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What skills do data scientists and analysts need?

What skills do data scientists and analysts need?

Published on 20-05-2019
As a program manager for data science at Propulsion Academy, I am often asked the following question: Which of the two programming languages, Python or R, should a budding data scientist learn? I had the luxury of learning and developing in both programming languages. During my research career, I have mainly focused on R. When I worked as a data science consultant, I did most of the implementations in Python.

In my opinion, anyone asking this question is new to data science and is confused by an abundance of online resources and the never-ending war between statisticians and non-statisticians (that includes engineers, computer scientists, and physicists) .

Brian Ray did a very nice comparison of the two in terms of ease of use and performance on his blog Python vs. (and) R for Data Science. In this blog I am trying to help people resolve their confusion by understanding the job market (what we all want is a job in this field) in Switzerland.

In order to answer the question of what skills data scientists should have, Propulsion chose a data science approach and scanned jobs.ch for the key words data scientist and data analyst. The result was 425 job postings (75% in English, 23% in German and 2% in French, as of April 2019). For data scientist job postings, 27% asked for R and 31.5% asked for Python, while for data analyst 24% of job postings asked for Python and 22% for R. With this data, learning Python seems cheaper than R, but at Propulsion we introduce both programming languages.

If I had to give an opinion on learning a programming language, I would say start with one (any) and then learn the second as well. For quick exploratory analysis, testing, and time series analysis, I think R is the better option, but Python certainly has an advantage for creating operational systems.

We have also observed that many employers have asked for skills in SQL and machine learning. This suggests that job seekers should try to develop these skills over time as well.

Aside from the hard skills mentioned above, our students have also explored other skills required for data science positions. Given their nature, it is not surprising that analytical skills are emerging as one of the most important soft skills. However, communication is still at the forefront of the soft skills employers ask for. I am often asked by outsiders to find candidates with a technical talent in data science, but also with great communication skills and the ability to understand the business. At Propulsion, we teach business acumen by offering an industrial data science capstone project to every student. During the project, the students work with business partners to solve a real business data science case.

Another important word that appears in our analysis is "team". Since daily tasks revolve around working with various other roles such as engineers, product managers, marketing, and others, teamwork is also an indispensable skill and should not be neglected by aspiring data scientists and analysts.

I would like to thank Phoenix Jieh and Nadia Chyalak for providing the numbers.

Are you interested in continuing this discussion? Contact us at [email protected]