Thinking of enrolling in a data science course but not sure what to expect from the day-to-day as a data science student?
In this post, we’ll cover the typical day-to-day schedule of a full-time student from when you sign on at 9 a.m. to when the day ends at around 6 p.m.
Every day begins with a group check-in. The goal of the morning check-in is to get you ready for the day, set goals, and remove any blockers that may come up.
Sometimes you’ll answer icebreaker questions or solve short warm-up questions to get the gears in your brain turning and primed and ready for the day!
This sets you up for success so you can make the most of your time in the classroom today.
A typical day will involve two lectures. You should come ready to engage and participate – our lectures are interactive! These live instructor-led sessions are deliberately engaging to help move your learning out of the theoretical and practice what you’ve read in the written curriculum materials.
This is dedicated time to work on core curriculum content, with the support of your peers. Pair programming helps you practice verbalizing concepts and walking through your thinking process for technical interviews, communicating with others, and solving problems together.
Rest, refuel, go for a run – whatever you need to do to come back afterward ready to take on the remainder of the day!
Now is your chance to chat one-on-one with your instructors. You can use this time to discuss your progress, goals, and understanding of core content. It’s also a great time to review labs or checkpoints to go more in-depth into how the code works and how you can bring that understanding to new problems.
Canvas acts as our textbook, and going through lessons and labs, plus taking quizzes to check your understanding, allows you to familiarize yourself with concepts before diving into the lectures to practice what you’ve learned.
We check your understanding of key concepts and tools throughout the course, including during timed assessments that help mimic the pressure of a technical interview (but in an environment we design to be much more supportive to learning and growing your skills).
Afternoon Check Out
The daily wrap-up sets expectations for the next day. You can also use this time block to share current work and personal projects, or discuss struggles and successes with your peers and instructor.
Community events are optional events designed to connect you with your peers and the broader Flatiron School network. These often feature workshops and guest speakers, but also yoga and happy hours – there’s always something going on that you can opt into!
Regular Course Activities
These are mini-assessments to check your understanding and key proficiencies. You can expect them about twice per week. Checkpoints give you a chance to practice coding in a time-boxed session without the pressure and check your understanding of the course material.
As a data science student, you’ll regularly face coding challenges meant to simulate real-world workflows or technical interviews. This will feel just like the checkpoints, but you will have 1 hour to complete the code challenge. This is one place where no collaboration is allowed. We encourage you to ask instructors questions, but these are solely your work.
Either solo or in groups, you will tackle real problems to develop portfolio-ready projects you can showcase to potential employers. Projects will consist of both technical and non-technical deliverables as well as presentations for each to gain experience presenting your findings to a non-technical audience.
Building a digital presence demonstrating your Data Science chops and establishing a personal brand is essential in the job search. To prepare you for graduation you’ll write four blog posts during the course (one for each phase, except the Capstone).
How To Get Started
Ready to take the next step and become a data science student? Apply Now to our Data Science course or schedule a 10-minute chat with admissions to see how you can take charge of your future in as little as 15 weeks.