How to Balance Work and Learn More About Data Science How to Balance Work and Learn More About Data Science
Data science is a very demanding profession because technology is advancing at such an accelerated rate. Data scientists always need to... How to Balance Work and Learn More About Data Science

Data science is a very demanding profession because technology is advancing at such an accelerated rate. Data scientists always need to be learning about new tools, languages, packages, updates, research results, etc. As a result, it’s difficult to do your 40-hour per week job and also keep learning and improving your skills.

How can you manage this need for constant self-improvement? How can you find time for yourself and your family outside of work? Can you convince your employer to give you time on the job to learn? These are all important questions, especially for people trying to transition into the field from other professions.

Before we get into a discussion of work balance, let’s start with a list of ways data scientists can keep current and continue to advance with their profession. If you have other tips, please leave a comment below!

How to Keep Up to Date

It’s important to keep yourself on a leading-edge path with respect to the rapidly evolving technology surrounding the field of data science. As a data scientist, here are some areas that I use to make sure I know what’s happening and what’s going to happen:

Continuing Education – as with professionals in other industries like healthcare, legal, financial services, etc., data scientists should always engage in continuing education. There are many online learning resources such as MOOCs (e.g. Coursera and edX) that offer attractive education options with specializations (certifications).

Webinars – choose a few of your favorite technology companies and register for some of their webinars that interest you. Good webinars are widespread these days so you should find an abundance of quality content. 

[Note: ODSC hosts webinars every few weeks. See the calendar for upcoming presentations.]

Conferences – pick a conference or two each year that includes technical sessions and tutorials to help you keep pace as technology changes. I enjoy attending conferences for this reason, as the sessions are compelling and presented by knowledgeable people.

Blogs – subscribe to a number of data science and machine learning blogs to keep up to date with the latest and greatest techniques. Blogs often email out summaries that allow you to review new content quickly.

Social Media – I get a lot of late-breaking data science industry news by following various industry luminaries on Twitter. I also connect with high-profile people on LinkedIn.

Peruse Stack Overflow – this site is a treasure trove of answers to your technical questions. You’ll find that Google offers a lot of Stack Overflow content in response to questions you may pose to the search engine.  

Google Alerts – this is a very useful Google service where you get daily e-mail summaries with links to articles aligned with a set of keywords you choose. I monitor “big data,” “data science,” “machine learning,” “artificial intelligence,” and “deep learning.” My daily summaries always contain a few jewels I would have otherwise missed.

Work Balance

OK, so now that I’ve reviewed how to keep out in front of the pack with data science, what steps can you take to maintain work “balance?” As a consultant in data science, I probably have more freedom in my schedule to reach some level of equilibrium. I do practice all of the above methods of keeping current, but I’m able to spread the effort broadly over a typical workday.

For others with more traditional full-time jobs, the process of achieving a balance may be more problematic, so let’s lay some ideas out on the table for how to optimize your time:

  • I heard on a recent webinar that industry luminary Andrew Ng carries around a file of recent research papers to consume while he’s riding around in Ubers (after hearing that tip I find myself doing the same).
[Related article: The Most Influential Data Science Research Papers for 2018]

  • You can use your smartphone during off-times (like riding on the train, at lunch, or waiting in line somewhere) to monitor your social media feeds, take in a webinar, and also check your Google alerts.
  • For learning, the online educational resources available these days allow you to learn at your own pace, whenever you’re able. I recently took several months to go through some new deep learning course content. I routinely spent a couple early evening hours watching lectures and doing assignments. It was a lot of fun and the process did not impact my work day at all.
  • You can set aside a few minutes each day to check your blog summaries. This could extend out to an hour or so if you find a particularly important article.
  • Use of Stack Overflow aligns more with when you have specific technical questions and how much time you have to drill down into discussions and follow links.


You may find that your own work balance will be based on how you weigh money versus free time. Here is a survey of 411 people along with an associated analysis of how people feel about this tradeoff, the results are summarized here:

  • Regardless of marital status, most people would prefer to work fewer hours and make less money.
  • People are much more willing to work longer hours if they are younger.
  • Men choose to work longer hours. While female job preferences stay the same after marriage, male preferences converge toward the middle

If money is your goal, then being very current with your data science skills will cost you in terms of your time. A work balance is also directly related to the type of company you work for. A large enterprise will likely have work balance policies in place, but if you choose a start-up then all bets are off – you will often give up nearly all your personal life for the company until the company is acquired or goes public. This is why most tech start-ups are filled with very young people who are able and willing to make that level of commitment.

What tips do you have to learn more about data science while keeping up with work? Share below!

Daniel Gutierrez, ODSC

Daniel D. Gutierrez is a practicing data scientist who’s been working with data long before the field came in vogue. As a technology journalist, he enjoys keeping a pulse on this fast-paced industry. Daniel is also an educator having taught data science, machine learning and R classes at the university level. He has authored four computer industry books on database and data science technology, including his most recent title, “Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R.” Daniel holds a BS in Mathematics and Computer Science from UCLA.