Most In Demand Data Science Skills – The field of data science is growing rapidly and data scientists are in high demand. If you want to enter this field, you need to have the right skills. In this blog post, we’ll explore the skills most in demand by data scientist employers and how you can develop these skills and get a job as a data scientist.
Data science is an ever-evolving field, and data scientists are constantly learning new skills. The most in-demand data science skills for data scientists include knowledge of cloud services, data storage options, business domain knowledge, software engineering, and MLOps skills—some of the top skills you can focus on developing to stay competitive. Industry. Feel free to send us your questions or comments.
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Most In Demand Data Science Skills
Ajitesh Kumar I am recently working in the field of Data Analytics in Data Science and Machine Learning / Deep Learning. I am interested in Java/JEE, JavaScript, Python, R, Julia, and programming languages like blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. inter alia. Follow us on Twitter for the latest updates and blogs. I’d like to connect with you on LinkedIn. Check out my latest book Thinking First Principles: Building Winning Products Thinking First Principles. Check out my other blog Revive-n-Thrive.com and by the end of this article you will know which technologies are becoming popular with employers.
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In the year I analyzed the most in-demand skills and technologies for data scientists at the end of 2018. That article resonated with people. It has over 11,000 likes on Medium, has been translated into multiple languages, and was the most popular story on KD Nuggets in November 2018.
In the year In an original 2018 article, I explored the need for general skills such as statistics and communication. I’ve also seen interest in technologies like Python and R. Software technologies must change faster than general knowledge, so I only include technologies in this updated analysis.
I searched Simply Hired, Theed, Monster, and LinkedIn to see which keywords appeared with “data scientist” in job listings in the United States. This time I decided to write code to delete job lists instead of searching manually. This effort paid off for Simply Hired, of course and a monster. I was able to use queries and the beautiful Soup Python library. You can find the scraping and testing code for Jupyter Notebook in my GitHub repo.
Scraping LinkedIn proved to be more difficult. Verification is required to view the correct number of job listings. I decided to use Selenium for headless browsing. In September 2019, the United States Supreme Court ruled against LinkedIn and allowed LinkedIn to delete the data. However, after several attempts I was unable to access my account. This problem may stem from a proportional limitation. ???? Update: I’m back now, but I’m worried I’ll get locked out if I try to scratch again.
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For what it’s worth, Microsoft owns LinkedIn, Randstad Holdings owns Monster, and Recruit Holdings owns Real & Easy Recruit.
LinkedIn data may not provide an apples-to-apples comparison from last year to this year. This summer, I noticed that LinkedIn fluctuated significantly from week to week for some technical job search terms. I assume they tested their interest score algorithm using natural language processing to measure intent. In contrast, relatively similar job listings for ‘data scientist’ appeared for the other three search sites over the two years.
For each job search website, I calculated the percentage of that site’s total data scientist job listings where each keyword appeared. I then averaged that percentage for each keyword across the three sites.
I manually searched for new search terms and deleted promising ones. In the year In 2019, no new contracts reached an average of five percent of listings, the cutoff I used to include in the results below.
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Let’s look at the first three options with bar charts. Then I will show a table with the data and discuss the results.
Here’s the chart #2 above showing the average percentage of gains and losses on listings between 2018 and 2019. AWS shows a 5% point increase. It appeared in an average of 19.4% of lists in 2019 and an average of 14.6% of lists in 2018.
Here’s the chart for number 3 above, showing the percentage change over the year. Compared to the average percentage of listings viewed in 2018, PeaTorch grew by 108.1%.
The tables are all made using Plot. Check out my guide on how to use Plot to create interactive visualizations. If you want to see the interactive charts, check out the HTML file in my GitHub repo. Plus a Jupiter notebook for scraping, analyzing and viewing.
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The data in the charts below are arranged in tabular format by the average percentage change in listings from 2018 to 2019.
I know these different steps can be confusing, so here’s a guide to what to look for in the chart above.
Pt is still on top. This is the most common keyword. It is in three out of four. Python has made great progress compared to 2018.
SQL is up. Passed R for the second highest grade point average. If the trend continues, it will soon be number two.
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The most popular deep learning frameworks have grown in popularity. PeaTorch had the highest percentage increase of any keyword. Keras and TensorFlow have made huge gains. Keras and PyTorch moved up four places in the rankings, while TensorFlow moved up three places. PyTorch starts with a small average – TensorFlow’s average is still twice that of PyTorch.
Cloud platform expertise is in increasing demand for data scientists. AWS appeared in about 20% of the listings and Azure in about 10%. Azure moved up four places in the ranking.
R had the largest overall average discount. This finding is not surprising given the findings of other surveys. Python has clearly overtaken R as the language of choice for data science. However, R remains the most popular, appearing in about 55% of listings. If you know R, don’t despair, but if you need more demanding skills, consider learning Python.
Several Apache products have fallen in popularity, including Pig, Hive, Hadoop, and Spark. Pig dropped five places in the rankings, more than any other technology. Spark and Hadoop are still in-demand skills, but my findings show a trend toward them and other big data technologies.
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Proprietary statistical software packages such as MATLAB (… – – “” Software Package – S .–. MATLAB dropped four places in the rankings, and SAS fell from sixth to eighth most common. Both languages recorded large percentage declines compared to the 2018 average.
There are many technologies in this list. ???? You certainly don’t need to know them all. A mythical data scientist is called a unicorn for a reason. ????
If you’re starting out in data science, I’d suggest you focus on in-demand and emerging technologies.
That’s my general study guide. Adjust it to suit your needs or ignore it and do what you want! ????
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I hope you find this guide to the most in-demand technologies for data scientists useful. If you do, share it on your favorite social media so others can find it. ????
I write about Python, Docker, data science and other technical topics. If any of this interests you, follow me and read more here.
Biography: Jeff Hale is a data scientist and author of Unforgettable Python ???? And the unforgettable Docker ???? and e-commerce COO. The marketplace has changed dramatically over the last 10+ years. Every decade has its most important talent. 20-30 years ago, engineers were the hottest products in the job market, then accountants – today data scientists.
Companies compete on statistics, better statistics get more customers. The importance of extracting valuable information from data has never been greater. Organizations realize that they need skilled professionals who can provide better insights and thereby increase the company’s competitiveness. But what are the most needed skills?
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Statistics, predictive modeling, model deployment, machine learning, deep learning, data wrangling, data visualization, multivariate calculus and algebra, programming/coding, business intelligence, problem solving, and curiosity.
That’s a lot to ask. You can switch between Python and R in your sleep, sprinkle in some JavaScript without waking your classmate, but lack basic knowledge of statistical principles. Or you might be a statistician who cooks breakfast according to Bayesian principles, but lacks common-sense business acumen. Or the Harvard Business Review is ready to publish your case study, but you don’t understand what the term ‘second premise’ means (yikes!).
The good news is, it’s rare to expect to have all of these skills ready when you walk in the door. After all, the concept of ‘data science’ is an increasingly fragmented field. In fact, there are only a few ‘must have’ skills.
We work in the industry
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