Subscribe to DSC Newsletter

Continuing along the path of Data Science

You like working with data. You’ve completed a few data science courses and enjoyed them. Now what?

I second Don VanDemark’s enthusiasm for course sequences, specialization tracks, and certification offerings. Whether through traditional brick-and-mortar schools, on-line offerings, or bootcamps, the carefully-planned curricula and (depending on the program) personal instruction can be very helpful.

Here are several additional recommendations from Month 15 on the path…

  1. Develop solid expertise in at least one programming language that offers good analytic support. (I chose Python.) Gain basic fluency in SQL. Even though the data scientist role is different than that of a software engineer, writing code and managing data are basic skills for your toolbox.

  2. The field of “data engineering” is emerging in parallel with data science.  Even if you do not plan to work directly with data infrastructure such as Hadoop, you’ll probably interact with data engineers about data collection, access, and storage.  Spend a bit of time learning about their work, tools, perspectives, and challenges. You’ll be better able to work with them when your roles intersect.

  3. Go to tech meetings. Meetup.com lists events where you can meet others with similar interests and learn how various organizations use data. Professional organizations ACM and IEEE host local data science-related meetings and workshops.  If there’s a hacker space in your area, check with them about workshops and community activities.  (If there aren’t many tech meetings in your area, note that some groups - such as The Hive in Silicon Valley and local ACM groups such as SF-Bay-ACM - post videos and slides from some of their events.)

  4. Practice.  There’s a gap between MOOC coursework and real world data.  To start addressing it, complete a small project. Explore platforms like kaggle.com and coursolve.org.  If you’re a college student an internship could be a great next step. If you work for an organization that uses data for business decisions, there might be an opportunity to try new skills there.

  5. As you learn and progress, develop clarity about the unique qualities and skills you bring to an organization: past experience and new knowledge.

  6. Independent coursework lacks a clear completion point.  If you plan to seek work as a data scientist it can be hard to tell when and where to apply for jobs.  This is when it seems especially helpful to talk with others:  friends and coworkers; people at meetups and study groups.

We’re learning, organizations are evolving; technology is evolving.  Exciting times!

Views: 1478

Tags: MOOC, bootcamp, data, education, science, scientist, training

Comment

You need to be a member of Data Science Central to add comments!

Join Data Science Central

© 2019   Data Science Central ®   Powered by

Badges  |  Report an Issue  |  Privacy Policy  |  Terms of Service