I wrote a post for Inside Big Data on transitioning into Data Science — a topic I’m actually qualified to give advice on!
It’s a familiar dilemma. You’ve done your research, read some books, taken some online classes – and at long last, you’re finally ready to get real-life work experience as a Data Scientist.
But as you browse job postings, you become discouraged: “They want me to be a d3 Expert and a Deep Learning ‘Ninja’? A ‘Wizard’ of ETL and a tidyverse-loving #Rstats ‘Samurai’? What does it even mean to be a Viking of scikit-learn by ascending to the Valhalla of XGBoost? Is that, like, two years of work experience? Three?”
As you add a few more classes to your ever-expanding study plan, you let out a sigh: “Maybe Data Science can be my second career when I retire.”
While I believe that lifelong learning is the universe’s most powerful force for good, it’s easy to overdo. If you’ve developed a solid foundation, you may be ready to look for your first job – even if your resume doesn’t perfectly match the requirements for your dream position. This is particularly true in Data Science, where no single human being will ever be qualified for your average entry-level job posting.