How to DIY Your Data Science Education

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If you want to be a data science professional but aren't eager to take the traditional route of signing up for a program offered through a university and buckling down to do it, that's no problem.

Although some people find the path of formal education works well for them, you can take a DIY approach instead. Here are eight ways to do that:

1. Teach Yourself Some of the Most Necessary Programming Languages

 
Working as a data scientist means you'll need to know some programming languages. There are numerous ones you should have under your belt to ensure you're a well-rounded professional. If you want a solid starting point, statistics show that a growing number of job postings seeking data scientists request that the respondents know Python.

Fortunately, you can go through a free Python tutorial offered by Analytics Vidhya. After doing that and feeling like you've gained a thorough understanding of it, consider learning R. It's another programming language often used in data science. Swirl offers a step-by-step process for people who want to learn R. You download an interactive console and take the course inside it.

2. Listen to Relevant Podcasts

 
Podcasts cater to people who want to be educated and entertained while they do things like work out, commute to their offices or make dinner. They can also supplement your data science education and make it more fun by breaking up some of the monotony you may feel while learning on your own.
Data Skeptic is worth a listen because it makes high-level concepts more manageable by presenting them in bite-sized segments. The content also covers how data science applies to the real world, which could give you some inspiration about future career choices.

You can also try Digital Analytics Power Hour, which features friends who live around the world virtually meeting up to discuss data science. Don't let its informal tone fool you into thinking you're not learning things you can use. Each episode aims to provide takeaways you can independently explore more as you learn.

3. Develop Your Data Science Library

 
If you don't own any or many data science books, now is the time to invest in some and start creating a library of titles you can refer to as you learn. Try "Data Science for Business" to learn how to extract insights from information. That book is part of the data science curriculum at more than 150 higher learning institutions.

"Getting Started With Data Analytics" is another title that helps you build a valuable foundation in the subject, especially as a self-taught person. Beyond these two books, think about making a list of the topics you encounter that interest and fascinate you. Then, search for books that mention those things.

4. Enroll in an Online Data Science Course

 
One of the great things about the internet is that it offers people countless opportunities to learn at a speed that suits them. You can sign up for a data science course and work through it in a way that matches your schedule. For example, Udemy has a data science bootcamp course. It's a paid option, but the class claims to give you "the entire toolbox you'll need to become a data scientist" and says you don't need prior experience.

Alternatively, the Data Science Foundations learning path from CognitiveClass.ai offers content you can access at any time, plus provides a virtual lab where you can practice concepts learned. These are just two of the numerous possibilities that specifically relate to data science. It's also worthwhile to explore similar topics in an online course, such as machine learning or business analytics.

5. Plan to See Technology Experts Speak in Person

 
Today's leading tech experts can explain how data science applies to other fields. For example, robots learned to navigate obstacles and adjust to real-time environmental changes in the environment thanks to improvements in the data used to train them.

Going to a conference and getting perspectives from experts can give your DIY education momentum for helping you feel inspired. The Data Science Conference is happening in Belgrade, Serbia, in November 2019, and it will have more than 50 speakers. There's also the Data Summit 2019, occurring in May in Boston.

Plan to clear your calendar now to attend at least one data science event. You'll likely be surprised by how much you learn in such a short time. Plus, you'll have a deeper understanding of how data science applies to other fields, letting you keep up with trends and developments.

6. Look to YouTube

 
YouTube is an excellent resource for furthering your data science studies. Be careful which videos you choose, especially since some of them are merely ads for courses and don't give you the learning content. However, you still have plenty of possibilities to watch.

Consider the Intro to Data Science — Crash Course for Beginners from freeCodeCamp.org when you're just getting started. There's also a semester-long free course from MIT called Introduction to Computational Thinking and Data Science. Once you grasp the introductory material, branch out into more advanced topics.

It's always a good idea to read the reviews from fellow viewers before getting started with the content. The comments they give could help you determine the overall quality of the course and whether it will meet your expectations.

7. Meet Other Learners and Data Scientists Locally

 
Investigate to find out if your community has regular meetings of data scientists and learners. The MeetUp website is a fantastic place to start that could reveal new opportunities to you. For example, New York offers various meetups, including groups for female data scientists. The options in San Francisco are similarly diverse, and there are some groups for data science beginners.

No matter where you live, use MeetUp or a similar site to figure out how you can meet data scientists face to face. You should enjoy benefits like those experienced after going to a conference but on a more regular basis.

8. Work on Data Science Projects

 
You may choose to go through the sections of this list in any order. At some point, though, you need to put all the learning into practice. The best way to do that is to get engrossed in a data science project. Springboard.com provides several to get you started. Then, once you become more experienced, come up with questions you want to solve and create projects with free data sets.

Anticipate a Bright Future

 
Data science is a career with huge potential, and the people who understand it at the professional level are in continual demand. The options on this list demonstrate that you can begin — and even complete — your learning without enrolling in a formal degree program.

Taking that approach requires discipline, but it could also give you the skills needed to accept and feel equipped for future jobs in the data science industry.

 
Bio: Kayla Matthews discusses technology and big data on publications like The Week, The Data Center Journal and VentureBeat, and has been writing for more than five years. To read more posts from Kayla, subscribe to her blog Productivity Bytes.

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