January Edition: Getting Started in Data Science

Starting off the new year with a warm, gentle welcome to all new and aspiring data scientists

Jan 3, 2022

5 min read

👁 Photo by Towfiqu barbhuiya on Unsplash

MONTHLY EDITION

We love deep dives and cutting-edge research here on TDS, but we’ll always have a soft spot for articles that guide beginners in the early stages of their data science journey. Now that 2022 is here, we wanted to welcome our community back and wish everyone a year of learning and growth— especially for those of you who have just recently joined the field, or are thinking about a switch to data science in the coming year.

One of the toughest aspects of starting something from scratch (or just about) is the dizzying number of choices one has to make. We want to help, so for our January Edition, we compiled some of our best resources targeted at aspiring and new data scientists. There’s lots more in our archives—coming up with this selection was itself a process full of difficult choices!—but we all have to start somewhere.

Reference and general resources

From structured courses and curriculums to advice on how to make the first move, these posts are a great place to start if you’re feeling overwhelmed and need the gentle guidance of more experienced data scientists.

How to Grow from Non-Coder to Data Scientist in 6 Months by Sharan Kumar Ravindran (10 minutes) A complete guide with all required resources.
5 Tips to Boost Your Data Science Learning by Julia Nikulski (9 minutes) Increase your learning curve by focusing on these 5 things instead of taking more online courses.
Want To Become A Data Scientist In 12 Weeks? by Rashida Nasrin Sucky (5 minutes) Think one more time before you spend your money.
A Complete 52 Week Course to Become a Data Scientist in 2021 by Terence Shin (10 minutes) It’s 2022 now, but this handy resource from last year is still just as valuable.
A Checklist to Track Your Data Science Progress by Pascal Janetzky (14 minutes) Use the one-day-per-week principle to gradually tick it.

Personal accounts and experiences

Sometimes, the most helpful insights come simply from listening to others who have gone through the same experience or process as the one you’re currently navigating. Here are a few personal reflections that might give you just the right dose of inspiration.

How I would Learn Python for Data Science if I Had to Start Over by Nicholas (7 minutes) Including three tips to help you avoid making the same mistakes.
My Personal Story on Transitioning from Art to Tech by Jesse Ruiz (she/they) (4 minutes) A case for attending coding boot camps.
For New Data Scientists, Domain Knowledge Is Sometimes More Important than Technical Skills by TDS Editors Sophia Yang talks about the importance of ongoing learning and finding great colleagues and mentors.
Transitioning from Social Science to Data Science by Danny Kim, PhD (13 minutes) What you know and what you should know.
"How’d you get started with machine learning and data science?" by Daniel Bourke (10 minutes) A journey that started in 2017, on a friend’s lounge room floor.

Zooming in on specific topics, one step at a time

Once you get going and make some initial progress, you might feel like there’s too much to learn about too many things. The posts below offer an accessible, beginner-friendly gateway to key areas for data scientists, whether you’d like to deepen your knowledge around Big Topics (programming, statistics) or more focused ones.

A Complete 26 Week Course to Learn Python for Data Science in 2022 by Frank Andrade (6 minutes) Learn most of the Python stuff you need for data science in 26 weeks.
8 Fundamental Statistical Concepts for Data Science by Rebecca Vickery (8 minutes) Statistics basics, explained in plain English.
5 Hyperparameter Optimization Methods Every Data Scientist Should Use by Amal Hasni (8 minutes) Learn about grid search, successive halving, Bayesian grid search, and more.
Getting Started in Data Science Means Cleaning Up Your Data "Act" First by Brandon Cosley (10 minutes) An Exploratory Data Analysis (EDA) workflow for reducing clutter in data.
An Easy Beginner’s Guide to Git, Part 1 by Julia Kho (12 minutes) Learn the basics of Git in one short session.
How to Master Pandas for Data Science by Chanin Nantasenamat (12 minutes) All the essentials you’ll need to use this popular Python library.

There’s always so much more to discover and explore, of course -if you’d like to check out more of our curated, topic-specific lists, head over to our Learn on Towards Data Science page.

Before we leave you today, one last thing we wanted to do is greet all the authors who shared their work on TDS for the first time in the closing weeks of 2021. (If contributing to our publication is on your to-do list for this year, we look forward to reading it!) They include Jacques Carstens, Vaughn Spurrier, Syed Saad Hussain, Naomi Kriger, Sahela Jones, Murtaza Ali, Emanuele Fabbiani, Andrew Nhu, Frank Corrigan, Paul Froehling, Alexander Biryukov, ichen, Jon Simon, Giansalvo Cirrincione, Aleksandr Perevalov, Thomas van Dongen, Anthony Cros, Halil Duygulu, Moshe Wasserblat, Junkai Ong, Dashiell Young-Saver, Zach Blumenfeld, Philip Singer, Alfonso Santacruz Garcia, Annie Sinclair, Sean Easter, Samantha Nasti, Harmke Alkemade, Liu Peng, Ahmed A. A. Elhag, john aiken, Brianne Boldrin, David Wiesenfeld, and many more. Happy 2022, everyone!