability in college Are familiar with Linear Algebra (either coursework or work experience) Are able to do a hypothesis test to determine: If a coin is fair given 100 flips Calculate a confidence interval for the mean height given 100 observations Explain how to test if events are independent Use Bayes Rules to see what the probability of an event is given another event Fit a linear model in R. Otherwise, students should familiarize themselves with Chapters 1-6 of CK-12 Foundation’s Basic Probability and Statistics – A Short Course. Each chapter should take between 1-2 hours. OUTCOMES Upon completion of the course, students have: An understanding of basic statistical hypothesis testing and confidence intervals. The ability to model data using well known statistical distributions as well as handle data that is both continuous and categorical. The ability to perform linear regression and adjust for multiple hypothesis. An understanding of how to calculate the number of samples needed to achieve required sensitivity and specificity. An understanding of bootstrapping and Monte Carlo simulation.

San Francisco

Python, Data ScienceOnlinePart Time

Explore Data Science online training introduces common data science theory and techniques to help programmers, mathematicians, and other technical professionals expand their data science expertise. Participants go at their own pace during an overall 40-hour exploration of frequently used data science techniques. Explore Data Science is in the form of an interactive game, with participants advancing from one planet to another as they're actively engaged with real datasets and interactive tasks. They earn points, awards, and badges for completing missions along the way. Python is used to complete the interactive challenges. There’s no need to fuss with environment and package setup. Explore Data Science has code execution right in the browser, meaning that users get real results, in real time. OUTCOMES Upon completing Explore Data Science, participants will have: - A working conceptual understanding of the field of data science - An understanding of using data science to achieve a variety of analytic goals, including next-generation predictive and advisory approaches - Experience with a wide variety of data science techniques, from Distance Metrics to Genetic Algorithms and lots more in between.

Data Science, Git, CSSIn PersonFull Time6 Hours/week20 Seats

Data Visualization With D3.js is a part-time evening course was developed and is currently taught by Kevin Quealy, Graphics Editor at The New York Times. This is a course about data visualization and D3, the powerful Javascript library frequently used to create data visualizations on the web. We use the library extensively at the Times – its creator, Mike Bostock, was a colleague for three years. D3 can be challenging to learn, and lots of the difficulty comes with learning syntax and understanding data joins, probably the most fundamental aspect of D3. On top of that, there are the headaches associated with what Hadley Wickham has called the "fiddly bits" of charts: scales, axes, labels, annotations and the like. These basically come for free with software like Excel, R, Chartbuilder or Data Wrapper, but with D3 you're in charge of every pixel, which makes it incredibly powerful. Ultimately, the reason to learn D3 instead of or in addition to those other (great!) programs is because it enables you to tell stories and communicate information interactively in ways that are simply not possible outside a web browser. Making these kinds of applications is worth a little bit of extra headaches up front. WHO IS THIS COURSE FOR? This is a course for anyone who wants to be proficient in the use of D3 and seeks expertise visualizing quantitative information.

Course Details

Minimum Skill Level
Open to beginners, but students should have experience writing HTML, CSS and basic JavaScript.
Python, Data Science, MongoDB, HadoopIn PersonFull Time40 Hours/week22 Seats

Application Deadline:May 29, 2017

Course Details

Deposit
$1500
Financing
Partner with Skills Fund.

Payment Plan
Up-front or in three equal monthly installments.
Scholarship
$3,000 scholarship for women, underrepresented minority groups, members of the LGBTQ community, and veterans or members of the U.S. military.
Interview
Yes
Minimum Skill Level
Applicants must have some previous experience programming (writing code) and studying or using statistics
Placement Test
Yes

MACHINE LEARNING: ALGORITHMS AND APPLICATIONS OVERVIEW From robotics, speech recognition, and analytics, to finance and social network analysis, machine learning comprises one of the most useful scientific toolsets of our age. This course provides an overview of the core principles of machine learning using a hands-on, project-based curriculum. There is an intense focus on implementing popular machine learning algorithms to solve real problems using real data. WHO IS THIS COURSE FOR? This is designed for people working in any number of data-intensive fields, including consulting, finance, IT, healthcare, and logistics, as well as for recent college graduates and entrepreneurs interested or specializing in those fields. PREREQUISITES Firm knowledge of the Python programming environment. Basic understanding of vector and matrix algebra (how to add and multiply vectors/matrices), as well as basic understanding of the notion of a mathematical function (e.g., understanding what f(x)=x^2 or f(x) = sin(x) means). Basic calculus and linear algebra is helpful but not required (e.g., how to take derivatives, what a linear system of equations is, etc.). A quick refresher on these topics will be provided. (Note: Knowledge of statistics is not required for this course.) OUTCOMES Upon completion of the Machine Learning course, students have: An understanding of the basic principles of machine learning from both an intuitive and practical level. An intuitive understanding of common feature design principles for image, text, and speech data. An understanding of how to use popular machine learning and deep learning software packages in Python, as well as know how to implement several popular machine learning algorithms from scratch. Extensive experience applying machine learning algorithms to real data sets.

OVERVIEW Data science has become the central approach to tackling data-heavy problems in both business and academia. In this course, students learn how data science is done in the wild, with a focus on data acquisition, cleaning, and aggregation, exploratory data analysis and visualization, feature engineering, and model creation and validation. Students use the Python scientific stack to work through real-world examples that illustrate these concepts. Concurrently, students learn some of the statistical and mathematical foundations that power the data-scientific approach to problem solving. WHO IS THIS COURSE FOR? Introduction to Data Science is for anyone with a basic understanding of data analysis techniques and anyone interested in improving their ability to tackle problems involving multi-dimensional data in a systematic, principled way. A familiarity with a programming language is helpful, but unnecessary, if the pre-work for the course is completed (more on that below). No prior advanced mathematical training beyond an introductory statistics course is necessary. PREREQUISITES Students should have some experience with Python and have some familiarity with basic statistical and linear algebraic concepts such as mean, median, mode, standard deviation, correlation, and the difference between a vector and a matrix. In Python, it will be helpful to know basic data structures such as lists, tuples, and dictionaries, and what distinguishes them (that is, when they should be used). Students should skip the pre-work if they can accomplish all of the following: Write a program in Python that finds the most frequently occurring word in a given sentence. Explain the difference between correlation and covariance, and why the difference between the two terms matters. Multiply two small matrices together (e.g. 3X2 and 2X4 matrices). Otherwise, students should complete the following pre-work (approximately 8 hours) before the first day of class: Exercises 1-7, 13, 18-21, 27-35, 38,39 of Learn Python The Hard Way. Videos 1-6 of Linear Algebra review from Andrew Ng’s Machine Learning course (labeled as: III. Linear Algebra Review (Week 1, Optional). The exercises in Chapters 2 and 3 of OpenIntro Statistics. OUTCOMES Upon completing the course, students have: An understanding of problems solvable with data science and an ability to attack those problems from a statistical perspective. An understanding of when to use supervised and unsupervised statistical learning methods on labeled and unlabeled data-rich problems. The ability to create data analytical pipelines and applications in Python. Familiarity with the Python data science ecosystem and the various tools one can use to continue developing as a data scientist

Chicago

1033 West Van Buren 3rd Floor, Chicago, IL 60607
Python, Data Science, MongoDB, HTML, Java, Hadoop, SparkIn PersonFull Time40 Hours/week22 Seats

Application Deadline:May 29, 2017

Course Details

Deposit
$1500
Financing
Partner with Skills Fund.
Payment Plan
Up-front or in three equal monthly installments.
Scholarship
$3,000 scholarship for women, underrepresented minority groups, and veterans or members of the U.S. military.
Interview
Yes
Minimum Skill Level
Applicants must have some previous experience programming (writing code) and studying or using statistics
Placement Test
Yes

Data science has become the central approach to tackling data-heavy problems in both the business and academic worlds today. The intent of this course is to expose students to the data scientific approach to thinking about and solving problems, and to help students learn to think about data-heavy problems that they’ll encounter in the future. Students learn how data science is done in the wild, including data acquisition, cleaning, and aggregation, exploratory data analysis and visualization, feature engineering, and model creation and validation. Students will use the Python scientific stack to work through examples that illustrate all of these concepts, with real-life use cases. Concurrently, students will learn some of the statistical and mathematical foundations that power the data scientific approach to problem solving. WHY TAKE AN INTRO TO DATA SCIENCE COURSE? The practice of data science involves both a collection of skills and a mindset for tackling data-intensive problems (or problems looking in need of data-intensive solutions). Working through this course will give students the tools and necessary background to think about datasets that they encounter in meaningful ways, and will provide enough knowledge to continue their own data science learning in a vast, exciting, and rapidly evolving field. WHO IS THIS COURSE FOR? This course is intended for people with a basic understanding of data analysis techniques, and those who are interested in improving their ability to tackle problems involving multi-dimensional data in a systematic, principled way. They want to glean actionable, data-driven insights from that data. A familiarity with some programming language is helpful but unnecessary if the pre-work for the course is completed. No prior advanced mathematical training (beyond an introductory statistics course) is necessary.

Course Details

Minimum Skill Level
Students should have some experience with Python and have a passing familiarity with basic statistical and linear algebraic concepts.
OnlinePart Time

Explore Data Science online training introduces common data science theory and techniques to help programmers, mathematicians, and other technical professionals expand their data science expertise. Participants go at their own pace during an overall 40-hour exploration of frequently used data science techniques. Explore Data Science is in the form of an interactive game, with participants advancing from one planet to another as they're actively engaged with real datasets and interactive tasks. They earn points, awards, and badges for completing missions along the way. Python is used to complete the interactive challenges. There’s no need to fuss with environment and package setup. Explore Data Science has code execution right in the browser, meaning that users get real results, in real time. OUTCOMES Upon completing Explore Data Science, participants will have: - A working conceptual understanding of the field of data science - An understanding of using data science to achieve a variety of analytic goals, including next-generation predictive and advisory approaches - Experience with a wide variety of data science techniques, from Distance Metrics to Genetic Algorithms and lots more in between.

Seattle

83 S. King St., , Washington
Python, Data Science, MongoDB, HadoopIn PersonFull Time40 Hours/week25 Seats

Application Deadline:May 22, 2017

Course Details

Deposit
$1500
Financing
Partner with Skills Fund.

Payment Plan
Up-front or in three equal monthly installments.
Scholarship
$3,000 scholarship for women, underrepresented minority groups, members of the LGBTQ community, and veterans or members of the U.S. military.
Interview
Yes
Minimum Skill Level
Applicants must have some previous experience programming (writing code) and studying or using statistics
Placement Test
Yes
OnlinePart Time

Explore Data Science online training introduces common data science theory and techniques to help programmers, mathematicians, and other technical professionals expand their data science expertise. Participants go at their own pace during an overall 40-hour exploration of frequently used data science techniques. Explore Data Science is in the form of an interactive game, with participants advancing from one planet to another as they're actively engaged with real datasets and interactive tasks. They earn points, awards, and badges for completing missions along the way. Python is used to complete the interactive challenges. There’s no need to fuss with environment and package setup. Explore Data Science has code execution right in the browser, meaning that users get real results, in real time. OUTCOMES Upon completing Explore Data Science, participants will have: - A working conceptual understanding of the field of data science - An understanding of using data science to achieve a variety of analytic goals, including next-generation predictive and advisory approaches - Experience with a wide variety of data science techniques, from Distance Metrics to Genetic Algorithms and lots more in between.

OVERVIEW This course is about data visualization and D3, the powerful JavaScript library frequently used to create data visualizations on the web. D3 can be challenging to use, and much of the difficulty comes with learning syntax and understanding data joins (the most fundamental aspect of D3). On top of that, there are the headaches associated with the minutiae of charts: scales, axes, labels, annotations and the like. But that extra effort becomes worth it when you realize that with D3, you're in charge of every pixel. That power and control enables you to tell stories and communicate information interactively in ways that simply are not possible outside of a web browser. WHO IS THIS COURSE FOR? Anyone who wants to be proficient in the use of D3 and seeks expertise visualizing quantitative information. This course will not train you to become a data scientist. (For that, we offer a licensed, accredited bootcamp). Even if you're already an expert in JavaScript and D3, this course will help you select the right form, hone in on the best way to communicate your idea, and build it. PREREQUISITES This course is open to data visualization beginners, but you should have experience writing HTML, CSS, and basic JavaScript. For HTML/CSS, you should know how to work with the DOM and be familiar with CSS selectors. For JavaScript, you should be familiar with variables, data types, arrays, loops, and conditional statements and you should have worked with functions and objects. For Git and GitHub, you should be familiar with forking, cloning, pull requests, and branches. Finally, you should have a general idea of working with and manipulating structured data. If you don't yet have this fluency, we strongly recommend completing the following free tutorials in advance of the course start: Git & Github: Complete Documentation HTML & CSS: Web track - Codecademy Javascript: Codecademy Intro to D3 Command-line (first two sections): Codecademy OUTCOMES Upon completion of the Data Visualization with D3.js course, students have: A working conceptual understanding of the field of data visualization, particularly as it relates to the internet and mobile devices. Deep knowledge of the forms and techniques of data visualization and effective display of quantitative information, with a specific focus on bar charts, scatterplots, area charts, line charts, choropleth and bubble maps, small multiples, annotation principles – and the strengths and weaknesses of each. Proficiency in using D3 to make static and interactive charts and documents and in using JavaScript to process and manipulate data.

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Our latest on Metis

  • Episode 12: March 2017 Coding Bootcamp News Roundup + Podcast

    Imogen Crispe3/31/2017

    Haven’t had time to keep up with all the coding bootcamp news this March? Not to worry– we’ve compiled it for you in a handy blog post and podcast. This month, we read a lot about CIRR and student outcomes reporting, we heard from reporters and coding bootcamp students about getting hired after coding bootcamp, a number of schools announced exciting diversity initiatives, and we added a handful of new schools to the Course Report school directory! Read below or listen to our latest Coding Bootcamp News Roundup Podcast.

    Continue Reading →
  • How to Get into Data Science Bootcamp Metis

    Liz Eggleston3/3/2017

    Unlike many web development bootcamps, data science programs often require some background knowledge in statistics and coding. So we’re diving into the admissions process at data science bootcamp Metis, and getting insights from alumni Deepak and Emily, who explain their journeys from the Metis interview to their new jobs at Facebook and Etsy.

    Amy, tell us about your role as the director of admissions at Metis.

    Amy: As Director of Admissions, I'm usually the first interaction students have with Metis. I walk students through the different stages of the admissions funnel and work one-on-one to help them determine what course will be a great fit for them. Sometimes it's the immersive bootcamp; other times it's our professional development courses.

    In some cases, when they're not yet ready for the immersive bootcamp, I provide resources to help them build up their backgrounds. My goal is to help students create a career path of success and help them move along the application process as seamlessly as possible.

    Can you walk us through the steps of the Metis admissions process?

    Amy: There are three steps of the application process.

    1. Submit a written application. There are open-ended questions and Python programming exercises. We ask students to have some programming experience and a statistical thinking background, and we’re able to see that in the first round of the application.
    2. Coding Challenge. If they move on to the next round, they are given 48 hours to complete a coding challenge. The challenge involves a technical assessment, an exploratory data analysis, and a data science project.
    3. Interview. Once we have those materials, we’ll set up a 30-minute interview with one of our data scientists to get to know the student more, help us determine if they are a good fit for Metis, and see if we are the right fit for the student.

    It takes two to three weeks from the time you submit an application to receiving a decision.

    What level of programming or statistics do you want to see in an ideal applicant?

    Amy: We've intentionally left this pretty open ended. Although we don't require students to have a background in Python specifically, we want to see how you tackle something you might not be familiar with. We do expect that students come in with some programming experience and some background in statistics, and we've designed the admissions process to delve deeper into those areas. You don’t need to have worked as a web developer, but if you don’t like programming, then this is not the best profession for you. We can test these skills in the admissions process based on the questions that we ask.

    What sort of backgrounds and experience in coding do Metis students have?

    Amy: Students come to Metis with various backgrounds. Some have the usual computer science, economics, math, and physics backgrounds, but then we also see students with non-traditional paths to data science: finance, history, English, or psychology.

    One thing our students have in common is their interest in technical skills and their statistical thinking. You may not have taken a formal statistics class, but this is something that you're very curious about, and you're able to show that your path has lead you towards this career in Data Science, even if your degree doesn't show that.

    We have about 60 hours of prework to help students develop a stronger foundation in areas that they might be weaker in. This includes Python, linear algebra, and statistics.

    Are presentation skills and communication skills important to being a data scientist?

    Amy: Data scientists, in general, need to be strong communicators both visually and verbally, because data scientists are really the translators within their company. They're taking the insights that they find in the data and communicating them to a wide audience of folks who may not be familiar with a lot of the technical aspects but need the insights from that data to make decisions and drive value.

    During the bootcamp, we focus on five different projects where students actually present their findings, because we want them to be comfortable communicating their findings in front of people. During the interview process, we have a project challenge that they actually present to our instructor.

    Emily, I'm curious what you were up to before you went to Metis?

    Emily: I came straight to Metis after getting a Master’s in Organizational Behavior, and before that, I got an undergraduate degree in Decision Sciences. I did have a lot of statistics experience, because I was a Statistics minor in college and I took econometrics courses in grad school. Conducting psychology research also requires a lot of statistics.

    I definitely didn't have a traditional computer science background. I had been using R, which is a language many data scientists use. I had taken one class in Python, and that was four years ago.

    What I really liked about Metis was that it filled in those gaps I had in Python and machine learning. I hadn’t done those more advanced techniques beyond regression.

    Did you do your coding challenge in Python or in R?

    Emily: I did do it in Python. I had a little bit of exposure to Python, and it is somewhat similar to R. I definitely needed some online references, but just to look up how to do an IF ELSE statement with Python syntax. I also knew that the Metis bootcamp was going to be in Python, so if I wasn't comfortable completing this coding challenge in Python, it probably wouldn't have been a good fit.

    Was the 60 hours of Metis prework helpful to you?

    Emily: Definitely. I was strong on statistics and weak on computer science. There were some students with opposite backgrounds. So the prework was a good refresher, but I really needed the Python, computer science, and machine learning focus.

    Deepak, how about you? You had actually worked as a web developer before but you more recently working in education. How did you decide to make the switch?

    Deepak: My background is in math as an undergrad, and then I started working with databases. I was working on end-to-end projects, analyzing and visualizing data, and presenting that to an audience. During that phase, I had to transition to a web developer on my own, but throughout that time I was working with a lot of data.

    Even during grad school in economics, I had a lot of exposure to economic data. So I've been playing with data for a long time, and that's what motivated me to come to Metis. Although I had a lot of data analysis skills, I didn't have a lot of computer science, machine learning, or predictive modeling skills.

    Could you get through the Metis take-home challenge with your Python skills?

    Deepak: To be honest, I didn’t have any Python skills before I started the take-home challenge. When I spoke with Amy, she said, "You can code the take-home challenge in any language you want, but we recommend Python." So I started looking at Python. It was a steep learning curve for me, but I did my take home challenge in Python. Because I had knowledge in other programming languages, I was learning the syntax.

    What did the Metis application tell you about the Metis bootcamp experience?

    Deepak: The intensity of the application gives you an idea about how rigorous the bootcamp will be. I had looked into other data science bootcamps as well, and some of them seemed like they would take anyone. At Metis, the take-home challenge and the interview process was challenging. You really had to put in effort to get through it, and when I did get admitted, I felt like I deserved to be in the program.

    Emily: I had a very similar feeling. Also, my brother is a data scientist; he'd spoken at Metis before and been to their career day, and had great things to say about Metis. I trusted his judgment. He told me that after 12 weeks, Metis students were giving really good, really strong presentations. That gave me a great feeling about Metis.

    What stood out to you about Metis when you were looking for data Science bootcamp? Did you look at other data science bootcamps?

    Emily: Looking at the landscape, there aren’t as many data science bootcamps as there are web development bootcamps, so I didn’t have to compare 20 different schools. I knew I wanted to work in New York, so that limited it further. I looked briefly at other ones, but I knew Metis was competitive. I looked at the syllabus, and Metis taught exactly what I wanted to learn: machine learning and Python. I looked at NYC Data Science Academy, but they covered some material that I already knew (they teach R as well as Python).

    Deepak: In my research, start date was important, and I looked at where alumni landed jobs; it looked really strong. The key factor for me was talking with the Metis data scientists during the interview. I had a chance to ask them honest questions, and they were really honest and open in their answers.

    Did either of you want to work in a specific role or at a specific company when you graduated from Metis? What was your goal?

    Deepak: I was not looking at a specific role or a specific company. My goal was to gather strong data science skills, and gradually put myself into the field.

    Emily: I wanted a Data Analyst or Data Scientist position. I didn't have a specific company in mind, but I had looked at previous job descriptions at a few companies I was familiar with. I found more job descriptions that required Python than R, so that pushed me towards Metis.

    Once you started at Metis, what were your classmates like? Was everyone on the same level as you?

    Deepak: For my class, I would say yes. Some people were stronger in programming, while other people were stronger in statistics, but overall I think we were on the same page when we started the program.

    Emily: It was quite a diverse group. I had classmates who were my age, a couple of years out of college. We also had people with PhDs in statistics, someone with 20 years of marketing experience, and people with undergrad degrees in computer science. That diversity is one thing that’s really appealing to me about Metis– all these people came to Metis because they were still missing some components.

    Emily, could you tell us about your favorite project that you built while you were at Metis?

    Emily: My final project was my favorite project. It was sort of “meta,” because I actually did a project about data science freelancers. There's a website called Upwork.com that has lots of freelancing jobs (including data science). I used the Upwork API to gather all the data scientists’ profiles and all of the current jobs posted on the website. My final project was to make a tool that actually matches these data science freelancers to jobs, tailored to their skill set.

    That project definitely evolved over time. When I started gathering this data using the API,, I definitely didn't know what the final project would be. As I iterated through it with my instructors and other students, I was trying to figure out "how can I make this as useful and impactful as possible, rather than just give summaries of this data?"

    I used Python for most of my project, but I also ended up using R to make the final web application. I used a library called Shiny, which makes it easy to build an interactive dashboard.

    Did you find that your final project was important during jobs interviews?

    Emily: Definitely. The other great thing about Metis is you also have to write a blog, which I had been meaning to do. When I went to job interviews, I often ended up talking about my project and the challenges. I would give people the link to my application and other projects.

    Deepak what was your favorite project?

    Deepak: My favorite project was also my final project, because I had a lot of time to work on it, and the topic was something that I was really interested in: soccer statistics. The biggest challenge was getting the proper data and the proper amount of data. I had to reach out to different data scientists across the globe to get the data I wanted, which was interesting.

    After talking with the instructors, we ended up expanding the project and creating a soccer betting application. Based on past soccer statistics, users bet on upcoming games and see over time, whether your betting strategy will make you money in the future. I absolutely loved it. The one-on-ones with instructors really helped me understand everything in more detail.

    Also, we did presentation practice, which was really helpful and you could see a drastic amount of improvement. And it was all due to the comments and tips from the instructors at Metis, which are the skills that I'm going to carry forward into my job in the future.

    What are you up to after graduating, Deepak?

    Deepak: I am currently a Business Intelligence Engineer at Facebook. Most of my work is analyzing data, creating pipelines, getting data from different data sources, and visualizing that for our stakeholders. There's just a little bit of data science involved in my job now, but this was a great opportunity for me when it came. I couldn't refuse. I can always build on my data science skills in the future.

    Deepak, do you feel like you get to use your skills learned at Metis?

    Deepak: I haven't used my skills yet because I'm very new to my job, but there are other people in my team who went to data science bootcamps as well. They definitely say that they use their data science skills, so I'm pretty sure I'm going to use them in future.

    Emily, tell us what you are working on and where you're working now.

    Emily: I'm on the Analytics team at Etsy, working with about 20 analysts right now, and we essentially embed with other teams. I'm working with our Search team, which is really exciting because search is a huge part of the Etsy experience. Unlike most companies, Etsy’s data scientists are almost all PhDs in machine learning and computer science.

    As Analysts, we design and analyze experiments and serve as the “quantitative voice” for our partner teams. Two things I’ve worked on is experimenting with Etsy’s search ranking system and work on opportunity sizing. I hadn't really had experience with big data before, so the learning curve was steep at first. The primary table I use has six billion rows. I've also started using Scala to write Hadoop jobs to get the data that's not stored in a SQL database. That's really exciting.

    Amy, are you looking for applicants you think are going to able to land certain types of jobs when they graduate?

    Amy: Metis is a vocational training program. Our job is to help students gain the skills they need in 12 weeks to become a data scientist. Most of those positions are entry level data scientists or a data analyst.

    We’re definitely trying to identify students who will be successful in the job search after 12 weeks at Metis. 12 weeks is not a lot of time, but the whole idea is that you can adapt quickly when you get into the workforce. We’re looking for passion, communications skills, technical abilities, curiosity, and grit. All these things easily translate into success after the bootcamp.

    For data science beginners, what's your advice to prepare for the Metis application?

    Deepak: First, understand what data science is. Don't go into the field just because people say it's classy or you make a lot of money. Do your homework, make sure you're passionate about analyzing data and telling stories with data. If you have that passion, and you have basic computer science and statistics skills, I would definitely tell anyone to join the field. There's going to be a huge demand for data scientists in the future.

    Emily: There are two sides to this answer. I could recommend specific Coursera courses or books. For example, the John Hopkins Coursera course is great.

    The other important prep is to do projects. Don't just read about data science; find a question you're interested in answering with data. There are a lot of data sets that you can basically download. Start playing around with them, as that will force you to learn these skills, and hopefully you’ll enjoy it. If you don’t enjoy it, then this might not be the best path for you. Try to figure out what questions are interesting for you and practice ways of investigating – that would also be great to show in an admissions process. There was a great Quora answer about this subject.

    I would be impressed with someone who says, "I don't have a formal background, but I got really interested in data so I developed these predictive modeling skills because I wanted to be able to predict this specific thing that I'm really interested in."

    At the same time, don’t be intimidated by data science job descriptions that say you need a PhD in machine learning and have a ton of packages on GitHub. That's not the case. Metis is not meant to take you from 0 to 60, but if you have the curiosity and the grit, don't be afraid to try. Don't feel that just because you don't have a traditional background, you can't make it in this field.

    Deepak: Metis will definitely provide you with the tools and skills required for your first data science job. Like Emily said, don't get intimidated. As long as you have those basic skills that we've mentioned and a very keen interest, Metis will provide you with all the skills required.

    Amy: I definitely agree with both Deepak and Emily. Do your research, reach out to people on LinkedIn, attend meetups. If this is something you're interested in, then you need to immerse yourself in the world of data science and and really understand the different paths that are out there.

    I love when students reach out to me. I'm always willing to have one-on-one conversations to talk about how to move from step A to step B and help them improve in their careers.

    Secondly, just do as much coding as possible. Get familiar with Python, do data science projects. Once you get into Metis, you don't want to spend time troubleshooting code. You want to really focus on learning the data science concepts.

    Find out more and check out Metis reviews on Course Report. Check out the Metis website.

    About The Author

    Liz pic

    Liz is the cofounder of Course Report, the most complete resource for students considering a coding bootcamp. She loves breakfast tacos and spending time getting to know bootcamp alumni and founders all over the world. Check out Liz & Course Report on Twitter, Quora, and YouTube

  • Your 2017 #LearnToCode New Year’s Resolution

    Lauren Stewart1/3/2017

    It’s that time again! A time to reflect on the year that is coming to an end, and a time to plan for what the New Year has in store. While it may be easy to beat yourself up about certain unmet goals, one thing is for sure: you made it through another year! And we bet you accomplished more than you think. Maybe you finished your first Codecademy class, made a 30-day Github commit streak, or maybe you even took a bootcamp prep course – so let’s cheers to that! But if learning to code is still at the top of your Resolutions List, then taking the plunge into a coding bootcamp may be the best way to officially cross it off. We’ve compiled a list of stellar schools offering full-time, part-time, and online courses with start dates at the top of the year. Five of these bootcamps even have scholarship money ready to dish out to aspiring coders like you.

    Continue Reading →
  • 8 Companies Who Actually Love Hiring Coding Bootcampers

    Liz Eggleston12/22/2016

    In our recent Student Outcomes survey, alumni reported that they were working in over 650 different companies! Of course, you may have read recent press citing companies like Google who apparently aren’t willing to invest in junior technical talent from coding bootcamps (we happen to know that coding bootcamp grads have been hired at Google and Salesforce, but that’s not the point)... 

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  • July 2016 Coding Bootcamp News Roundup + Podcast

    Imogen Crispe8/1/2016

    Welcome to the July 2016 Course Report monthly coding bootcamp news roundup! Each month we look at all the happenings from the coding bootcamp world from new bootcamps to big fundraising announcements, to interesting trends. This month the biggest trends this month are initiatives to increase the diversity in tech, some huge investments in various bootcamps, and more tech giants launching their own coding classes. Read below or listen to our latest Coding Bootcamp News Roundup Podcast!

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  • Metis Career Services: Getting Hired as a Data Scientist with Capital One Labs

    Liz Eggleston5/13/2016

    You’ve all heard about judging coding bootcamps and data science bootcamps based on their outcomes (aka does a bootcamp get students jobs), but we rarely get the perspectives of both the bootcamps and the employers at the same time. So we were really excited to have our guests, Megan Ayraud of Metis and Brennan Biddle of Capital One Labs, join us for a video interview.

    Meet Your Panel:

    Megan is the head of careers at Metis, a Data Science bootcamp in New York, San Francisco and now in Chicago. Megan helped develop the 12-week Careers Curriculum, which is concurrent with the bootcamp curriculum at Metis, and provides a lot of post-graduation support for Metis students.

    Brennan is a Data Science and Data Engineering Recruiter at Capital One Labs in New York, which is an employer partner of Metis.

    It can be really difficult to sort through job placement rates and stories about bootcamp grads who've gotten jobs after graduating. In this live Q&A, Megan and Brennan answered all of our questions about how Metis prepares future data scientists for their first jobs, and how an employer like Brennan can hire effectively from a coding bootcamp like Metis.

    Q&A

    Megan and Brennan, can you first tell us a little bit about your roles. Megan, what does it mean to be the Head of Careers at Metis?

    Megan: As the Head of Careers at Metis, I work with all of our students and graduates on helping them through the job search within the current data science market. As you mentioned, I've built a curriculum that goes alongside our data science curriculum, and it's all about developing them in their career and getting them prepared for the job search post-Metis.

    There are workshops built into the 12-week bootcamp, one-on-one meetings, mock interviewing, and we bring speakers in who are data scientists in the community to help shine a light on what data science is like in their companies. I organize a career day at the end of every bootcamp cohort and we invite employers to meet our students and network. I also work with all of our students individually, along with my career advisors. Shout out to Metis’s amazing career advisors, who help find our grads amazing jobs.  

    Likewise, Brennan, what does it mean to be a Data Science & Data Engineering Recruiter right now?

    Brennan: The fact is that the term data science means a million different things to a million different people. Even internally within Capital One, we're a big machine. Different groups in different corners of our business define data scientists differently. The hardest part is finding the right candidates that match up with specific needs of each individual group within our organization and finding where those technical strengths lie.

    Brennan, are there baseline requirements needed to be a data scientist at Capital One? Do candidates need a certain type of degree?

    Brennan: Really good question. I can't speak to everywhere, but here at Capital One, we want our data scientists to essentially wear a research and development hat coming into the door. Our Data Science organization is the most random, diverse, crazy, eccentric group of people you'll ever work with. Just sitting in my little pod alone, we have a Neuroscience Ph.D., a Math Ph.D., and a former high school math teacher. There's not one specific type of a person or background, either personal or professional, that we're looking for. I usually tell candidates the weirder background you come from, usually the more successful you'll be as a data scientist here at Capital One.

    That actually aligns really well with bootcamps because everyone who graduates from a  bootcamp has a past life or career.

    Megan, how do you coach students on incorporating their past lives with their future lives as data scientists in just 12 weeks?

    That imposter syndrome is a common problem and we work with our students a lot, not only in groups and workshops, but also a lot of one-on-one mock interviewing. When we’re working on the technical interview, we actually bring data scientists in and have them actually go through a real live mock interview as students can expect in the real world.

    That helps build a lot of confidence in soft skills, and how to present your background and tie in your past life to your new life as a data scientist. It's amazing to see that transformation. A lot of people say, "I was a math teacher in my former life, how could I possibly tie that into data science?" As career advisors, we see that connection so clearly, but it's our job to help them develop that narrative, practice it, build confidence, and then be able to tell that story to employers.  

    Brennan, have you hired Metis graduates?  

    Brennan: We have hired a few!

    What types of roles are Metis grads going into at Capital One Labs? Does Capital One have a Junior Scientist role?

    Brennan: Yeah, we do. Even for a “junior” data scientist, we're looking for a really strong foundational data science background. This for us is someone that is really good a Python, has at least a little bit of exposure to working in a Hadoop framework, and someone that is interested in functional languages.

    We do have junior level roles (although they don't pay like junior level roles). We anticipate hiring someone that we can grow, mold and develop. And we find that places like Metis do a really good job of building that foundation that we can build upon.  

    How did you meet the students that you've hired from Metis? At a career day or did you guest-lecture at Metis?

    Brennan: The answer is both. Metis has had a few cohorts and we've been working with them throughout. Our team goes to the Metis classroom, introduces ourselves to the cohorts, tells them what we do, and learn about the projects they're working on. The last cohort came in for a career day to see our office space and interview and they asked a lot of questions of different data scientists on different teams. Metis has a career night that we attend every single cohort as well. At this point, we have a pretty good relationship with them at different stages in their process.

    We work a lot on building brand within the bootcamp as well. Now that we're hiring, we're starting to build a bit of a reputation and by bringing people in to see our space, telling them about the projects that we do, I think that we're starting to generate a lot of interest.  

    Megan and Brennan, I want to talk about what goes into a technical interview for data scientists. What would I expect as a data science candidate?

    Megan: I see employers do things really differently. There is a lot of overlap from coding interviews for software engineer roles to data science. I see most of our grads having to do some level of whiteboarding. They may not necessarily be coding on the whiteboard but instead walking through a solution, how you would approach it, and how you would get to an answer. At Metis, students do whiteboarding practice almost every day. They’re pair programming all the time at Metis, so they feel comfortable doing that with an employer on an interview.

    We also see companies incorporating case studies into the interview process. Case studies are just a huge part of the data science world. Sometimes those are in the office, sometimes they’re take-home projects that the candidate has to be ready to present  in front of an audience at the interview. Communication is such a huge part of the data science role, so candidates have to show communication skills, logic and technical skills, and also portfolio review. At Metis, students build five projects along the way so that they have this robust portfolio to show employers. I see a mix of those three things happening in data science interviews.  

    Brennan, of those three things: communication skills, technical skills, and portfolio, which stood out the most about the Metis students you have hired?

    Brennan: I hate to give a floppy answer but it really is a mix of all of three. Our interview is a four step program. I warn our candidates that during our interview process, they may be asking themselves, "Am I interviewing for a software engineering job or for a data science role?” We really do test those Python and Scala skills in our candidates, but our interview process is pretty detail-oriented and by the end of it we have a good feel for their analytical abilities, their mathematical abilities and their culture fit as well. And we try to paint a nice, complete picture when making our final decision on our candidates.

    Do any of the Metis students you’ve hired have PhD’s?

    Brennan: Megan, correct me if I’m wrong, but I don't believe they do. The people that we've hired don’t come from traditional computer science background.

    Megan: One person has a Masters degree in Math, which is relevant, but not computer science. Another person had a bachelor's degree.  

    Has it ever been a concern for the Capital One Labs team that those students didn’t have PhDs?

    Brennan: Generally speaking, in our data science community here at Capital One, not many of them come from a traditional computer science type of background. Of our Ph.D.s, we legitimately have a Ph.D. in theology all the way through to astrophysics and everything in between. A few of those are computer science backgrounds but not all of them.

    Brennan, how do you ensure that new hires at Capital One Labs are supported as they continue to learn after they had graduated from Metis?

    Brennan: Absolutely- I think that's the hardest thing for me to portray to candidates as a recruiter. At Capital One Labs, we have a large community of data scientists all over the country- San Francisco, Chicago, New York, DC, Dallas- and that community of data scientists is really supportive. We know most of one another on a first name basis. We have our own internal Big Data Academy as well. We have meet ups internally where we all meet in one city and catch up with one another. Capital One is a great place to work as a data scientist for several reasons, but the one that's hardest to portray is the community within the Capital One culture.  

    Capital One is a really big company but as a data scientist it feels more like you're working at a big start up with around 100 to 200 people as opposed to a huge bank with 50,000 employees. They all know each other very well, have lunch together every single day, and travel to see one another all the time. It's a really tight-knit community of people.  

    Megan, as you build Metis's employer network, how important is it that your employer partners provide mentorship for your graduates?  

    Megan: That's something we definitely look out for, and we hope to partner with those types of companies. We see our students and our grads being selective for those types of opportunities as well. Unless they're coming to Metis with a lot of professional experience already under their belt – and we definitely do have that profile of a candidate – then they may be okay in a less mentorship-driven company. But to find an employer like Capital One Labs is just so awesome for our students.

    We've also started working with employers on building out apprenticeship programs and that has been very successful. And really the model works. We have tried 8 to 12 week maybe even 16-week apprenticeships where they can come in, work on real projects for the team, and there's a heavy mentor component to it. They can get up to speed in more of a comfortable but accelerated pace, and when they come on full time they're ready to go from Day 1.

    Do you see most Metis graduates going into straightforward Data Scientist roles or are there other types of roles that students can get when they graduate from Metis?

    Megan: The title that we most often see is Data Scientist. We also see Data Analysts or some analyst position. Data Engineer is usually the third title we see.

    What have you seen your most successful Metis graduates do differently that really sets them apart as candidates for data scientist roles?

    Megan: First, how do you define “successful?” In my eyes it's a student who lands a competitive data scientist position at a company that they're super passionate about within the first month or two of graduating from Metis. When I see people following that path, they've been incredibly engaged during the bootcamp and also they're all-in with careers. They're just soaking up the advice and all the tidbits of information that we that we put out there.

    The most successful Metis grads are juggling and balancing a lot of or spinning multiple plates at once. They're building their projects, they're learning, they're involved with the career team, they're getting their resumes together, they're interviewing. Juggling everything is a lot to ask, but they're able to handle it gracefully and confidently.

    After Metis, they have a plan. They're not taking a few months to just relax, they're aggressively attacking their search and taking all the advice that we've given them and totally utilizing their network that we helped them build and just going after it. I see those people with the most offers, having the most options, and feeling most successful out of Metis.

    Do you recommend that students start applying for jobs the second they graduate from Metis or should they wait a couple of months after they graduate to improve their portfolio?

    Megan: I think personal comfort level factors in, but we see a lot of people starting to get a little antsy about their search around Week 9 of the bootcamp. They’re starting to think of their list of target companies to start applying to and researching. Around that time, they may start putting out some applications and start getting some coffee meetings with hiring managers. By career day, they're starting to more actively apply. I think that's a pretty good timeline to start things off.

    Do Metis and Capital One Labs have a nice feedback loop? If you notice a new hire is lacking in one subject, are you able to give that feedback to Metis?

    Brennan: Yeah, fortunately we haven't seen any gaps to relay back to Metis. Capital One has a relationship with Metis on several different levels. We partner with them in all kinds of different ways. So there’s a nice give and take between our relationship as far as helping them help us and vice versa.

    Megan: We have a close partnership and it's been great to work with them. They've definitely given us some awesome feedback on career day and that has certainly evolved drastically over the couple of years that we've been running bootcamps. It's just awesome to get that advice because they're the ones that we're doing this for and their feedback is so important to us.  

    Brennan, will you hire from Metis in the future?

    Brennan: That's the plan!

    Megan, there’s a lot of talk about how to report outcomes and the methodology behind those statistics. Is Metis planning on publishing an audited report anytime soon?

    Megan: We're definitely thinking about that very seriously. We are actually accredited by a company called ACCET and we have to go through an audit process frequently. We have to submit verified employment data once a year. Our numbers are very much accurate and if they weren't, they would shut us down. We are under very strict guidelines on how we report that data.

    Brennan, last question. What is your advice to other employers who are thinking about hiring from a bootcamp or from Metis in particular? Have you found the secret sauce to navigating through the bootcamp world?

    Brennan: The hardest part about hiring from bootcamps is that they essentially open the floodgates and all of us potential employers are all trying to find the top talent from the bootcamp all at once. The hardest part for us is having an interview process that selects the top talent and making sure that they are the right fit for our role, but then also being fast enough and agile to work with Metis grads as well because there's so much competition for this talent. My advice is to start early and build a strong relationship with the bootcamp and then build out a process that can effectively and quickly hire the right people.  

    Is there anything that we totally skipped over that either you Brennan or Megan want to make sure our Course  Report readers know about? 

    Megan: It’s been great working with Brennan and the Capital One Labs team and we look forward to more partnerships and continuing the relationship and hopefully getting even more Metis grads onto their team in the future.  

    Thanks so much to Megan and Brennan! To learn more, check out Metis reviews on Course Report or visit the Metis website here!

    About The Author

    Liz pic

    Liz is the cofounder of Course Report, the most complete resource for students considering a coding bootcamp. She loves breakfast tacos and spending time getting to know bootcamp alumni and founders all over the world. Check out Liz & Course Report on Twitter, Quora, and YouTube

  • Learn to Code in 2016 at a Summer Coding Bootcamp

    Liz Eggleston4/14/2016

    If you're a college student, an incoming freshman, or a teacher with a summer break, you have tons of summer coding bootcamp options, as well as several code schools that continue their normal offerings in the summer months.

    Wondering what a college student or a school teacher can do with coding skills?

    Continue Reading →
  • Alumni Spotlight: Emily & Itelina of Metis

    Liz Eggleston11/9/2015

    Since launching in 2013, Metis has always partnered with leading tech companies to design their bootcamps- they teamed up with world-renowned Rails shop Thoughtbot to develop their Web Development bootcamp, and partnered with Datascope Analytics to design their current Data Science bootcamp. While we’ve chatted with instructors and curriculum designers at Metis, this is Course Report’s first time talking with Metis students! In this Alumni Q&A, Emily and Itelina tell us what they were up to before Metis, their data science bootcamp experience and their lives post-graduation.

    Liz: What were you up to before you started at Metis? Tell us about your education background, your last job, things like that.

    Emily: Before Metis, I was working for a small branding firm where I was doing qualitative research and visual strategy/brand strategy, and I really liked doing research. I have a Bachelors of Fine Arts, which isn’t something you necessarily think of with data science. I got really interested in data visualization and wanted to learn more quantitative skills. I started taking courses in the evening in statistics, and that snowballed until I found out about Metis and this data science course and I thought, “Okay, I want to learn all of those things that they’re teaching.”

    Itelina: I graduated with a degree in mathematical economics from Princeton University four years ago. In the last four years, I’ve been working as a management consultant at Price Waterhouse Coopers. I work mostly with healthcare clients such as hospitals and insurance and pharmaceutical companies.

    I did a lot of projects that involved solving problems with data, so I was often the lead on the analytics work stream. I worked on analyzing data from hospitals and insurance companies and seeing different patterns in cost of care and quality of care. That got me really interested in solving problems in data, and I also did some of that in my undergrad degree.

    I really wanted to find a program where I could the latest techniques, technology and tools, and Metis was the perfect program.

    Liz: Itelina, what clicked and made you think, “I want to be a data scientist, go to a bootcamp and get that data science job after I graduate”?

    Itelina: When I was an undergraduate doing economics research, I used statistical software to analyze data. When I was consulting, analyzing data wasn’t always the primary focus because I was part of a group working on strategy.

    But we started working with clients who had increasingly larger data sets. Very early on I realized that some of the traditional tools used in data analysis, such as Excel, were obviously not powerful enough to do the kinds of analysis we needed with larger quantities of data.

    So I started taking courses online forum Coursera. I took the data science specialization track and I learned a R and things like that. That made me realize that there are a lot of technologies and tools out there and that I wouldn’t be able to focus on learning all of those technologies while working at my job. That made me realize that I wanted to attend a bootcamp program.

    Liz: Emily, you mentioned doing a night statistics class, right?

    Emily: I did some online courses as well. Not through Coursera, but I did one that was through EdX.

    Liz: Did you ever go to meetups or workshops before you thought about plunging into a full-time bootcamp?

    Emily: I hadn't been to any meetups before. I did go to the open house that Metis had just to check them out to see if this was really something that I was interested in, but I wasn’t really going to meetups.

    I did some in person courses as well as the course that I did through EdX. I was sold on the idea after I went to the Metis open house. After that, I researched the other boot camps in New York, and I felt like Metis really stood out to me.

    Liz: Itelina, did you also look at data science bootcamps in New York?

    Itelina: Yeah, I did. Honestly, I considered graduate school programs vs. bootcamps. I figured that doing the bootcamp would be good for me because it would be a shorter program, more practical. I looked at some of the other bootcamps in New York City. There’s a bootcamp called Data Incubator; they’re geared more towards students with PhDs.

    There were a couple of other programs, but I just thought that Metis was great for what I was looking for because of its instructors, full day program and the curriculum, so that was why I chose it.

    Liz: Emily, what were the other boot camps that you looked at?

    Emily: I can’t remember the other ones that I looked at but I looked to see which ones were being offered in New York. The main reason I chose Metis is because they had their curriculum published. I think there was another bootcamp in New York that was teaching mainly R, and I was more interested in Python because I felt like it’s a language that’s  very versatile and can be used for so many different things.

    They touch on so many different languages like D3, which I wanted to  get better at. We did a little bit of SQL, MongoDB and Hadoop. I wanted the full breadth of languages.

    Liz: Tell us what the application was like? Did you have to do a coding challenge in a certain language?

    Itelina: For the application process at Metis, you do a take-home challenge. You’re required to do it in Python but you can do it at your own pace at home. There’s a set of problems for you to solve with descriptive results and answers.

    There was a video interview with one of the instructors. They do test your background but it wasn’t intense or difficult.

    Emily: I don’t know if I had the same questions as Itelina. For my take-home I don’t think you were required to use a particular language. I didn’t use Python because I didn’t know it at the time. I solved it with JavaScript because that was the language I knew best at the time.

    Itelina: I knew how to do data analysis in R but when I got the challenge from Metis, I said, “I need to learn how to use Python,” so I looked through a book and did the challenge.

    Emily: I just used the JavaScript console. I think they were impressed with that because they figured, “Oh, she’ll be able to use the terminal, no problem.”

    Liz: How many people were in your cohort?

    Itelina: I think about 20-25 people.

    Emily: I think it was 21.

    Liz: Did you find it to be a diverse class in terms of age, gender and race?

    Itelina: In terms of gender, definitely. I think half of our class were girls and half were guys. Age, definitely; we had people who recently graduated from college and people who were trying to make a mid-career change. We had people who were born outside of the U.S., so definitely a lot of diversity in backgrounds.

    Liz: I’m not super familiar with the world of data science but is it fairly male-dominated?

    Emily: I’m not sure because I feel like it’s such an emerging field. That’s one of the things that I considered when I was looking for a job after graduation. There are many people trying to hire data scientists right now. I’ve met people who ask what I do and I say I’m a data scientist and they say, “My company’s been trying to hire a data scientist for months and months and they can’t find anyone.” That could be because there are more positions to fill, but it could also be that different companies are looking for someone very specific to fulfill their needs. I don’t know yet how if I feel that it’s a male dominated field; possibly.

    Liz: Who were your instructors and was it important to you to research the instructors?

    Emily: It was important to me. I was actually a bit disappointed because when I first went to the open house Irmak and Bo were instructors at the time. I met them and thought they both seemed really nice. I was excited for them to be my instructors then they weren’t. But, the instructors for our cohort were also pretty amazing.

    Before it started, I looked at their LinkedIn profiles and Googled them to see what their backgrounds were. They were definitely very qualified.

    Liz: Who were your instructors?

    Emily: Erin Schumacher and Jon Hanke.

    Liz: What was the teaching style like?

    Itelina: The general format for the bootcamp is lectures for a couple of hours in the morning and some pair programming problems to improve our coding style. In the afternoon it’s mostly individual work on projects. The instructors are available and you can ask them any questions or ask you can ask your classmates. It’s half structured, half individual work.

    Liz: You’ve both done a traditional 4-year degree.  What did you think about the teaching style compared to a traditional university classroom?

    Itelina:  I thought the format was very innovative. It’s project based so every two to three weeks we do a project and make presentations. It’s very practical and simulates a real work environment where you’re delivering results in short time periods.

    I thought it was a very innovative learning model because you’re learning and doing. At the same time, we have good coverage of the theory behind the different models and the technology tools.

    Emily: I thought so too. Having the unstructured time in the afternoon — it was sometimes hard to stay focused, but it did replicate a real work environment in that you don’t always have someone looking over your shoulder and telling you “You need to do X, Y and Z.” You sometimes have to figure that out for yourself and manage your own time.

    Liz: Were there also teaching assistants or former students that were around to help?

    Itelina: We had one teaching assistant. She was around to help everybody with the problems sets as well as the questions so we had very good support.

    Liz: Was there a good feedback loop with Metis?

    Emily: We had one-on-one meetings with staff members including one of the co-founders. They like to do that a few weeks in to see how it’s going for you and to see what’s working for you and what isn’t, so that they can make real-time adjustments to the course. They encouraged us regularly. Occasionally they asked us to fill out anonymous surveys.

    Itelina: I definitely agree.  They were very active in getting feedback and making adjustments on the spot. If there was a particular topic that we wanted to cover more, they would schedule an ad hoc session in the afternoon during our free time. It was a very good process.

    Liz: Did everybody that you started with finish with you?

    Emily: I think we had one person who started who left after the first week. The other 21 people made it all the way through to the end.

    Liz: Did you have tests or assessments throughout or was it mostly project based?

    Itelina: We didn’t have tests; mostly project based assessments.

    Liz: What technologies did you learn at Metis?

    Emily: Python was the main language that we used. For different projects we were encouraged to use different technologies. SQL, Mongo DB, Hadoop,  D3, Hive, Spark —we covered those and you could use them or not, depending on whether they were necessary for your project. Other people with more advanced knowledge coming into the course got into Neural Nets, but that wasn’t explicitly taught.

    Liz: Emily, you mentioned your background and wanting to get into data visualization; were you able to do that at Metis?

    Emily: You could go as far into D3 as you wanted to, but there was a lot of emphasis on communication. The three main things that they talk about are programming, statistics and communication. At the end of a project when you’ve done all the complicated statistical models, you need to explain it to your client or the stakeholders in the project. A lot of the communication comes from data visualization. That was discussed and we were encouraged to explore that.

    Liz: Itelina, can you tell us about your favorite projects?

    Itelina: For one of my projects I worked on some data that was published in a Kaggle competition. Kaggle is a company that hosts a lot of predictive modeling competitions. Sometimes a company will say, “We have this data set and we’re looking for someone to develop a predictive model on this outcome.” Kaggle will host this challenge as a competition and there is money involved, so people compete for these prizes. There’s a leaderboard where people see their scores. It’s a great resource for keeping up with the latest techniques that people are using as well as data sources.

    I used Kaggle to get a set of data. That data set contained information on patients and their medical records. The topic was predicting a person’s risk for developing type 2 diabetes based on their past medical record. For that project I worked on developing a predictive model for a person's risk for diabetes based on these other factors. That was definitely an interesting project.

    Liz: I love that because it’s clearly your former career influencing the way you use data science skills to solve problems that you couldn’t have solved before. Did you work on it alone or with a group?

    Itelina: For that project I worked with a couple classmates who also picked healthcare related topics. Another classmate focused on the wellness aspect. She was analyzing diet and blood pressure, and how that correlated to their risk for certain diseases. Somebody else did something similar with heart disease. I worked with a group collaborating on similar topics but on different angles.

    Liz: Emily, what was your favorite project?

    Emily: It was probably my final project where I did some analysis of UN data. It was for a data visualization challenge that the UN was hosting on the Millennial Development Goals. It was a 15 year project in which the UN focused on specific goals.

    One goal concerned curbing the spread of different diseases. I focused on HIV data, and found some interesting things going on with the spread of new cases of HIV in Sub-Saharan Africa. I looked at which countries were able to stop the spread of HIV.

    It was hard to determine causation in the data, but I managed to come up with some interesting visualizations. I was a finalist in the competition. Also, the UN took notice of me and offered me a job!

    Liz: What are you up to now?

    Emily: I’m on a short term consulting contract with the UN right now. I’m working on a dashboard exploring some data related to climate change.

    Liz: What was that process like?

    Emily: I sent it to them because I had to make the entry into the data visualization challenge. Also, my school had a contact at the UN because they were hiring. I sent in an application and got an interview  because they saw the work that I did for my final project and thought it was good.

    Liz: I’m assuming you’re using data science skills for this project.

    Emily: At first when they were telling me about the project I was a little bit scared. I knew it was only a three-month contract and I thought, “You want me to do all that analysis in three months? You want me to solve climate change in three months?” But they have a lot of analysts who’ve been working with this data for a couple of years. I am working with a team of people who are figuring out how to present it and build the dashboard.

    Liz: What is it like transitioning from a bootcamp into a real data science position?

    Emily: At first it was difficult because I’ve worked on so many of my projects individually. I had to figure out how to setup a workflow in terms of code.

    On the other and, I really feel like I need to shake my imposter syndrome because I feel like I have  so many skills. I’m working  with people who are also very skilled, but I don’t feel like my skills are sub-par.

    Liz: Itelina, what are you up to now since you’ve graduated?

    Itelina: I’ve been doing a lot of job searching. I would say the process has been a lot better than I would have expected if I was just searching for jobs on my own because Metis provides a great network. We have great career services and great support.

    In fact, before I graduated from Metis, I actually had a job offer. I think Metis really does a great job of making the right introductions and connecting you to the right companies that are a good fit for your skills.

    I’m looking for a job where I can grow my career, so I want to take a little more time to talk to different companies and see what’s out there.

    Liz: What’s your dream position?

    Itelina: I definitely want to work for a company that has a really good vision, so a company that will make an impact on our society. I’m looking at opportunities in mid-stage startup companies, although not exclusively. I’m looking primarily for opportunities in healthcare, but I’m looking at opportunities in other industries as well. Definitely a data scientist/analyst type of position.

    Liz: What is Metis’ approach to job preparation? Did you do mock interviews, resume building?

    Emily: We did all of the above. They did a really great job. We had workshops on writing resumes and cover letters. We also did a mock interview where we were actually whiteboarding. We had the opportunity to do interviews for soft skills and one-on-one meetings with career staff.

    Sometimes it felt like it was a lot to take in while learning so many things, but it was really supportive. I still communicate with the career staff. They still respond if I want them to look over a cover or some changes I’ve made to my resume.

    Liz: Itelina, you’ve gone through a number of data science interviews with different companies. Have those been set up through Metis or through your own network?

    Itelina: Partly through Metis and partly through my personal network. Generally Metis makes an introduction or tells us about a certain opportunity. We take it from there and see if there’s a fit.

    Liz: What is a data science interview like?

    Itelina: It’s actually very different from company to company. I’ve interviewed with companies where all that’s required is a take-home challenge then going to the company and meeting people and seeing if there’s the culture fit.

    I’ve had other companies ask me to solve math problems. They would ask me to do that for an in person interview as well, and I would have to make a presentation to an entire group on a past project and defend my work.

    I’ve had interviews where people asked me questions related to SQL. I think it’s different depending on what each position is asking for.

    Liz: What has been the reception of you being a bootcamp graduate?

    Itelina: People are generally interested. At almost every interview people ask me to describe what the program is like and the things that we do and how they teach. People are definitely interested in that kind of background. They’re interested in how well it prepares you for the kinds of problems you have to solve in a real job.

    Liz: My last question that I ask everyone is was it worth the money and would you recommend it? Do you think that you could have learned everything you learned on your own?

    Emily: I would say yes, absolutely. I think it was worth it.  It was a challenge. It was really intense but I don’t think that I would’ve gotten all of this knowledge on my own, especially in this short amount of time.

    I’m continuing to reinforce that knowledge and add to it with projects that I am working on. I really don’t feel like I could’ve downloaded all of that knowledge into myself in any other way.

    Itelina: I absolutely agree with everything Emily said. I would definitely do it again in a heartbeat. It was definitely worth the money. The learning environment, having the type of classmates that you can learn from, the instructors — you definitely couldn’t have gotten it just by learning on your own.

    Again the Metis hiring network is also really great, so I would definitely recommend it to anyone. I wish the program was longer so that I could stay there and learn more!

    Liz: Anything you want to make sure people know about Metis, bootcamps or data science in general?

    Emily: There is a lot of talk about data  science because it is one of these fields where there are more open positions than there are people to fill them. I would say to do one of these courses,  a masters or a bootcamp, you really have to love working with data, love a challenge and love problem-solving.  You have to love working hard. I don’t think it’s something you can do just because you want a guaranteed position and a decent salary. You have to love this kind of work.

    Itelina: I would say anybody who’s looking to get their foot in the door in data science or looking for a change should check out bootcamps. This type of program is very innovative. I benefitted greatly from the program so I think it’s great for somebody who is trying to start a new career.

     

    Would you like to learn more about the Metis data science program? Check out their Course Report page.

  • Watch: Metis Data Science Open House

    Liz Eggleston8/24/2015

    Did you miss the last Metis Data Science Open House? Catch the replay, along with some of our favorite moments from the panel Q&A, here!

     

  • Learn Data Science at These 22 Coding Bootcamps

    Harry Hantel12/9/2016

    You don’t have to be a data scientist to read into these statistics: A McKinsey Global Institute report estimates that by 2018 the US could be facing a shortage of more than 140,000 data scientists. The field of data science is growing, and with it so does the demand for qualified data scientists. Sounds like a good time to pursue data science, right? No kidding! Data scientists make an average national salary of $118,000. If you’re looking to break into data science, or just trying to refresh and hone the skills you already have, Course Report has you covered. Check out this comprehensive list of the best data science bootcamps and programs in the U.S. and Europe for technologies like Hadoop, R, and Python.

    (updated August 2016)

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  • January Coding Bootcamp News Roundup

    Liz Eggleston2/2/2015

    Welcome to the January News Roundup, your monthly news digest full of the most interesting articles and announcements in the bootcamp space. Want your bootcamp's news to be included in the next News Roundup? Submit announcements of new courses, scholarships, or open jobs at your school!

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  • August Bootcamp News Roundup

    Liz Eggleston8/29/2014

    Welcome to the August News Roundup, your monthly news digest full of the most interesting articles and announcements in the bootcamp space. Want your bootcamp's news to be included in the next News Roundup? Submit announcements of new courses, scholarships, or open jobs at your school!

    Continue Reading →
  • Want to be a UX Designer? Find out How at our Webinar with Metis!

    Liz Eggleston8/26/2014

    What does it take to become a UX Designer?  

    Continue Reading →
  • Exclusive Course Report Bootcamp Scholarships

    Liz Eggleston8/12/2014

    Looking for coding bootcamp exclusive scholarships, discounts and promo codes? Course Report has exclusive discounts to the top programming bootcamps!

    Questions? Email scholarships@coursereport.com

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  • Webinar Series: Data Science for n00bs with Metis

    Liz Eggleston7/23/2014

    We learned so much in our webinar with Laurie & Irmak of Datascope Analytics and Metis, including:

    As promised, you can watch and share the whole webinar on demand by following this link

    Continue Reading →
  • Course Report & Metis Present: Data Science for nOObs

    Liz Eggleston7/16/2014

    Join Liz of Course Report and Laurie & Irmak of Metis on Tuesday, July 22 for a free webinar. You'll also have a chance to ask any questions you have about the Data Science course at Metis!

    Date: Tuesday, July 22
    Time: 6:30pm EST

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  • Q&A with Alex Baldwin, Metis

    Liz Eggleston7/11/2014

    Since launching their Ruby on Rails bootcamp in Boston, Metis has expanded to new cities and added courses in new fields. Their upcoming Product Design course in New York aims to prepare students for a job in the growing field of UX and Product Design. We talk with Alex Baldwin, co-creator of Metis Product Design Curriculum, about the differences between UX Design and Product Design, why the bootcamp model is great for this subject, and the types of students who will excel at Metis. 

     

    What is a product designer and how is it different from a UX Designer?

    Never have I met another industry that cares more about what they call themselves. Are you a UI or a UX? Usually, you’re doing a lot of everything. So I see product designers as people who are not afraid to just jump into something. Some days, I’ll be doing database stuff. I might not be the one to write the queries but I’m the one that’s going to bring attention to that problem and make sure that resources are allocated to fixing it. There’s definitely been a push in the last couple of years; designers are becoming more of marketers and using growth hacks. It’s definitely bringing design and marketing together.

    It’s really fascinating to see something like the Dropbox case study of how their referral program allowed them to be enormous. Our clients at thoughtbot request types of features like that, and being able to attach a growth loop onto something that people really desire is a fantastic way to make sure that you stay interested in your product and people are using it and recommending it once it’s good.

    When it comes to products, you have to look at flows, user interfaces, functionality, measurement and research and there are all these other aspects to consider.

     

    Why do you think that the boot camp model will work for product design?

    I think our boot camp model is wonderful because the last month is all about passion projects. We’ve identified that there’s definitely this need when people join bootcamps, specifically those who want to do product design, that they have something in their portfolio that they can show. They care most about getting feedback on their work and getting those as close to done as possible.

    Doing this learning in the classroom setting is way easier; students sit down, map out where they need to go, and the instructors can go through and evaluate how to get them there most efficiently.

    I think that in-person feedback is really important. So, having the dedicated 3-month period of time where this is the only thing they’re focused on is what will really be successful.

     

    What sort of student are you expecting? Are you expecting somebody who has to have a passion project in mind? Do they need experience in coding or web design?

    First and foremost, they should have some sort of background in graphic design. I think if you already call yourself a designer and you have a portfolio, it doesn’t really matter what’s in that portfolio. It could be illustration, marketing pages, it could be anything about design, whether it’s UX or not.

    There are no “last job” requirements. What we really care about and what we’ll be interviewing for is people that have been satisfied with their work but they crave more. They want more of a connection with the people that they’re making these projects for and they want to make sure that the projects work.

     

    Will you all have a technical challenge or will it be more of a culture interview? Have you thought about what the interview will look like?

    Yeah; the course does include basic HTML and CSS. The reasoning for that is it’s hard to get things to completion when you don’t control that stack. If you’re waiting on someone else to finish part of your work, you’ll just have to sit there and wait. So I think we’re going to spend a lot of time building those skills.

    There’s not necessarily a technical challenge, although we are going to have people do a graphical challenge. We want somebody to produce something… Even if it’s really bad, that’s fine; we just want to see where people’s graphical skills are. But I can’t tell you exactly what our challenge is!

     

    Will students have pre-work on HTML/CSS?

    Yep, we have two weeks’ worth of pre-work, and all our pre-work consists of custom tutorials that we’ve written ourselves. We just want to make sure that they’re versed in the software flow and are able to scale up those skills pretty easily from the beginning.

     

    What has it been like to design a curriculum for a fairly new subject matter?

    This has actually been super fun because there’s nothing else to go off of. We took the core cases of interaction design and made those cases stick together really well. We looked at the key abilities and how we could increase people’s yield in those activities.

    We’re doing these weekly exercises and they’re really meant to push you into getting to being in a production role and thinking about the whole loop of products.

    For example, one of the exercises that we have students go through is we’ve produced all the Javascript to give students a weather app. However, there’s no style to it. So we’re giving them a little toolbox to build their own weather app that will actually work. What’s great about that is they don’t have to worry about any of the coding concerns; it’s already been done. What they have to focus on is okay, I have 100 options and they can be in a million different states. It’s those kinds of things that will be very valuable for our students, getting that confidence up.

     

    It’ll be really cool to see how many different variations of the same assignment turn up.

    We are all really excited.

     

    So will students do those 7 projects over the first 2 months and then they work on their passion project?

    The structure is 7 weeks of lectures and exercises- there are no abstract lectures. One day we’ll talk about data analytics. At the end of that lecture, they’ll translate that concept into an app.

    The way the lectures work is that the co-instructors will trade off. You’ll have one person taking one day and the other will be walking around and helping students while the lectures are going on and answering questions.

    Then we also have a team project that we’re going to put together. We want to group some of the designers in teams of 4 or 5 and have them work on a behavior builder. What I like about the behavior builder is that it’s really easy to see how people interact with this in real life and measure it’s success, as opposed to hey, let’s release a music app and maybe it’ll do something or it won’t. It’s very clear whether this action is happening or not.

     

    In talking with students, I hear a lot of feedback saying that they want to work as a group and do team projects so they’re prepared for that in the real world.

    It‘ll be super fascinating because these students aren’t all going to be from similar backgrounds. They’ll realize that one person is clearly better than them at graphic design, so they should be the visual designer of this project. One person has done research before, so they should fulfill that role. And just seeing how those roles specialize and how they work together will be neat.

     

    Will the Metis Product Design bootcamp have “personal investment days” like the Ruby on Rails bootcamp does?

    I think with the Rails bootcamp, they have Knowledge Bomb Fridays with friends of thoughtbot and employees. They also have Fireside Chat Tuesdays that are employer-focused, where they have people speak from hiring companies and they’d bring in a Head of Engineering to talk about something that’s specific to that company.

    We plan on doing similar things with guest speakers from the community.

     

    Have you had to think about working the outcomes into the curriculum like doing job placement, interview practice and stuff like that or are you letting Metis handle the brunt of that?

    Metis is handling most of that. They have a really awesome talent placement manager that we’ll be working with and they’ve done great work. I think the hiring percentage out of the first bootcamp has been strong, and it’s only been a few weeks.

    For me, I’m thinking how can we get someone in 3 months to be hirable at thoughtbot? I know that there’s other companies that hire similarly to us but usually their bar is not quite as high. They might want more visual design but generally, the bar at thoughtbot all around is high. If I needed someone to be hirable here  which we’ve done in the past through our friendship program – I think we’ll be doing a really awesome job and unleashing some seriously terrible monsters in the design field on the city of New York.

     

    Does that mean that thoughtbot will be hiring from this program?

    We are a hiring partner and we do have an office in New York. If anyone’s a fit, we’ll see. I still don’t have any students placed so as soon as that happens, we’ll have a better gauge.

     

    Want to learn more about Metis? Check out their School Page on Course Report or their website here!

  • Acquisition of Dev Bootcamp by Kaplan Lends Legitimacy to Coding Bootcamps

    Liz Eggleston7/3/2014

    The recent sale of Dev Bootcamp, the first coding bootcamp of it’s kind in a rapidly expanding space, to Kaplan marks a significant landmark in the coding school world. Kaplan, the test prep powerhouse, lends legitimacy to the bootcamp model as a viable form of education. 

    Other coding schools’ recent fundraising only confirms that the model will continue to grow and perhaps even expand to subject matter beyond coding, like sales, user experience design, and digital marketing. General Assembly raised an impressive Series C round of $35MM in March 2014, and Flatiron School, based in New York, recently pulled in $5.5MM in new funding in April.

    Continue Reading →
  • Instructor Spotlight: Irmak Sirer & Laurie Skelly, Metis

    Liz Eggleston7/3/2014

    Irmak Sirer and Laurie Skelly work as data scientists (Irmak is also a partner) at Datascope Analytics, a data-driven consulting and design firm in Chicago. When they met the folks at Metis, who have already proven their propensity for great partnerships with their thoughtbot collaboration, it was clear that a Data Science program was in the cards. Now, Irmak and Laurie are designing the curriculum for the upcoming Metis Data Science course in New York, which is one of the only programs that teaches relative beginners (students don't need PhDs to apply) to be employable data scientists. 

     

    Tell us about Datascope Analytics and how you got involved with Metis.

    Laurie: Datascope is a data science consulting firm. We work with a wide range of clients from regional not-for-profits all the way to Fortune 500 and national corporations. People usually ask what kind of data we work with. We’re the kind of firm that uses any data source to help people solve their problems. We’re a very broad, general-purpose firm.

    Most of us have some kind of academic background and we have continued to come back to the idea of doing some sort of teaching or training. We’re small in size, so finding the bandwidth to run classes and develop new material was really going to be difficult and probably a really long-term idea.

    We ran into Jason and Bernardo from Metis at the Strata conference, which is a data science conference; I used to work for Kaplan part-time in graduate school. They were looking for data scientists, people with domain knowledge to help develop the curriculum, and we were really excited to find a way to do some teaching and training without having to take care of all the logistic aspects ourselves.

    Irmak: Laurie first met Jason and Bernardo and the entire Datascope team was really excited about it and I also jumped in on the opportunity. Now Laurie and I are designing the course and we will be the first instructors.

     

    We have seen the boot camp model expand to data science and product design, etc. Why do you think that the bootcamp model can work for data science?

    Laurie: I think that there are a few reasons why it’s a really great option for a lot of people. Usually, people who would consider the bootcamp are comparing it against a master’s program or self-teaching.

    A couple years ago, there were really no options for Data Science masters degrees, and this year I think there are 40 new ones that are starting. But those will take 1-2 years and they’re usually upwards of $50,000.

    On the other side, there’s self-teaching. There’s so much available on the internet but it’s really difficult, even for disciplined people to stick with it for the amount of time it takes to learn and be employable.

    The Metis program is a very happy medium of a 3-month time window where you can take a leave of absence or say, “I’m gonna make the jump and figure this out.” I’m sure that some people will also be sent by their employers.

    Irmak: I think with something like a master's program, you absorb a lot of theoretical knowledge. Then you start working as a data scientist and it also takes a long time to get familiar with the real life practical applications of that theoretical knowledge and relearning all you were taught. We want to throw you right into the thick of real problem cases and supply the information you need to get through. This gives you valuable experience.

     

    Do you expect that students will graduate ready to be junior data scientists at a company?

    Laurie: Absolutely. There are a ton of people out there who have all or nearly all these skills; they’re just filling in a couple of cracks.

    I had the experience of knowing a lot of what I needed to know and not understanding what it was called. We come from different academic traditions and a lot of times, when discussing machine learning and regression techniques and modelling, people will be talking about the same space and really have a lot more knowledge in common than they realize. They just need to have that reconciliation. There are also people in the academic world who need to adjust to how different things are in the world of business.

    I don’t want to hit this point too hard because I think it’s part of the secret sauce of why data science is so valuable right now, but we don’t know everything. Being on a job, we don’t know everything all the time. Data scientists, even the best ones in practice do a lot of Googling. It’s about having that skill and that confidence to apply the solution to the problem even if it’s a problem that you’ve never encountered before.

    That’s the sort of thing that we can definitely teach in 12 weeks, especially the way that we’ve designed it- the process of fearlessly tackling very technically challenging things, knowing where to look, and knowing how these pieces fit together.

     

    This will be your inaugural cohort- do you expect people to have experience with coding or analytics or statistics, or could somebody theoretically be a beginner?

    Irmak: What we expect is some exposure and experience in statistics and programming but not more than that.  We’re actually expecting different people with very different skill sets.

    I think that there will be people that have a lot of programming experience and have seen a little bit of statistics, maybe in school, but they don’t really do focus on analysis. There could be a student that worked in the sciences, as another example, where they used statistics a little bit and they coded a little bit but they haven’t done anything like data science directly.We would also consider people that do more traditional types of statistics. They’ve done some coding but they’re not that experienced in programming.

    The idea here is, not everybody is going to gain the exact same skills going through the boot camp. Wherever you are weaker in the big picture of Data Science, you will learn and strengthen that part.

    And we think that curiosity and creativity, those kinds of personal attributes are also very important; because as Laurie said, a lot of data science is having confidence in your ability to learn something you don’t know, which means that you should be curious and creative with the tools themselves.

     

    Laurie: If your question is- are we going to be able to take beginners and make them data scientists in 12 weeks? I would say the answer is “not yet.” Because this is our first class, we don’t know how inexperienced someone could be and still bring them up to speed.

    Fortunately, I think that since we’re so early in the data science bootcamp game, we’re going to have a pretty competitive applicant pool. Maybe in the future we’ll be able to assess people and say, “You’re really great on your programming but you’re missing some skills in experimental design; go bone up on that and come back and we’ll be ready to take you.”

    Our biggest goal is to be telling the truth when we say in 12 weeks you’ll be able to get a job and not drown in that job. We don’t have any interest in putting people in situations they’re not ready for.

     

    Have you thought about what the interview is going to look like?

    Laurie: Yes! Based on people’s expectations, it’s way more of a culture interview – but there is a technical challenge as well. People think about data scientists and they think about the toolbox and how intimidating it is. But like we said, we really need curious, clever people more than someone with a lot of technical experience. I’d rather have someone who I can tell is going to pick something up quickly or is asking clever follow up questions and is just really listening closely. We’ll probably learn more as we go but for this round, we’re going to be setting ourselves up for a better experience if we find a lot of really good personality matches. These will be our pioneers.

     

    What is the technology stack that students learn in a data science boot camp?

    Irmak: We think of it not only as a technology stack; we think of it as data science dimensions- the domains and the data, algorithms, tools and the visualization/communication part of a project. Basically, we want the graduates to be equipped in all of these dimensions.

    In terms of data, we’ll be teaching SQL and non-SQL databases. We’ll be teaching about APIs and web scraping, where you could get data from different sources and how to clean that data.

    In terms of algorithms, we will go over machine-learning algorithms; we will go over regressions, supervised learning and non-supervised learning. The tools you will learn is  how to apply those algorithms.We will teach the course using Python as a programming language. There are a whole bunch of packages in Python where you can apply these types of algorithms.

    In terms of visualization and communication, we think that the communication skills and the presentation and the relationship with the client is very important. We will teach these skills alongside visualization tools such as d3.

     

    Laurie: In every project, you have to decide what kind of data you’re using, how do you get it, how do you store it, what are you doing with is as far as algorithms, what are you using to implement those algorithms and when you are done, how are you going to show those results to somebody else?

    So for each area, or dimension, of data science, we will provide broad exposure to what’s out there so the students have a good sense of the ‘lay of the land’. In each one we’ll also be working from a kind of ‘home base’ - for a programming language, it will be python; for visualization, it will be d3; for databases, we’ll use mongoDB and MySQL. For each of these they’ll have some repetitive use and training, so they will build up a toolkit that they are familiar with, but also understand what alternatives are out there for each piece and be ready to make some changes in the lineup if a job or project calls for it.

    Of our 12 weeks, we use one of them to spend a lot of focus on d3 because everybody wants to be able to make cool visualizations in d3 and we think it’s worth it.

     

    How will your project-based curriculum look?

    Students will be working on 4 or 5 projects and for each one, there’ll be some kind of output. There are many ways where they might be doing similar algorithms but different people will be picking different data sets so they’ll have different outcomes and it’ll be really interesting for people to express themselves as they’re going.

    Irmak: I think it’s a great way for them to show how they can tackle a lot of problems for potential employers, instead of just one big project, you get to show completely separate, unique problems that have nothing to do with each other that show all your skills.

     

    Has it been a challenge to create a bootcamp curriculum for data science?

    Laurie: Metis really picked the right data scientists in partnering with Datascope; we’re obsessed with design. When we were at Strata, we were doing a workshop called “Design Thinking for Dummies (Data Scientists).” So this is the kind of stuff we love to chew on anyway.

     

    What will a typical day look like at Metis?

    Irmak: First of all, generally, you will be working on a project. The first project will be just a week, another one 3 weeks long, and so on.

    You will have questions about how to make progress with your current project, you need to know about the tools, algorithms, approaches. During the day we will give some lectures where you are getting some of that knowledge and then most of the day, you are actually working on that project, applying that knowledge and also, creating new questions.

     

    Will students have pre-work once they get admitted and how many hours are you expecting that to take?

    Laurie: They have online pre-work; we have aimed for a maximum of 30 hours so that people could do it in about 2 hours per day at home, basically 2 weeks.

    Pre-work focuses on using the command line, Python and some statistics background. They’ll work through some examples from some books that we love and there’ll be some example problems from those books. If they can submit the answer correctly, they’ll pass pre-work.

     

    Are you going to have the “personal investment days” that the Ruby on Rails boot camp has at Metis, where they give people free Fridays?

    Laurie: Yeah; we’re going to have guest speakers come in; we’re going to have more like culture days. There are going to be some cool conferences going on while we’re in New York. That and the fact that we’re enforcing that the day ends at 6 and that people continue being reasonable whole people and will take breaks. It will be important to try to keep people from burning out too hard.

     

    Will Datascope Analytics be hiring from the Metis graduate pool?

    Laurie: We will be a hiring partner, absolutely. We’re really excited to have first look at the fresh recruits. As a goal, it’s absolutely at the forefront of my mind to create a course that can help students get awesome jobs because that’s why people want to be data scientists. In addition to Datascope, we are sharing information about the type of companies Metis should target to become hiring partners.


     

    I’m sure you all have amazing insight into who they should be reaching out to.

    Irmak: Metis is in charge of a lot of the organization but we’re in contact with companies that we worked with, companies that we know and have relationships with and we know are in need of data scientists.

    Laurie: I don’t know how many data scientists you know but I’ll just get emails from people at random companies asking if I have recommendations. And it actually feels like a big relief to say, “Let me put you in contact with our hiring person at Metis.”

     

    Want to learn more about Metis? Check out their School Page on Course Report or their website here

  • Course Report & LaunchLM: New York Coding School Alumni Panel

    Liz Eggleston6/10/2014

    If you're thinking about applying to a coding bootcamp in New York, then you must attend this paneled discussion with top coding schools! Join Course Report and Launch LM in the Hive at 55 downtown space for an evening with alumni from 8 bootcamps. 

    RSVP here to claim your spot- space is limited! 

    Continue Reading →
  • Student Spotlight: Derek Kaknes, Metis

    Liz Eggleston5/27/2014

    Derek Kaknes was sold on Metis because of their association with thoughtbot. A recent graduate, Derek tells us about what drove him to learn to code, the teaching styles that worked best, and how Metis taught him to write code he's proud of. 

     

    What were you up to before deciding to go to Metis?

    Before Metis I was working with a small team at a company that I helped co-found.  At the company, we had gone through several false starts trying to hire the right software engineer, so I viewed Metis as an opportunity to gain the skills necessary to fill that gap.

     

    Did you apply to other bootcamps? Why did you ultimately decide on Metis?

    I looked at several other bootcamps, including Launch Academy, Flatiron School and DevBootcamp, but ultimately decided on Metis because of their connection with thoughbot.  The opportunity to learn thoughtbot best practices from thoughtbot developers themselves was a huge selling point.

     

    Which instructors/mentors were especially helpful to you? Did you feel like the teaching methods worked with your learning style?

    Both of our primary instructors, “Goose” (Matt) and “Steiner” (Josh), were fantastic; but we also had the benefit of other thoughtbot employees dropping in to help whenever they were around.  The ability to ask questions and get answers from developers who are in the industry using best practices was enormously valuable.

     

    Can you talk about a time when you got stuck in the class and how you pushed through?

    We were taught repeatedly that we should never be “stuck” on a problem in isolation for more than 30 minutes.  Whenever I was stuck for more than a few minutes, I would first ask a classmate for help and, if we couldn’t resolve the issue, elevate it to one of the instructors.  As a result, I don’t have a great example of being “stuck” in class, but if I had to choose a frustrating experience it would be debugging AJAX requests made to an external API.

     

    Tell us about your final project- what technologies did you use, how long did it take, what does it do?

    My final project was called “BTC Shop” and is an ecommerce shopping application designed specifically for bitcoin payment.  The main technologies I used were the Coinbase payment API, which is a pre-built “plug-and-play” option allowing applications to receive btc payments, and the Blockchain API, which is a free third-party API that allows applications to send, receive and track btc payments.  Using the Blockchain API, I was able to recreate the functionality of the Coinbase product in my own implementation, including producing a QR Code for user’s to scan and send payments.  The whole development cycle took three weeks, including roughly one week for initial research and design.

     

    Would you have been able to learn to code and get a job without Metis?

    No way.  I had tried previously to teach myself, but consistently got caught on some early hurdles and never made much progress.  Metis provides an awesome platform to accelerate your initial learning curve and, through the association with thoughtbot, enforces current industry best-practices to every line of code that you write.  Could I have learned to write code that worked without Metis?  Maybe, but not code that I would be proudly confident to show a potential employer.

     

    Want to learn more about Metis? Check out their School Page on Course Report or their website

  • Student Spotlight: Adam Patterson, Metis

    Liz Eggleston5/22/2014

    Adam Patterson knew he wanted to make a career change, and turned to Metis, a 12-week Ruby on Rails bootcamp in Boston (and New York), to learn the skills to make the switch. In this Student Spotlight, Adam shares his experience, including what made the instructors effective, how he got support from classmates, and the value of an in-person bootcamp. 

     

    What were you up to before deciding to go to Metis?

    Before joining Metis I was working in a business development role at an education company. I had a very small amount of programming experience, but enough to know that this was a career change I needed to make. After three years of frustration over not being able to change or improve systems I relied heavily upon, I decided to gain the technical skills to be a true problem solver in today's world.

     

    Did you apply to other bootcamps? Why did you ultimately decide on Metis?

    I did not apply to any other bootcamps. I did look into other programs, however already being located in the Boston area limited my reasonable choices. I primarily chose Metis because it was being taught by thoughtbot developers and it was exciting to be part of something new.

     

    Which instructors/mentors were especially helpful to you? Did you feel like the teaching methods worked with your learning style?

    Both of my instructors were especially helpful. One of the best parts of the experience was that my classmates and I each built a strong relationship with each instructor, allowing us to learn from both of them in a special way. We even had a third instructor join the group toward the end, and he quickly became just as important to us as our main instructors. It's a real testament to the teaching style and approachability of the people at thoughtbot. The instructors did a terrific job of adjusting to our content delivery suggestions on the fly. With over a dozen students at different levels and with unique learning styles, it is difficult to make it work for everyone; I felt as though they did as good of a job as could be expected. 

     

    Can you talk about a time when you got stuck in the class and how you pushed through?

    Just one time? During the beginning stage of Fail Forward (my final project), I was having a lot of trouble implementing the LinkedIn API and Oauth gem. This was especially problematic because I wanted to get that sorted out before moving on to the rest of my app. I was fortunate enough to have a classmate who used a similar method for his user authentication, and he helped guide me through the implementation. The environment created by the instructors was one of collaboration, and this was an outstanding example of how supportive the group was of one another. 

     

    Tell us about your final project- what technologies did you use, how long did it take, what does it do?

    My final project was called Fail Forward. It is a place for professionals to share stories of failure, receive feedback from peers in their industry, accumulate knowledge, and become better through failing. I was inspired by a social experiment from high school and the programming mantra of "fail fast, fail often". I used the LinkedIn API and Oauth gem for user login in order to link it to each user's LinkedIn profile. Users could then post their failures within their industry, update their posts with new knowledge, comment on other user's fails, and follow specific fails to find out when they are updated. It is mainly Ruby on Rails, with some JQuery, CSS, and HTML. It took me about 2.5 weeks to get the app where it is today.  

     

    Would you have been able to learn to code and get a job without Metis?

    I think almost anyone could learn to code without a bootcamp. The value of a bootcamp is, of course, learning the essential skills to be job-ready in a much shorter period of time, learning best practices (very important in Ruby), and receiving immediate hiring support. Again, learning from thoughtbot devs was one of the best parts about Metis; I may not have had much prior coding experience, but I have learned only good coding habits and did not have any bad ones to break. 

     

    Want to learn more about Metis? Check out their School Page on Course Report or their website

  • Interview with Jason & Josh of Metis

    Liz Eggleston2/12/2014

    What do you do once you've established yourself as a top-notch web consultancy in Rails apps? If you're thoughtbot, you collaborate with Kaplan to create Metis, a 12-week Ruby on Rails course, to fuel the next generation of developers. 

    We caught up with Josh Steiner (of thoughtbot) and Jason Moss (of Kaplan) to find out more about the first Metis cohort, how the program helps graduates find jobs in tech, and what students can expect to learn throughout the course. 

     

    What is your background and how did you end up in the Code Bootcamp space?

    Josh: I’ve been working at thoughtbot for over a year now, and before that I was a part of thoughtbot’s apprenticeship program. Prior to my apprenticeship, I was a student at Rensselaer Polytechnic Institute, and while there, I made Rails applications for Major League Gaming.

     

    How did you get started with Metis?

    Josh: Matt (the other instructor) and I are both involved in Learn, which is thoughtbot’s educational venture (we have workshops, forums, and mentoring), and we saw a bootcamp as an evolution of that idea.  thoughtbot is known around the world as being an expert in Rails, so this seemed like a great next step. We pitched it to our CEO, Chad, and he actually told us that Kaplan had approached thoughtbot with the same idea. So thoughtbot and Kaplan came together at the same time, wanting to do the same thing. Matt and I were put on the project to design the curriculum and teach these first classes.

     

    Tell us about Metis and the relationship that thoughtbot has with Kaplan.  

    Josh: It’s been a wonderful partnership- very collaborative. In some aspects, such as the curriculum, thoughtbot takes the lead, and Kaplan provides expertise (e.g., on learning science and assessment). In other aspects, such as hiring/placement, Kaplan has taken the lead and thoughtbot is assisting by leveraging our client network to identify hiring partners. They’re a great group to work with, and you can tell they are very passionate about what they are doing.

     

    When is your first cohort?

    Our first class is Feb 24, and you can apply for the June cohorts in Boston and New York on our website.  There will be more to come after that, but so far, those are unannounced.

     

    How many people are you seeing apply for the first cohort?

    Josh: We’re at a number we’re happy with for the first class, especially with only two months to get the word out and launching over the holidays. We wanted a slightly smaller class for the first time.

    We have seen a ton of interest, however. People see thoughtbot on the ticket and the class appeals to them because of that. We would like to get people to the level of an apprenticeship at thoughtbot, but unfortunately we can’t hire everyone. Hopefully, a few of them will end up working at thoughtbot.

     

    What are you looking for in potential students?  Do they need programming experience?

    Josh: People need to be comfortable with the computer, but they don’t need to have done any programming in the past. We’re mainly looking for three traits: drive, grit, and communication. We think that these are the most important qualities of a good programmer, and are particularly important for somebody who is going to learn to program in such a short amount of time.

     

    What can a potential student expect to see in the admissions process?

    Josh: We have an application on our website, and then filter students for an interview to better understand their motivation, goals, and familiarity with Metis. We also ask some technical questions to understand how they approach and solve problems.

     

    Describe the partnership Metis has with Upstart.

    Jason: We have partnered with Upstart, a firm that provides individuals with access to funding for career advancement in exchange for a percentage of their future earnings.  As part of the agreement, participants in Metis will have the option of guaranteed tuition financing (up to $15,000) through Upstart in exchange for a small and pre-determined fraction of their future earned income. For more information, visit: www.upstart.com/metis

     

    Can you give us a quick run-down of the curriculum?  What’s your teaching style?

    Josh: The curriculum is broken into three parts- the first 7 weeks will be teaching the fundamentals of programming.  We’ve designed an application, and through the application, we’ll exercise all of the things that we think good web developers need to know.  Each day, Matt and I will present a problem to the class (eg. “how can we get users to log into our site”) and then teach them what they need to know to solve the problem. Then we’ll all program it together. By doing it this way, they get to see how experts approach problems. By hearing why we make decisions, instead of us just telling them “this is the way to do it”, they will be able to apply that knowledge in other scenarios, as well.

    The next two weeks will be a team programming project- we’ll emulate a client project, and they will see how we work at thoughtbot every day. Matt and I will come to them with a few ideas, the team will pick and plan out the project, and program that collaboratively for the next two weeks.

    The last three weeks will be their capstone project. This is a solo project, and it’s something they will be passionate about, because they picked the project themselves. Up to that point, they’ll get small homework assignments (wireframes, database diagrams etc), so that on Day 1 of their capstone project, they can just sit down and start programming.

    Also, during the first 7 weeks, we’ll have an “Investment Day,” which is modeled after something we have at thoughtbot.  On Fridays, students can work on anything they want. Some students may want to reinforce subjects that they have struggled with. Others may take the opportunity to work on open source, pair program, or build a small app on their own.

     

    How does Metis help your graduates find jobs in tech once they've completed the program?

    Jason: Metis has a full-time Employee Placement Specialist on our team, whose primary responsibility is to help our graduates find jobs. This person is a resource, who can help with everything from resume-writing and interviewing to career coaching and company targeting. Everyone at Metis will also participate in a Hiring Day at the end of the 12 weeks. The Hiring Day is an opportunity to meet directly with employers who are interesting in hiring entry-level Ruby on Rails developers, and specifically, Metis graduates. Also, once they've graduated and while they're looking for jobs, we provide them with 3 months of online professional development through thoughtbot's Learn Prime so that they can keep sharpening their skills.

     

    Can you explain the relationships that Metis has with partner companies?  Who are some of your partner companies?

    Jason: Some of our Hiring Partners include: CoachUp, Constant Contact, Iora Health, LevelUp, WegoWise, 3PlayMedia, Wistia, and of course, thoughtbot and Kaplan.  We'll be adding many others in the near future.  Hiring Partners are not required to hire our graduates, but they do agree to attend and interview graduates from at least one Hiring Day and then they give us feedback on the graduates.  We give the hiring partners our students’ resumes and code samples, and we work to understand their needs and optimize our curriculum accordingly.  

     

    Do you get a recruiting fee for placing a student with a partner company?  Does the student get a tuition refund?

    Jason: Yes, we get a 20% placement fee, which is paid within 30 days of the student’s start date.  A student who is placed and stays at a Hiring Partner gets a $2000 tuition refund.

     

    Tell us about the Boston tech scene- why does Boston need a bootcamp right now?

    Josh: The main reason this is our first location is because thoughtbot is headquartered in Boston, and we’re close to New York, where Kaplan is located. In addition to that, Boston has a great tech scene. We have an awesome Ruby group in Boston and we have two Ruby events each month, which are great opportunities for students to network.  There are usually announcements with multiple companies hiring at both of these events.

     

    To learn more about Metis, check out their course page or visit their website!

  • From the Kapor Center Blog: Coding Bootcamp Scholarships for Underrepresented Minorities

    Liz Eggleston2/4/2014

    Course Report is featured on the Kapor Center blog!

    Coding bootcamps are producing graduates that enter the workforce almost immediately, so their approach to recruiting and retaining students from underrepresented backgrounds may quickly start to define, and potentially diversify, the landscape of the tech industry.  One way to reach out to potential students is through scholarship programs. Check out the full article on the Kapor Center blog to see a full list of coding boot camps currently offering scholarships specifically to underrepresented minorities. 

    Continue Reading →

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