The State of Data Science Fall 2019

Mikolaj Wilk
Posted on Dec 9, 2019

Over the past decade, data science has emerged as an important and rapidly expanding field. Data Scientist has been the #1 job on Glassdoor’s “50 best jobs in America” for each of the last 4 years. Demand for Data Scientists has certainly increased over that span and is anticipated to grow into the future. While the field is growing quickly, the adoption of data science techniques is, in fact, moving surprisingly slowly. Even while businesses increasingly say they aspire to be  “data-driven,” they fail to take the necessary steps to achieve that goal.  

The NewVantage Partners’ 2019 Big Data and AI Executive Survey indicated that only 31% of companies say they are data-driven. Moreover, respondents indicated that the principal challenge to becoming data-driven was people (62.5%) and process (30%), not so much technology (7.5%). Part of this challenge in people and process is transforming the current culture. One vocal critic of the slow adoption of data science in business has been Peter Wang, the CEO of Anaconda:

 

Source: https://twitter.com/pwang/status/1172239543732506624

 

According to GeekWire, Tableau had about 86,000 customer accounts, as of the end of 2018. In contrast, Anaconda, on its website lists that it has about 18 million users worldwide! This wide gap suggests there is a huge, as yet untapped business opportunity for data science in business, as Peter Wang alludes to and which the NewVantage Partners’ 2019 Big Data and AI Executive Survey confirms. Therefore, the adoption of data science techniques and tools in business has a long way to go.

What positions are companies currently hiring for and what skills are they looking for? 

After building a Python web scraper using the Selenium library to retrieve all the job postings on Indeed.com within 25 miles of New York, NY, here’s what I found after analyzing 7,319 unique postings for search terms: “Data Scientist,” “Data Engineer,” “Software Engineer,” “Python,” “Data Analyst," and “Machine Learning Engineer.”

These were the results:

  1. Analyst: 1,338
  2. Software Engineer: 1,331
  3. Data Scientist: 722
  4. Data Engineer: 460 
  5. Data Analyst: 427
  6. Machine Learning Engineer: 125 

The greatest majority of related job listings have "engineer" or "analyst" in the title; however, these job listings do not necessarily require data science specific skills. Data Scientist is actually third among the list of related jobs. 

Top Skills: 

  1. Machine Learning: 1,479
  2. Python: 1,403
  3. Analytics: 682
  4. R: 434 
  5. SQL: 431
  6. Statistics: 371

Although programming in R does make the list of required skills for data science roles, it appears as a supplementary skill alongside Python. Another important distinction is that while machine learning is one of the top skills being asked for, deep learning (96) and “artificial intelligence” (217) were not as common, since these are much more specialized skills. 

In addition to the technical skills above, perhaps even more important, are communication skills. Being able to summarize and articulate business insights and explain various data science methods and problems to colleagues and business executives is vital to being able to provide business value. This is the missing element of so many companies that are not yet data-driven.

As digital transformation of business continues and more businesses become increasingly data-driven, data science tools and techniques will become an indispensable part of every company. With so much room still left to grow, now is a great time to become a data scientist! 

Check out the web scraping code: gitlab.com/mwilk11/indeed-web-scraper

About Author

Mikolaj Wilk

Mikolaj Wilk

Data Scientist, Developer, Scholar and Scoutmaster with a background in Physics, determined to unlock business value and solve problems with analytical and data-driven methods.
View all posts by Mikolaj Wilk >

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