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5 Things You Must Know to Succeed in Your Data Science Program

A simple guide for aspiring Data Scientists

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Photo by Marvin Meyer on Unsplash
Photo by Marvin Meyer on Unsplash

In March of 2019, I was accepted to NYU’s Master’s of Science in Data Science program. I was extremely excited but unsure of what I needed to know and what is to come as I did not have prior data science experience. I spent my summer before my first year preparing, and now that I am in my final year, I have tips for others entering a similar program. An article like this could have better prepared me for my first year, even considering the unprecedented 2020 circumstances.

If you feel like you are in the same place, follow these next 5 steps to prepare for your data science program. Some of these tips might not be news to everyone but can be entirely new for someone lacking experience. If you were accepted, that means you have what it takes to succeed; you now have to put in the work!

1. Brush up on Calculus, Linear Algebra, Probability, and Statistics.

If there is anything you are going to take from this article, it is THIS. If you’re like me, you might’ve gotten high marks for all of these subjects, but you haven’t touched them in years, so you need to brush up. Probability and Statistics are the core drivers of data science, so get very comfortable with Bayesian and Frequensic Statistics. For Linear Algebra, refresh on matrix transformations, multiplication, and Eigenvectors/values. To refresh calculus, review derivatives, integrals, logarithmic rules, and these applications in multivariate space. All three of these topics come together in your basic Machine Learning class. Below are some resources to help you review:

Photo by Steven Geringer
Photo by Steven Geringer

2. Practice your coding skills

Python is one of the more popular languages in data science due to the built-in packages that allow you to model with ease. Not only is coding in Python essential, but being able to swiftly solve basic programming questions will be extremely beneficial, especially when completing technical interviews for internships. The best way to practice these types of problems is on sites like HackerRank and LeetCode. Some common problems you might want to focus on involve: matrices, arrays, and dictionaries. A common approach to tackle these problems is to filter for easy and then work your way to medium and hard questions. **** I practiced on these sites the entire summer leading up to classes, and it put me at an advantage for my Programming for Data Science class at NYU.

Another helpful resource is Python Crash Course by Eric Matthes; it is an informative beginner’s guide to coding in Python!

3. Complete a Data Science project

A huge benefit when interviewing for internships is prior data science experience. If you don’t have any data science projects on your resume, you can spend your time working on one from a data science competition site or come up with a project on your own. Some project websites are Kaggle and CodaLab. Avoid common projects such as the Titanic problem; employers have seen it too many times. If you are worried about your level of expertise affecting your ability to complete a project, start with a simple project that you can find guidance on (Github, Medium, etc.). Then, take what you learned and apply your knowledge to a new one. All of the resources are out there to help; you need to utilize them.

A project you come up with on your own should be something you’re passionate about; think about your interests (sports, health, news, etc.), and create a project that solves a problem within the topic. You might have to look at existing projects for inspiration, but it will be worth it since most employers prefer a self-made project. This self-made project will show your passions, and you’ll be able to speak on it easily. Once you have a project on your resume, you’ll have an advantage both in the classroom and during interviews. After your first semester, you will most likely have group projects and add them to your resume, but having this initial project will give you a head start.

4. Read up on best practices

NYU’s administration recommended Data Science from Scratch by Joel Grus, and it was the most helpful resource going into my first year. I don’t know what I would have done without it! You might not understand a lot on your first pass through, but you can refer to the book and get a simple explanation of difficult data science concepts when you are in class. The book also provides access to code on Github, which shows basic implementations of data science concepts. I recommend reading this book and exploring the internet for some other simple introductions to data science; let your curiosity take you. Below is a link to Grus’ book and some other links to help you get started.

  • Data Science from Scratch: First Principles with Python 2nd Edition by Joel Grus
  • The Absolute Beginner’s Guide for Data Science Rookies by Ignacio Montegu

5. Build your network

My biggest take away from undergraduate business school is that you are just as good as your network. Spend a bit of time each day building your network. Connect with professionals in your desired career path or company; LinkedIn makes it very easy. Reach out to these professionals and ask for an opportunity to connect and maybe even a quick phone call. Ask about how they got to where they are, let them talk about themselves. Then ask for recommendations; perhaps you need a suggestion for a class to take or a specific subject to focus on (NLP, Big Data, etc.) to be at Data Scientist at that company. Every data science department is different and has a different definition of a Data Scientist. If you want to be at their specific company, it is good to know what their job entails.

There is no harm in connecting with professionals, and at the end of the day, you’ve left a good impression and have a new connection. Building your professional network ahead of your first semester gives you a head start in the internship search and helps you decide what classes to take.

In addition to networking with industry professionals, it’s also important to network with your classmates. Please spend some time finding those on LinkedIn with your program in their bio. These students will soon be your teammates, study partners, and support; it is nice to connect with them as I am sure they would be happy.

It is important to understand that your peers are your classmates and will become your professional network. These students will get jobs post-graduation, and who knows, maybe it will be a door for your dream job! Job prospects shouldn’t be the main reason to connect with your cohort, but it is something to keep in mind.


You are now ready for your data science program! By allocating your time to the tips above, you will be better prepared for your data science major and are already a better data scientist than you were before. Please feel free to comment on any other helpful resources or topics I’ve missed. Good luck!


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