Top Data Science Skills In 2017: Identify Where to Work and the Skills to Land You There

In today’s article, I’m going to research some job listings at LinkedIn’s top 3 companies and then tell you what these listings reveal about the top data science skills in 2017. By identifying, learning, and then mastering these skills, you’ll be 1 step closer to landing the data science job of your dreams. Without further ado, let’s look at and consider these top data science skills in 2017.

LinkedIn’s Top Companies 2017

Just last week, LinkedIn published its Top Companies 2017 list that named (Google) Alphabet, Amazon, and Facebook as the top 3 companies at which to work in the entire world. Comparison metrics included job application rates, brand engagement rates across social, and employee retention rates. If you haven’t seen it, I’d highly recommend taking a look so that you get an idea of what characteristics earmark a great company. That way, when it comes time for you to consider making your next career move, you’ll know what to look for.

The Top Data Science Skills in 2017

No doubt, any data scientist that lands a job at one of these companies is a data scientist that’s 100% on their A-game. And although these individuals are usually 2 steps ahead of the average, it’s not impossible for you to catch up, depending on your background. Spoiler alert: skill-building will almost certainly be required.

Just to give you an idea of where I am getting my information (and to reassure you that I’m not just pulling it from a hat ?? ), I pulled my research from LinkedIn Jobs at Google’s Data Scientist jobs listing, Amazon’s Data Scientist jobs listing, and Facebook’s Data Scientist jobs listing. I looked at several position descriptions within each of these listing and noted the commonalities. These are as follows:

Top Data Science Skills in 2017

1.      Proficiency in Python (or R)

2.      Experience with SQL

3.      Data Modeling / Machine Learning Skills

Depending on the specific position description, preference also seems to be given to candidates who can do things like: generate data visualizations in D3.js, write MapReduce jobs, write ETL scripts, and code in C++.

(Just a quick LininkedIn Learning plug: you can pick up skills in Python and machine learning in my LinkedIn Learning for Python for Data Science Essentials. If you want to get up-to-speed in SQL, I recommend the LinkedIn Learning course SQL: Data Reporting and Analysis.)

Here’s the Takeaway

According to job adverts from the world’s leading employers, the top data science skills in 2017 are the same as they have always been. If you want to get a job in data science you need to:

1.      Learn to code in Python (or R)

2.      Know how to query and reformat data in SQL, and

3.      Know how to use machine learning methods to make predictions.

I, of course, am not too surprised by any of these skill requirements. They are industry standard. A word of warning though. Although these positions only request “experience with SQL”, if you’re going in for a job interview, you need to be a SQL all-star. SQL is sort of basic (and in my opinion, boring). It is super easy to learn and use. You often need it in data science, but it’s importance is generally understated. My point is, if you land an interview at a top-company, make sure that you are on your A-game for each of the position’s requested skills. Just having had basic “experience” with SQL will not be enough.

There you have it, now at least you know the top data science skills in 2017 that will help you land a job. Knowing what you need to do and doing it are 2 different things though. Sometimes we need encouragement and direction. What’s worse, there are lots of people that already have the skills they need, but they still can’t land a job. That’s because there is MUCH more to getting a great job than just knowing the tools and skills a job requires. I have learned and mastered the secrets to landing a great job, so much so that I can work for myself now, and don’t have to rely on any one company to support me. For more about how I did this, and how I can help you get ahead in your own data science career, please see my About page.

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