Flatiron School Data Science Instructor, Lindsey Berlin, has a passion for learning as much as she can about data science and sharing that with her students. Lindsey knows what it takes to succeed in Flatiron School’s intensive data science bootcamp and shares how students in both the full-time and Flex formats can get the most out of their bootcamp experience. Plus, learn what students can expect in the Flatiron School curriculum, and how she keeps her lessons fresh with today’s newest data science tools and technologies.
How did you get started in data science, Lindsey?
No one person ever has the same path into data science, which is one of the things I love about it! Before attending Flatiron School, I had never held the title “Data Scientist.” I have a master's degree in International Affairs, and I worked at an immigration law firm as a research analyst and a general tech associate. I held positions that were data science-adjacent, but Flatiron School’s Data Science bootcamp brought me directly into data science and fit everything together. Plus, I was one of the first students to go through the data science bootcamp.
What motivated you to teach data science after you finished the bootcamp?
I also have a background in teaching, and I really enjoy teaching! Teaching is the best way to learn, and after bootcamp, I wanted to go deeper into data science. Bootcamps offer the necessary foundation, and then there's so much more to learn. The more I get to teach, the more mastery I develop. This industry is constantly changing. Just today, I learned about a new library that I'm excited to explore and will potentially teach me a different way to do something!
There are a lot of data science bootcamps — what stands out about Flatiron School’s Data Science Bootcamp?
I love the fact that Flatiron School teaches data science – not data analytics. Focusing on data science means that Flatiron teaches students coding and other imperative tech skills. We not only cover SQL and data visualization, which are really cool tools, but also dig into Python and go much deeper into the machine learning side. We're preparing students for many future opportunities.
What does the Data Science Bootcamp curriculum cover?
A lot! Before you even start at Flatiron School, you are introduced to basic Python and data visualization using Matplotlib. Setting this foundation helps to ensure that the content isn’t completely foreign to you going into the program, and that you have some understanding of code to get the ball rolling.
We dive way deeper into Python in the bootcamp. We begin with Pandas, a big data exploration library. You can use Pandas for everything, which is why it’s the backbone of our curriculum. We also teach SQL, data visualization, and the bulk of our machine learning is done in statsmodels and scikit-learn.
By the time we're in the advanced part of the course, we're running neural networks using tensorflow or using the natural language toolkit at NLTK. We build recommendation systems and then we branch off into niche topics. Our goal is to help students find their niche, but even if they don’t find their niche, all our students get a broad set of skills that allows them to work in lots of different ways.
What kinds of projects are students working on?
We start off pretty simple with projects dealing with data parsing. By the end of the course, students pick their own project, which can be anything, so long as it is uniquely theirs. We don't expect them to completely reinvent the wheel, but we want to see students that are looking at data in an uncommon way or looking at a data set that’s hard to work with.
Students leave the bootcamp with all the tools needed to gather their own data by connecting to APIs online or web scraping. We encourage students to go to the data portal for your city, country, or state to find some local data which they can work with. For example: If you're super into NFTs, go to an NFT marketplace and grab a bunch of data and start to explore.
By the end of the bootcamp, students have all the tools they need to gather data to make their own supervised machine learning project that they're excited about.
What is your teaching style like?
My teaching style is very active, and I live code in front of my students. Not all instructors do that, but it gives me a chance to go off on useful tangents to build the foundation of the capabilities of code and how to write it. Depending on the type of lesson I'm running, I'm either:
My lectures and sessions are built around the curiosity of, "How do we share and build things together?" Sometimes people look at code online and see this finished final product and wonder, "How could I ever write that?" I aim to clarify that coding process with my students. I want them to understand how someone got to the clean, perfect piece of code. First they wrote a little piece, iterated, tried, ran into errors, troubleshot, and then got to a place they felt comfortable.
What kinds of changes do you make to the data science curriculum at Flatiron School?
We are constantly iterating our curriculum to reflect the needs and requirements of the current job market, so we can keep our commitment to helping our students find jobs after graduation. To do this, we look at what jobs are out there now, the jobs our current alumni are getting, and which skills do those jobs need.
Flatiron School has a base curriculum and all instructors have sessions and learning objectives they need to meet, but every instructor has control over the delivery of their own lesson plans. For example, I love the deployment aspect of machine learning and talking about how to actually use your model in production. So I run extra sessions around that, so students can use new libraries that are just being solidified. I share those recordings and invite other students in so that they have a border horizon of what data science can look like now.
After teaching at the Flatiron School the past few years, do you think that there is an ideal student for the Data Science Bootcamp?
In my experience, the students who typically succeed the most are ones who have a clear idea of why they want this and what they want to do with it. Typically, this is someone who has job experience. These students have worked within an industry and are usually able to put the teachings in more context. They’re also able to anchor their learning to an industry they're excited about or something that they want to go back to with new skills. Knowing what you want to do with this data science program helps to anchor, drive, and find your niche so you can market yourself after graduation.
Do you recommend total data science newbies do some self-teaching before applying to the bootcamp?
Yes and no. Immersing yourself in the language certainly doesn't hurt, but I would say one of the hardest parts about getting started is getting started. When you're first getting into tech and code, you have no idea when something is wrong because of something you did or because of something else. That can be really frustrating when you don't have the context yet to recognize the difference. Getting comfortable with the content is the best way to ease that process.
Flatiron School offers free data science lessons if you want to check out what it looks like before enrolling in our dedicated bootcamp. Some of our instructors also run YouTube channels with intro videos on Python. Watching YouTube videos or doing something more hands-on are the best places to start immersing yourself in tech.
How many hours a week do you expect your students to commit to Flatiron School?
That totally depends. Flatiron School offers a 15-week, full-time data science bootcamp as well as a 40-week, part-time bootcamp. Whatever you commit to at the beginning of the experience is what we hold you to.
In the full-time program, we expect a bare minimum of 40 hours a week, but realistically, it’s closer to 60 hours when you include the practice lessons, studying, and time connecting with other students. Learning live online means a commitment to many daily Zoom meetings and Hangouts.
Flatiron School’s part-time Flex program offers more flexibility. You’ll set a pace at the beginning based on when you would like to graduate, and we’ll hold you accountable to that timeline. Accountability is really important when following through with this program.
Tell us about your favorite bootcamp student success story so far!
Flatiron School uses a phase structure, with five phases in the current structure of our curriculum. About a year ago, one of my students went through Phase 1 and immediately got a job! A company needed the skills we taught in that first phase, which kickstarted this person’s career into an excellent full-time position down the road. This person started with no tech experience, was previously managing hair salons, started the program part-time, mastered this content early on, and was able to keep building as they continued with the program. When the company saw what they were learning in the bootcamp, they hired them on from a contract position to a full-time role. Now they're doing data modeling and much more of these other pieces that we taught them later in the program, but all they needed to get in the door was some Phase 1 intro stuff!
How do you assess student progress? Should students expect tests or exams?
It looks different depending on which learning format you commit to. All students, regardless of their enrollment style, are graded on projects since that’s what employers are looking for in interviews.
If you do the live full-time program, nearly half the length of the program is dedicated to projects. Full-time bootcampers also have code quizzes and tests to ensure you’re understanding key concepts before moving on. Since it’s so fast-paced, it’s easy to get behind and overwhelmed if you don’t have those core concepts solidified. We put the most emphasis on empowering students to drive their own learning to get the most of the bootcamp.
What’s your career goal for a student that completes the data science bootcamp?
It depends on what aspect of data science students are excited to pursue. When all a student wants to do is tweak machine learning models, it’s no surprise when they end up as a machine learning engineer. Other students that are passionate about finding insights for companies pick up titles like Business Intelligence Analyst.
How can bootcampers stand out in today’s data science job market?
Pick projects you actually care about, are passionate about, that drive and excite you, and make you want to come back to it again and again. Learning data science is hard! If it's not exciting for you, that's no fun and why would you torture yourself? Work on data that you care about. I've listened to podcasts of people hiring data scientists who say that potential employers would rather see your excitement and passion than look at a project that you thought they would like.
Additionally, the skills you learn at bootcamp are translatable. No matter what data you're working on for a certain project, it shows you can work on any kind of data set.
Which resources do you recommend for complete beginners who want to start their career in data science?
First, figure out why you want to enter the tech field and find resources that tailor to that desire. If you're excited about the idea of data science, but don't really know what that looks like for you, find problems or questions that are interesting, and see how other people are solving them and talking about them in podcasts, blogs, YouTube, or TikTok. There are great conversations being had in these spaces that can enlighten you to the field. Expose yourself to the language and what it looks like to work in data science. There are tons of data science podcasts. Towards Data Science is a blog aggregator and podcast from which I've gained valuable insight.
In general, Google is a great resource. They own a data science competition website called Kaggle that hosts short courses that are a bit more technical. Start by googling how people solve an issue using data science. If you come from healthcare, google “healthcare data science” and see what comes up. Follow the trail to the blogs, podcasts, and videos to build your resources.
Plus, there are beginner Python, Pandas, Tableau, and SQL resources that will be really useful. People who know why they want to do this are so much more successful, so find what you’re passionate about and dive in!
Do you see data science as recession-proof?
It has been for me! I've seen it bloom over the last few years of chaos. At the end of the day, if you can automate problem-solving for companies, you're always going to be a better hire than someone who needs to do something manually and slowly. What you're learning with Pandas, Python, and machine learning is how to grab insights from data programmatically and automatically. People still need those insights, still need to have a sense of what's coming, need to know what's in their data and what it's telling them about their customers and products. If you can do that effectively using data and using programming, I have not seen the limit to those jobs yet.
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