5 Ways to Improve Your Data Science Skills in the time of COVID-19

Published in

Towards Data Science

8 min readApr 9, 2020

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The worldwide COVID-19 pandemic has caused an unprecedented, stressful and uncertain time. The world is on complete lockdown and we all must do our part to help each other by practicing responsible social distancing and remaining indoors as much as possible. In this current situation, it is easy to feel at a loss of what to do and it can be extremely difficult to maintain the feeling of productivity in your new daily routine.

Personally, since I’ve been working from home, I’ve struggled with separating my “work-from-home time” with my “personal time,” causing my work/life balance to lean heavily towards the “work” side. My favorite thing to do when I wanted to switch gears from work mode was to go walk around my neighborhood in Brooklyn and read in the park or visit one of the local coffee shops. Since that isn’t possible anymore, I have resorted to working longer hours because I have the constant feeling that I just haven’t been doing enough. The lack of purpose in my personal time has driven me to re-evaluate how I want to spend it. The quarantine has given me time to think about my professional career path and what I can do now to better position myself to meet these goals after this crisis is over.

In this time of uncertainty, there is one thing I can say for sure: I am not alone in this feeling. This constricting feeling of cabin fever is affecting everyone in the world right now. One memorable piece of advice that has stuck with me through this time is to ‘control the controllable.’ The world is filled with so many things that are out of our control, so it is important to not get overwhelmed and to focus on the things you can control in your everyday life. One thing you can control is improving and investing in yourself every day!

I put together this blog for anyone who has spare hours and wants to push themselves to learn new data science skills or brush up on their current skills. I hope this serves as either a productive distraction or a little bit of encouragement to put some of your extra time to good use!

Photo by Emma Matthews Digital Content Production on Unsplash

1. Learn A New Data Science Skill with an Online Course

The hardest part of learning a new skill is knowing how and where to get started. Good thing there are a ton of online course materials and resources that are geared towards self-starters (like you!). There are tutorials and lectures available online to learn a specific data science tool or for more broad concepts.

Here are just a few of the many resources available:

Coursera-

Pros: What I like about Coursera is that it has a ton of free classes for different programming languages at all levels. The explanations tend to be very clear and concise. There also tends to be examples and exercises to really get a grasp of the concepts.
Cons: The feedback and problem reviews may be slow/delayed which can be frustrating.

Udemy-

Pros: There is a large selection of courses that are low cost (about $10 USD) and you can go at your own pace.
Cons: Anyone can publish a course so make sure you pick courses that were created by experts in the field you are looking for. Definitely look at the ratings of the class before you take it.

Linkedin Learning-

Pros: Linkedin Learning is an easy to use site that you can directly connect to your LinkedIn profile so you can share your skills and interests across platforms.
Cons: The courses tend to be geared mostly towards beginners, so there may not be a ton of courses for advanced data scientists. There is also a lack of accredited certifications which is a bit of a bummer since Linkedin Learning is a paid service provider (~ $30 USD a month).

DataCamp-

Pros: DataCamp was created with the sole focus of learning data science online. This platform offers a large variety of quality content on a site that is easy to use. The pricing is also laid out transparently and gives multiple plan options.
Cons: Much of the content is very text-heavy which can be discouraging, especially for beginners. Some reviews have stated that the courses can be a little ‘hand-holdy’ and don’t provide enough problems to practice.

These are not, by any means, the only resources out there! If you are on a tighter budget, there are also a multitude of Data Science YouTube channels available for free so you can get your learning on!

2. Blogs

If there is a topic or concept that you know you have difficulty understanding, look for blogs from other data scientists on sites like Medium, Towards Data Science, Analytics Vidhya, etc. You are guaranteed to find a blog or multiple blogs that explain confusing concepts clearly and concisely in less than 10–15 minutes.

If you really want to challenge yourself, I highly recommend writing your own data science blogs on concepts you aren’t comfortable with. By going through the writing process, it will help you get a more complete grasp of the subject manner enough to be able to teach it to someone else. I have personally found this to be an extremely helpful, although time consuming method, but it is extremely rewarding because you feel accomplished when you finish it and it is something you can add to your portfolio for future job searches. Being a blog writer shows innitiative and allows recruiters to see how you communicate complicated concepts to a broader audience, which is an extremely valuable skill to have!

3. Data Science Books

There is a plethora of easily digestible and extremely informative reading materials available for data scientists at all different points of their educational journey.

For beginner Data Scientists:

Naked Statistics by Charles Wheelan — This is a fast and entertaining read that introduces you to the importance of statistics and uses real world examples of mathematical concepts. It is a great introductory book for people just starting in the data science realm!
Think Stats: Probability and Statistics for Programmers by Allen B. Downey- This is a great book for learning simple and useful Python techniques to explore real world data.
Data Science from Scratch by Joel Grus — this one is great for complete beginners. You don’t even need a background in Python for this book!

For Intermediate Data Scientists:

Python for Data Analysis by Wes McKinney- This is a book that shows you the ins and outs of standard Python libraries like NumPy or pandas.
Python Data Science Handbook by Jake VanderPlas- is an overall great guide that walks through the standard Python libraries that are used like NumPy, pandas, Scikit-learn, and Matplotlib.

For Advanced Data Scientists:

Deep Learning with Python by Francois Chollet- Francois Chollet is the creator of Keras, which is one of the most widely used and most popular machine learning libraries in Python. This book is a necessary read for Data Scientists who are looking to utilize Deep Learning.
Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville- a comprehensive textbook of Deep Learning written by three experts in the field.

4. Practice Technical Skills

One of the most intimidating parts about getting a job as a data scientist is going through a technical interview. This is the time to put in some time to brush up on the technical coding skills that are necessary for landing that new job! By just putting in half an hour to an hour of practice a day, you can improve your confidence and coding skills exponentially. Here are just a few resources to practice all different programming languages:

Hackerrank — is a place where programmers from all over the world come together to solve problems in a wide range of Computer Science domains such as algorithms, machine learning, or artificial intelligence, as well as to practice different programming paradigms like functional programming.
Exercism.io- this platform has 50 different coding languages to choose from! You can complete different coding challenges and upload your solution for mentors to review. It is a great way to learn new techniques and ideas.
Topcoder — is one of the first competitive coding platforms online. There are also articles and forums to help you learn new skills!
LeetCode- provides coding challenges you can solve directly. This platform also has a ‘Mock Interview’ section to prep you for your next job interview!

If you are preparing for technical interviews and have a bit more time, there are courses like ‘Data Science Interview Prep’ by Udacity that are free and available online.

5. Start a Side Project

In my opinion, there is no better way of improving your data science knowledge or skills than by completing side projects. My personal favorite is Kaggle. Kaggle is an online data science platform that hosts predictive modelling and analytics competitions for data scientists, data miners, and statisticians. Companies and researchers post datasets from all different fields and people compete for the best exploratory data analysis and predictive models. Kaggle is a great resource for learning about real world data. It also allows you to see other data scientists’ notebooks and how they approach their data! This is extremely helpful for people who may not have a background in this field and want insight to the thought processes behind the data analytics. It is also a community where people can give constructive criticisms or new ideas to create the best possible model.

Other data resources for starting your own project are:

FiveThirtyEight
UCI Machine Learning Repository
Data.gov
Google Public Datasets

These are just a few of the many resources that are publically available to data scientists. Just pick a dataset and get going! This is a great opportunity for you to build up a portfolio of data science skills and projects that you can talk about/showcase at your next interview! By competing in data science competitions or completing side projects on your own, it shows that you have the initiative and drive in your personal life to continually learn and improve.

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Thank you for taking the time to read my blog post!

I wish you the best of luck on your journey to continually improve yourself and I hope you go into tomorrow knowing that you have made yourself better than the day before!

Referenced in this blog post: