How to Prepare for Your Data Science Program
July 29, 2020
Congratulations! You have applied and earned acceptance to a data science program. How can you prepare before class starts?
Congratulations! You've completed all the right steps to earn acceptance to a data science graduate program. Now, what can you do to get ready to start your coursework? Being admitted to the program means that your background is well-suited to your program. Nonetheless, if you are anything like me, you might want to know what you can do to prepare so that you hit the ground running. While your needs will vary depending on your background, I've shared some topics below that could be beneficial to refresh prior to starting your coursework. Some programs even require additional classes surrounding these topics to get you ready for other degree requirements, so be sure to tailor your prep work to your own program!
(1) Laptop Access: Having laptop access is important to learning data science by doing. The good news is that as the field continues to grow, most laptops (Mac or PC) will do the job! If you are purchasing a new laptop, try to get in touch with current students in your program to review their recommendations. In general, laptops with more memory will be helpful. It is possible that your school will have accommodations for laptops if you need it and some coursework may be run online via pre-configured environments. Reach out to your program to ask for specifics.
(2) Programming: It is likely that, in some form or another, your assignments will be completed in a programming language well-suited to data science, such as R or Python. If you have experience programming in these languages or a similar one, learning another within a course should not be a problem. If you have less experience, reach out to your program to see which languages your degree will be using. Then, start simple using Jupyter Notebooks for Python or RStudio for R. If you already have some experience programming but are new to data science, consider reviewing some of the popular data science libraries like numpy, pandas, or sklearn in Python and dplyr, ggplot2, and caret in R. Getting familiar with relational databases through SQL is also a great idea.
(3) Probability, Statistics, and Linear Algebra: If your program requires a probability and/or statistics course, you can start reviewing common fundamentals like conditional probabilities, random variables, and linear regression. One good resource for probability is Ross's A First Course in Probability, which I used in my undergraduate probability theory coursework. Check out your library resources that just became available to you with an active student account! You can also consider brushing up on Linear Algebra via online resources like Khan Academy. Many data science building blocks are based on these mathematical concepts, so having completed coursework in, or having even an introductory knowledge on, these topics will be vital to moving beyond standard implementations of algorithms to understanding how the algorithms work.
If you already feel prepared on these topics, there are a few other topics that would be helpful to review:
First, career development: Consider reviewing common job interview questions in the data science field. Interviews start as early as September for summer internship positions, so getting a head-start before you have homework is a great idea! Are you prepared for a career fair? What kinds of employers interest you and why? Do you have an up-to-date resume and cover letter template?
Next, since data science is a growing field, there is always lots of new research to read about. If you're already familiar with the field, consider delving into new data science topics in a field that interests you, especially by reading up on any research being conducted within your program. This will not only help you to get familiar with some up-and-coming topics, but also get you in the mindset of constantly learning. Now, you're starting to think like a data scientist in the field!
Acknowledgements: A special thanks to my degree program for sharing some of these topics with me before starting my program!