Subscribe to DSC Newsletter

Career Transition Towards Data Analytics & Science

Here is Rafael Knuth's story.

In 1992, I entered the job market and landed a job as an advertising copywriter for McDonald’s. I was tasked with ideating radio, TV and print advertisements to curb burger, fries and soft drink sales. The internet did not exist in the public domain back then, and my first laptop was actually a mechanical type writer. Around 2000, I became a freelance marketing manager, working for small and mid sized businesses. At that time, my English was not good enough to work for companies outside of my home country Germany (it’s still far from perfect).

Fast forward 10 years, I was still working as a marketing guy, yet after years of self-study, my English became profoundly workable. I managed to acquire some of the largest US based IT and software companies as my clients, and in 2013, I started teaching myself to code. Back then, I was increasingly worried that as a technology illiterate, I might be flushed out of the job market in a forseeable future.

At the moment of writing this post, I am bootstrapping a data literacy consultancy, catering to large enterprises around the globe. I teach business users how to work with Excel in ways they haven’t seen before. Plus, I teach them how to code and work with data in a utility scale environment. My learning journey was tough, but it can be smooth for any business leveraging on my experience.

My biggest fear of becoming jobless turned into the business opportunity of my lifetime.

T-Systems employees protesting against their employer’s decision to release 10,000 workers who don’t possess any coding skills. Source: Verdi | Markus Fring

10 observations I made during my own transition, which might propel yours

You might be tempted to say: “Nah, that’s not me. An ad guy turned consultant!” And you know what? You’re right! I‘m not you. Just take my observations and use them to craft your own, unique career transition story. Use my learnings to avoid unpleasant surprises and expensive mistakes. In 5 to 10 years, so I hope, we will have thousands of stories, and distinct career transition patterns will emerge.

The observations below are not sorted in order of importance, I rather arranged them for the sake of an easily digestible narrative. Let’s get started.

  • 1st observation: just learning to code will get you nowhere
  • 2nd observation: Excel is dead, long live Excel
  • 3rd observation: the more you share, the more you learn
  • 4th observation: citizen data scientists are coming, yet their scope is limited

Read full article with detailed explanations regarding each of the 10 observations, as well as what Rafael found to be the most useful things to learn (languages, statistical techniques, etc.) here

DSC Resources

Views: 114

Comment

You need to be a member of AnalyticBridge to add comments!

Join AnalyticBridge

Follow Us

On Data Science Central

On DataViz

On Hadoop

© 2018   AnalyticBridge.com is a subsidiary and dedicated channel of Data Science Central LLC   Powered by

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