Our latest on Level

• Learning Data Analytics via Experiential Learning at Level

  Liz Eggleston4/8/2019

  Level

  

  If you’re going to take a Data Analytics bootcamp at Level, you should understand Experiential Learning to get the most out of it. Partnered with Northeastern University, Level uses experiential learning theories to teach on campus and online so that graduates are prepared for any challenges their jobs throw at them. We spoke with Rebecca Rufo-Tepper, Director of Learning at Level, who explains the tenets of Experiential Learning and how students should expect to see those applied to a Level data analytics bootcamp.

  Rebecca, what’s your background and how did you get involved in the bootcamp world?

  I have been in education for over two decades – as a K-12 teacher, in higher education, and leading a non-profit focused on learning. I support instructors and educators and leverage best practices to make learning relevant and meaningful for students and ensure we provide them with experiences that allow them to excel, leverage their passions and interests, and be challenged.

  With such a long career in education, how did you feel about the bootcamp trend when it started to gain steam?

  I noticed that universities struggle with innovating; students’ undergrad work should prepare them to be career-ready. When bootcamps originally started, I was very interested in how they might provide alternative pathways for people who may not be able to afford a degree program as well as people who already have a degree and are looking to accelerate or transition in their career. I was interested about the potential, but was wary of a skills-based curriculum that is focused on drills and isn’t thinking about engaging the learner in ways they can succeed.

  There are so many bootcamps today - why did you want to work specifically with Level?

  Level is affiliated with Northeastern University which holds Experiential Learning as core to the university, so it’s also core to Level and has made it very special in the bootcamp space. Being a part of Northeastern means we can leverage the university’s best practices and resources and students can work with Northeastern’s career and professional services to develop their resume and job hunting skills. Because students have access to university staff, facilities, and experts, it feels like a holistic experience versus a bootcamp that does one thing.

  Level cares about the learner, not just about churning out people with a specific set of skills. It’s about helping students grow, understand themselves professionally, and discover how they can take what they’ve learned to be successful. Our programs are flexible to allow students to make the programs work for their schedules, which I think is also unique.

  Many students are used to the traditional classroom and being fed information. So what advice do you give students who are new to “hands-on” learning? How can they get the most out of Level from Day One?

  The way that traditional education has designed our learning environments is the opposite of how people learn. People learn best when they’re doing, trying, testing, and moving back and forth between theory and practice. It might feel strange at first but once you start, it’s a very easy shift to make because it’s what humans are designed to do. Think about something you’ve learned to do – how did you learn how to play basketball? If you were just reading about basketball, you’d never know if you were good at it.

  For Level students, hands-on, experiential learning builds over time – there are low stakes at the beginning and making mistakes is part of the process. Just remind yourself that this is how people learn and step into that space and become comfortable with it. It’s good to recognize those moments of tension when you’re pushed out of your comfort zone – that’s when you’re learning. And be sure to communicate if you’re struggling.

  What exactly is Experiential Learning? ​

  In Experiential Learning:

  • Students are learning by doing with lots of hands-on work and connecting theory to practice.
  • There’s also a large component of reflection and self assessment. You’re not waiting for an instructor to deliver a grade to know how you’re doing, you can check your understanding and know where you’re struggling and excelling, and recognizing your strengths and weaknesses.

  Experiential Learning works especially well in a bootcamp environment. To really understand something deeply is when you “transfer” - you learn a skill in the classroom and then you execute it in a different setting so it stays with you and becomes part of your learning trajectory.

  How will students see Experiential Learning when they’re at Level?

  At Level, we have a few projects in our Data Analytics bootcamp that use Experiential Learning.

  • xCases - These are case studies we’ve created with industry professionals that allow students to consider a problem from the perspective of an executive, focusing on what students might do in a data analytics role after they graduate. Using a data set, they determine what kinds of questions to ask, what they want to figure out, what kind of problem they want to solve. They have to apply learned programming skills to figure out a ‘best’ solution.
  • Capstone Projects - Students work on individual capstone projects towards the end of their program. It can be focused in the field they’re interested in - perhaps where they want to get a job - and they use a publicly available data set, ask a question, go through the data to answer it, and put together a presentation.

  Experiential Learning might look different depending on the program length (a one-day workshop vs. a 22-week program) but there is always a real-world application for what you’re learning - you’re not learning skills in isolation or imagining what you might do, you’re actually doing it.

  And how will students see self-assessment and reflection at Level? ​

  At Level, we’ve embedded “Check Your Knowledge” self-assessments into the curriculum. They don’t count towards the grade, but students can take these assessments whenever they learn a new concept, check their own answers, and redo them if needed so they don’t have to wait for an instructor to grade and return it - they can get feedback in real-time. They can reflect on how they’re doing and get support, so that when they reach the higher-stakes projects and exams, they have had lots of chances to check their learning along the way.

  How does Experiential Learning prepare students for work in a real job?

  There are three main components that shape Experiential Learning that are relevant to having a job and connect to a workplace setting:

  • Ambiguity - As a Data Analyst, you’re going to face conditions where information isn’t complete or it’s uncertain. In the Capstone and xCases, we include a degree of ambiguity where you have to figure things out using deep critical thinking - the more times you do it, the easier it becomes, and you become more comfortable navigating ambiguity.
  • Realism - In traditional education, teachers ask students to discover a specific outcome, but that’s not as realistic as it would be in the workplace. We design projects correspond to an actual business problems and leave the space for students to figure something out - there isn’t necessarily one correct answer or solution.
  • Complexity - In the workplace, you don’t always know what decisions you’re going to make and what the outcomes will be. We include a degree of complexity that builds over time, leading up to the capstone project.

  The final Capstone project is the most complex, the most ambiguous, and the most realistic project the students do. They build these skills over time so that when they become a Data Analyst and face something in the workplace, it’s not paralyzing. It’s one thing to know the skills, know the content, and know the theory – it’s a totally different thing to apply that to the work environment. We try to give students as much experience as possible so that after they graduate, it doesn’t feel unsafe and they know they can be successful.

  What role does the instructor play in experiential learning? What should students expect from their instructors at Level?

  Level instructors come from the industry so they have the background, theory, and real-world experience and provide that lens for students. Along with the Teaching Assistants, they provide theoretical background for a deep understanding and bring out real-world relevant connections.

  The instructors are constantly moving between theory and practice, teaching lectures and then weaving it together in labs so the students are learning and then doing. All of our instructors are trained in the fundamentals of Experiential Learning and have access to curriculum designed to implement those concepts.

  Level teaches in-person classes in Boston, San Francisco, Seattle, Silicon Valley, and Charlotte, but also online in a virtual classroom. Is it possible to apply Experiential Learning to online learning?

  Our online programs all have instructors – you’ll always be meeting with someone virtually to do the same work as the in-person classes. However, that’s translated to a virtual environment through tools like Zoom that allow students to be interactive. We use Zoom breakout rooms to group students together and work on labs and the instructors can pop in and talk with the students. It’s very interactive - it’s not just sitting and watching an instructor on a screen, students get to interact together and collaborate virtually.

  Interested in learning more about Level’s Data Analytics bootcamp? Visit their website for more information and read reviews on Course Report.

• Alumni Spotlight: Isaiah Coates of Level

  Liz Eggleston2/2/2017

  Level

  

  Isaiah Coates is perfect proof that traditional web development isn’t for everyone. As a freelance web developer, Isaiah knew he had a knack for technology, but was way more interested in databases and problem solving. So he decided to attend Level’s Data Analytics bootcamp in Charlotte, North Carolina, and landed a job at AAA! Learn about Isaiah’s real-world capstone project, why he’s staying involved as a TA at Level, and how he’s using his new data analytics skill set as a Web Analyst.

  What were you up to before you went to Level?

  Before Level, I had hit one of the worst times of my life. I had dropped out of college because I had too many financial burdens, and had moved back to my hometown of Memphis. I was working as a technician, repairing Apple laptops. I knew I was good with computers, but honestly, it was a terrible life.

  How did you decide that a data analytics bootcamp was the right education path for you?

  I was a computer science major in college, so I actually started freelancing in back end development and front end development. I like making decisions based on facts and started to understand more about databases. That was a real paradigm shift for me. I realized that I understood computers, I liked math, but I didn’t want to be a software developer for a living.

  I remember reading blog articles about this huge data science revolution that was bubbling up, so I started to explore Level and other data science bootcamps.

  Since I had worked as a freelance developer, I had worked with other developers that actually graduated from bootcamps like The Iron Yard, so I was familiar with the end result of a bootcamp. I understood that you take ~12 weeks, get the skills that you need, and then you get a job that would have taken four years if you went down the traditional degree path.

  How experienced did you need to be for the Level application? Did they give you an Excel challenge?

  We did have an Excel test, which required us to use pivot tables. I didn't know about pivot tables at the time, so I just did this super long work around, but it worked.

  What was it about Level that made you feel like you were going to be successful there?

  Before I chose Level, I looked at a FinTech bootcamp in New York called Byte Academy and I looked at The Iron Yard. Choosing a bootcamp in Charlotte was a huge advantage because my mom lives in Charlotte, so I knew I would just have to save enough money to pay my bills and operate as a human being. So I decided to start the application process with Level.

  Location was a real differentiator for me, but I also got a scholarship to Level after I finished the application, so that was huge. I also chose the data analytics program because I was trying to get away from the traditional web languages– Python, Java, HTML, and JavaScript. Looking back, I now realize that at the time I was a bit ignorant because I didn't know that analysis required programming also, but just a different form.

  I also appreciated that I could learn data analysis from a reputable university. I had heard of Northeastern before; I'd seen their logo and their colors, so I knew that they were credible.

  Tell us about the learning experience at Level. What did you learn and how was it taught?

  Now that I work in analytics, I realize that everything we learned at Level was super relevant. The entire course is cumulative, the concepts build on top of each other. We started with statistics, because everything in data analysis goes back statistics at the end of the day. Everything from basic statistics, to probability, to logistic regression (essentially finding a correlation between two data sets).

  We manipulated data, learned how it looks, learned how to clean it and normalize it and apply statistical ideas that we learned in the first two weeks. Then we started working with datasets in Excel, where we got really dirty. Now I can actually boast that I know essentially everything about Excel!

  Once we had a grasp on doing analysis in Excel, we moved on to R, which is a programming language and a really mathematical language. R accomplishes everything that Excel does, but does it with one line of code! Then we moved onto MySQL and NoSQL, which is how we pulled data out of databases.

  In the last two weeks of class, we started learning more technical machine learning.

  What did you build for your Capstone Project?

  Our final project was with an actual company. I partnered with a coffeehouse in Boston called Pavement. They were having inventory problems and felt like they were wasting too much of their product. What we really found out from the Capstone Project is that the shipments honestly fluctuated way too much, so we couldn’t really standardize the ordering. While it was hard to predict his future shipping schedule, I gave him a template for a good shipping schedule based on what he had accomplished in the past.

  The project wasn't super successful in my eyes. However, the business owners got a lot of insight from my learning and that's all that really matters at the end of the day.

  What technologies did you use for the Capstone Project?

  I did the Capstone Project while I was learning Machine Learning and Tableau. Machine Learning uses R and is like the predecessor for artificial intelligence, and Tableau is basically PowerPoint for data.

  My project really helped me through machine learning and R, and it helped with my presentation skills through Tableau. I've never been afraid to speak in public, but I also hadn’t ever professionally presented findings, so that was a great experience.

  We did these projects solo, but we bounced ideas off of each other in class.

  Tell us about your new job! Are you a data analyst?

  I’m a Web Analyst at AAA, and I work in the marketing department. It's crazy how well this has worked out in the end.

  How did you end up getting the job at AAA? You mentioned the final project was a big part of it, but was it through Level? Is AAA a Level partner?

  I can't help thinking that about 80% of the reason that I got this job was my capstone project. Most of the interview was about the capstone project. They wanted to hear that I'd used data analytics in some form, but they also wanted to know about my web development experience because I need to understand the structure of the Internet in order to be a Web Analyst. This is exactly where I wanted to be.

  Level and AAA do not have a connection. I actually got the call from AAA on a Friday afternoon, and I was completely done with my job search. I had been looking for 2 months, and felt like I was ready to give up! When I got this random call from a recruiter, and from there, the hiring process literally took four days.

  I got the call on Friday and then I scheduled a phone interview. I talked with the recruitment agency on Monday, and had a phone interview with AAA on Tuesday. I had a face to face interview on Wednesday, and I got hired on Thursday.

  I'm also a TA once a week and offer office hours on the weekend at Level.

  At Level, did you learn everything you needed to know for your job as a Web Analyst at AAA?

  Data analysis is a super broad field. My job now actually requires a few tools that aren't taught at Level - Google Analytics, for example. I think it just depends on what’s required in the scope of each job. I use Excel on a daily basis, no exaggeration.

  Here’s what I find most outstanding about Level, during my entire time at Level, I learned Excel, statistics, RStudio (which is R, SQL, Tableau and Orange). All of these are open source (except Tableau) and free software which combine to make a suite called SAS, which is probably the most sought-after skill I've seen in my entire job search.

  I once read an article that said SAS was the most valuable skill professionally right now. Everything we learned in Level made us have really in-demand skills, and I think that's awesome.

  Now that you’re a Teaching Assistant at Level, do you think that it's important to have TA's at a bootcamp who actually attended the bootcamp?

  It's been super beneficial for me and my students. I can tell them that I did that exact project, not to worry because we all freaked out about it, and it works in the end.

  I do believe that there is definitely a 100% benefit to having alumni that are readily available to talk to students, and to talk to incoming applicants about Level if they’re considering it. It's super valuable not only for the people in the program, but also for mentors and TA’s themselves. I've learned a ton from my students. They find new solutions and different issues with the projects that we didn't find. They're doing their own self-learning, self-teaching, and introducing me to new blogs that I can read or new concepts that I haven't heard about. It helps a lot too when you start job searching.

  What types of job roles do Level students get when they graduate? Have you talked to your current students or friends from your cohort?

  I see people end up in jobs that combine their past experience with their new analytics skills. For example, I was a web developer, then I went to this web analysis program, and now I'm a Web Analyst. In my cohort, one of my classmates was a CPA, and then looked for jobs as a Financial Analyst. I heard that another of my classmates is working as a Credit Risk Analyst.

  What was the hardest part of your career change? Do you have advice for somebody who is considering doing Level or learning data analytics?

  One thing that actually differentiates Level is that they invite employers to come in and speak about data analysis– recruiters, staffing agencies, etc– but they don’t do traditional job placement. So don't come to the program thinking "Okay, I'm putting my eight weeks in, at the end of the program they're going to get me a job.” You must be a self-starter and a go-getter, take this opportunity as a step and try to progress yourself to the next point.

  What was your plan of attack for getting a job after graduating?

  I remember exactly what I did. The last day of class was a career day, so we had a lot of speakers come in that day, and I called every single one of them to schedule an interview.

  On Monday, I started setting up appointments with whoever I could reach. Each week, I think I was sending like 200 resumes. Then depending on which callbacks I got, I would adjust my resume. I did that for about six or seven weeks. After a while, I started getting called back for analysis positions. Then one day, I got the call that changed everything. But really, you have to be in charge of yourself, be a go-getter.

  What’s your advice to future bootcampers who want to make a career change like you did?

  You can’t have any hesitation. You have to put everything into this, because you have no time to do anything else.

  Transitioning careers is not something that's easy for anyone, in any sector. It's a difficult process, but don't get discouraged when you're in the course because you're learning this for the first time! I'm not going to sit here and lie to you and tell you that's easy because it's not at all. You have to be willing to put in effort every single day to accomplish your goals.

  It's really just will power. At the end of the day, you're learning something new, and the only way to get through it is to do it one step at a time.

  Read more Level bootcamp reviews on Course Report and check out the Level website!

  About The Author

  

  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 Stewart12/30/2016

  Claim AcademyCodesmithMetisCoding TempleThinkfulHack ReactorGeneral AssemblyWyncodeTech ElevatorGrowthX AcademyBov AcademyIronhackFlatiron SchoolFullstack AcademyDaVinci CodersRED AcademyWe Can Code ITDesignlabV SchoolGrace Hopper ProgramLearningFuzeDev BootcampLevelThe App Academy NLTurnToTechStartup Institute

  

  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 →

• Episode 9: November 2016 News Roundup + Podcast

  Imogen Crispe12/1/2016

  Coding DojoRevatureDeltaV Code SchoolAlliance for Digital Employability Craft AcademyNational American University Coding BootcampHack ReactorWyncodeRefactorUOperation SparkBottegaFlatiron SchoolTuringLevelBerkeley Boot CampsGalvanizeGW Boot Camps

   

  Continue Reading →

• August 2016 Coding Bootcamp News Roundup + Podcast

  Imogen Crispe8/31/2016

  Coding DojoAdventure Code SchoolEmpower Security Academy42Hack ReactorGeneral AssemblyWeb Dev CampK2 Data ScienceSecureSet AcademyLaunchCodeMakerSquareFlatiron SchoolLaunch AcademyIvy Data ScienceDeVry BootcampAda Developers AcademyHackbright AcademyGrace Hopper ProgramMaster 21 AcademyDev BootcampEpicodusBlocLevelUNC Charlotte Boot CampsBitmaker General AssemblySabioGalvanizeZip Code WilmingtonData Application Lab

  Welcome to the August 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 news is the Department of Education's EQUIP pilot program to provide federal financial aid to some bootcamp students. Other trends include job placement outcomes, the gender imbalance in tech, acquisitions and investments, and paying for bootcamp. Read below or listen to our latest Coding Bootcamp News Roundup Podcast!

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• May 2016 Coding Bootcamp News Roundup + Podcast

  Imogen Crispe5/31/2016

  Interface Web SchoolDigital UnicornThinkfulBarcelona Code SchoolInceptures Software School42LaboratoriaHack ReactorGeneral AssemblyFlatiron SchoolCode Career AcademyDevelopMe_Hackbright AcademyDev BootcampLevelGalvanizeDevMountainTreehouse

  Welcome to the May 2016 Course Report monthly coding bootcamp news roundup! Each month we look at all the happenings from the coding bootcamp world, from acquisitions, to new bootcamps, to collaborations with universities, and also various reports and studies. Read below or listen to our latest Coding Bootcamp News Roundup podcast.

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• How I Learned To Stop Worrying And Love Data Analytics

  Kendall Gibson4/18/2016

  Level

  

  If you asked me two months ago what data analysts do, I most definitely would have given you this clownish answer: “They analyze data.” That’s because I tend to act like a know-it-all, especially when asked seemingly simple questions. Then again, I didn’t know very much about data analysis back then.

  Now, after finishing a data analytics bootcamp run by Level at Northeastern University, I can clearly see myself answering that very same question with many more words, a lot of more thought, and much, much more confidence. I should know; I have been asked to do so several times in job interviews.

  So what do data analysts and data scientists do? In short, they provide quantitative support. They are the people that managers, developers, even CEOs go to when they need guidance. They are modern-day priests, and data is their holy scripture, and I will say that my bootcamp experience was successful in ordaining me.

  Any department, from any organization, can benefit tremendously from consulting a data analyst or scientist. Given reliable data and enough time to work, they can come up with all kinds of valuable insights regarding anything, from identifying key-markers for potential sales growth, to pinpointing the exact temperature to set a thermostat. With data analytics skills, you can qualify for jobs like business analyst, health data analyst, financial analyst, or director of marketing.

  Making those insights can be difficult, as it turns out, which is probably why Harvard Business Review called data scientist “the sexiest job of the 21st century.” Smart, after all, is the new sexy. But becoming smart simply means learning the right stuff, and figuring out what you are best at. Level taught me that, in the wide world of data analytics, there are many nuanced positions that each require specific skills. I believe that focusing on the skills you are comfortable with and interested in is the best way to find employment, in any field. So without further ado, here are my top tips for how to add data analytics to your skillset.

  4 Tips to learning Data Analytics

  1. The best way to learn data analysis tools is to use them.

  In the morning we had lectures, in the afternoon we had labs. Each Level lab had something to do with the lecture. During labs is when the concepts we learned from the lectures gelled for me the most, personally, which is the case because I was applying them firsthand. Literally. For example, when we started out we were learning simple probability, which was something I thought I had mastered since I took two classes on it in college. But working with experimental results in Excel, R and Tableau made things like Bayes’ Theorem and type I & II errors really make sense to me, in ways they had never before.

  Finding data can seem like an arduous task, but with the power of the Internet it is made simple. There are many free resources for large sets of data, including Data.gov, Kaggle, and Amazon Web Services, but what one guest lecturer told me was that he found that charities usually have large data sets just sitting around, and will usually be happy to let someone take a look at them. I am currently doing volunteer consulting work for a Boston-based charity organization, which has given me hours of valuable practice time, and may also make a difference.

  2. Presenting on Topics Helps Solidify Lessons

  Every other week during the Level course, we were given a project that we were told to give a presentation on. Some were individually based, and some were in groups. This, and other exercises we participated in, helped me in learning soft skills that will definitely come in handy throughout my career. Getting up and speaking in front of people was not a new experience for me, but interpreting an analysis of data to a live audience was.

  One of the projects I worked on was that I looked at web analytics for my own blog, and worked out how consistent my writing schedule was for past years. Explaining the process and interpreting my results made me feel slightly nerdy, for having to tell strangers how many science-fiction short stories I had written, but I loved getting feedback because it gave me confidence, and it helped me refine my public-speaking skills. The number is 49 and still growing, if you must know.

  3. Lecture is great, but field work is necessary

  I thought the classroom part of the bootcamp was ideal for learning all aspects of being a data analyst or data scientist, except for one thing: gathering requirements. Learning what the problem is that you have to solve is an important part of the data analysis process, because each data analysis problem is unique, and it is difficult to develop that skill in a classroom setting. Most of the problems we worked on were textbook examples with foregone conclusions; there was no element of ambiguity about them. That is where the capstone project came in.

  Everyone in the course got paired up with an organization who had a real data problem on their hands, and we were tasked with finding a solution to it, and presenting our results firsthand. The instructors advised us to ask our sponsors lots of questions about their data, and the problem that they needed solved. Doing that proved to be super useful for framing my approach to the project, so I highly recommend it if you are working on a data analysis project yourself.

  For my capstone project, I worked with a large market research organization. Because I signed an NDA, I must keep certain aspects of my project a secret, but I will tell you that it had to do with survey data. I was given raw data that they collected from one of their surveys, and was asked to conduct a preliminary investigation as to what insights could be drawn from it. This gave me a chance to practice manipulating a large data set, and visualizing what trends I could find in it that I thought were insightful. Putting a presentation together, which is something data analysts constantly do, pulls together many skills that you really need feedback in order to work on, so practicing them in the field is crucial.

  4. Learning with Peers is a Must

  Having a motivated group of peers to navigate the curriculum material with was energizing, and felt unique to a bootcamp. The accelerated pace that we were learning at made me feel proud of what I was doing, and I could feel that the others were radiating that same sense of pride. If you choose not to learn data analytics in a classroom setting, I believe that you will be missing out on the inspiration, motivation, and feeling of mutual respect that you would have if you were to go through learning it with a group. Even if they come from just one other person, those things can go a long way, so I suggest making a friend who will learn data analytics with you.

  From the beginning of the course, the Level instructors emphasized that our classmates were a) going to help us in learning the subject material and b) going to be a part of a shared network for a long time after graduation. I loved the feeling of solidarity that Level fostered between me and my classmates, and believe that it made it easier for me to learn the material. Maybe I got lucky, because my cohort at Level was fantastic, but I am sure that any group can find will provide you with at least some moral support to gain strength from.

  What's Next?

  After gaining analytical skills, I feel that I can view business problems from a more objective perspective. I hope that providing quantitative support is a part of my roles moving forward, because I would get to solve problems and communicate my ideas, which are things that I enjoy. I would like to work at a non-profit organization, because I would feel proud having my work make a difference.

  I thoroughly enjoyed my experience with Level, and would emphatically recommend a bootcamp style course to anyone who is looking to learn data analysis skills in a short period of time. The bootcamp Level offers is rigorous, fast-paced, and challenging, but completing it was a life-changing experience for me.

  To learn more, read Level reviews on Course Report or visit the Level website here!

  About The Author

  

  Kendall is a recent graduate of Level, an intensive, two-month course on data analytics where students learn about data analysis tools, procedures, and techniques. You can read more of Kendall's writing on his blog.

   

• Learn to Code in 2016 at a Summer Coding Bootcamp

  Liz Eggleston7/24/2016

  NYC Data Science AcademyLogit AcademyMetisTech Talent SouthGeneral AssemblyWyncodeIronhackFlatiron SchoolFullstack AcademyMake SchoolLevelNew York Code + Design AcademyCode Fellows

  See our most recent recommendations for summer coding bootcamps here!

  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.

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• How to Choose the Best University Coding Bootcamp

  Liz Eggleston4/12/2019

  DevPoint LabsRevatureUniversity of Arizona Boot CampsWyncodeKU Boot CampsDeep Dive CodingSMU Boot CampsFlatiron SchoolFlatiron SchoolUCLA Extension Boot CampsNorthwestern Boot CampsUC Irvine Boot CampsRutgers BootcampsUNC-Chapel Hill Boot CampsUniversity of Utah Professional Education Boot CampsUT Austin Boot CampsEpicodusUCF Boot CampsUCF Boot CampsLevelCase Western Reserve University Boot CampsUNC Charlotte Boot CampsBerkeley Boot CampsSoftware GuildUC San Diego Extension Boot CampsUniversity of Denver Boot CampsGW Boot CampsGeorgia Tech Boot Camps

  You've heard of household bootcamps like Hack Reactor, General Assembly, and Flatiron School – but have you noticed universities that offer coding bootcamps? Universities have now been partnering with coding bootcamps since 2016, but these university coding bootcamps aren't all the same! Research your options below and find out which coding bootcamps offer college credit, which are part-time to accommodate your schedule, and read our tips for choosing the best university coding bootcamp for you. 

  These are partnerships where a coding bootcamp either offers classes on the university campus, taught by the bootcamp’s own professors, or students can study at the coding bootcamp campus but get college credit. Some of these partnerships also allow students to use the GI Bill to pay for coding bootcamp tuition.

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• Data Science Bootcamp: Which is Best for You?

  Harry Hantel4/16/2018

  NYC Data Science AcademyMetisThinkfulGeneral AssemblyData Science EuropeData Science DojoK2 Data ScienceMicrosoft Research Data Science Summer SchoolXTOL.CareersZipfian AcademyInsight Data ScienceFlatiron SchoolScience to Data ScienceSPICED AcademyThe Data IncubatorBitBootCampSpringboardLevelData Science RetreatGalvanizeData Application Lab

  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.

   

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