2. You’ll meet a ton of great new people. Learn from their diverse experiences and use them to build your network.
An immersive education program has students from different backgrounds, and most of them are career-switchers. Each person is a great resource of information, and I had the opportunity to learn from fellow students and hear their varied perspectives. Watching my classmates, who have experience in facilities, journalism, actuary, and economics, tackle a problem from different angles was a great learning experience. I have improved in many aspects of project management apart from model building — including presenting, problem-solving, and scheduling — by learning from my peers.
You’ll also automatically expand your LinkedIn and in-person network by being a part of these programs.
Meeting people from different background offers connections in fields much different from your own. I come from an engineering background and now I have connections in many other industries who will be focused on making moves in the data science field. You’ll see a funnel effect where people from various sectors study together, and then bring their new data skills to a range of roles in other fields. This will be your new network, and tons of opportunities may come from your connections.
When I look at data science roles on LinkedIn, I’m connected to more people in a particular company through General Assembly than through my graduate school, University of California, Irvine.
3. Your instructors can make or break the experience.
This one is a cliche and can be said of any educational program. Just like a bad boss can make you hate your job, a bad instructor can break this experience for you. Fortunately for me, my instructors have been excellent and were able to distill difficult concepts like Bayes statistics into simple, lucid form. Smart folks are not always great teachers, but my instructors at General Assembly were both.
A great instructor should have subject-matter expertise and the ability to distill it down for folks new to the topics. They should repeat themselves, and if students are confused, try other techniques to make sure they understand the content. They also need to have patience, and make sure students never feel stupid asking questions. All questions should be answered so that everyone in the class understands.
My instructor, Matt Brems, broke down Bayes equation terms like posterior probability, marginal likelihood, and prior probability, repeating them every day for the first week. Every single day, in both the classroom and during office hours. In the end, I can close my eyes and explain core statistics concepts to people in ELI5 (“explain it like I’m 5”) technique.
Please scout your instructors and know more about them before joining class. I was lucky, but make sure you do your research!
4. Choose your final project wisely.
Like many long-form classes, DSI requires students to complete a capstone project that demonstrates the skills learned throughout the course, showcases the branches of data science you want to pursue in your career, and is a culmination of 12 weeks of coursework.
My friend Ben, in his project, wrote an excellent blog post about his approach to solving a data science problem, and explained the rationale behind selecting a capstone project with practical implications. His project detailed how he uses data to help his moving company make better estimates for the cost of moving from point A to point B in Washington, D.C. Similarly, my project’s purpose was to help a business, The Whisky Exchange, understand how a company should price its whisky.
The capstone project at the end of the course should accomplish three goals:
It should reinforce your DATA SCIENCE TOOLKIT. I strongly believe that your capstone project should encompass the data science tools that interest you and the tools you want to pursue in your career as a DATA SCIENTIST.
It should be relevant to a business problem/group that needs some solutions.
It should be fun! After all, data science should be fun and learning cool things using data is a very satisfying experience.
If it achieves all of the above, you’ll have a great portfolio piece that showcases your interests, skills, and talents. This will be a great resource to share with future employers, collaborators, and data nerds who want to get to know you better.