{"id":999882114,"date":"2018-03-27T13:12:00","date_gmt":"2018-03-27T13:12:00","guid":{"rendered":"https:\/\/dice.npgdev.com\/?p=715785212"},"modified":"2025-03-18T15:29:01","modified_gmt":"2025-03-18T15:29:01","slug":"recruiting-in-the-data-science-space-get-to-know-python","status":"publish","type":"post","link":"https:\/\/dice.npgdev.com\/hiring\/recruitment\/recruiting-in-the-data-science-space-get-to-know-python","title":{"rendered":"Recruiting in the Data Science Space? Get to Know Python."},"content":{"rendered":"

If you\u2019re helping tech firms recruit data analysts and data scientists, as well as artificial intelligence (A.I.) experts, you may know that these highly specialized tech pros rely on a small handful of well-known languages to develop machine-learning (ML) apps for various industries. Even if you\u2019re on top of that, it pays to know that a subtle change may be coming: the Python ecosystem could eventually overtake R as the platform of choice for data analytics and machine learning. As more and more tech pros get involved in data science, they might be turning to Python precisely because its syntax is easy to understand, and it\u2019s a language that many people know, in contrast to the highly specialized R. Python is a general-purpose programming language used for a variety of purposes, with an emphasis on utility and readability, while R is a programming language favored by data scientists and analysts who are building statistical computing and graphics programs. A recent KDnuggets poll<\/a> of tech pros who use both R and Python showed that, over the past two years, there\u2019s been a slow decline in R usage in favor of Python. Meanwhile, a separate survey<\/a> from Burtch Works revealed that Python use among analytics professionals grew from 53 percent to 69 percent over that same time two-year period, while the R user-base shrunk by nearly a third. Are these user samples valid? If you\u2019re recruiting pros skilled in machine learning and data analytics, should you invest the time in learning more about Python? \u201cBroadly speaking,\u201d said Greg Ambrose, CEO of Stack Talent, a technology search and recruitment company headquartered in Chicago, \u201cwhy one would gain traction over another is, to some degree, a function of philosophy and preference.\u201d As Ambrose also noted, his computer science candidates nearly all gravitate toward flexible, broader-use Python, while candidates with traditional data science and statistics backgrounds focus more on R. And while he hasn\u2019t seen any substantial shift in hiring, he has noticed that more hiring managers seeking to fill statistics roles are interested in candidates with Python experience. (Companies are currently continuing to leverage both languages, he added.) \u201cCombining R and Python is both reasonable and feasible,\u201d agreed Enriko Aryanto, the CTO and a co-founder of the Redwood City, Calif.-based QuanticMind, a data platform for intelligent marketing. \u201cWe run them both in our data science platform internally. But if I were starting my career all over again today, I might consider focusing on Python rather than R. It\u2019s a more-general language with broader applications.\u201d R\u2019s limitations are potentially giving Python the edge in the data science and machine learning space. \u201cR has issues with scalability,\u201d Aryanto said. \u201cIt\u2019s a single-threaded language that runs in RAM, so it\u2019s memory-constrained, while Python has full support for multi-threading and doesn\u2019t have memory issues. When choosing a language, it all comes down to choosing what\u2019s best to solve your problem.\u201d That said, Aryanto\u2019s team uses R in their day-to-day data science and machine learning tasks. R is also firmly entrenched in academic programs, and Aryanto surmises that there will be statistics majors training in it for years to come. It\u2019s worth mentioning that, despite Python\u2019s current ascendancy in the space, it will likely end up eclipsed by some other language or platform. That\u2019s just how technology works; it wasn\u2019t that long ago that SAS was in wide use, only to have R replace it as a cost-effective (and open-source) competitor. But there may come a day when it\u2019s no longer critical for tech pros in the analytics and machine learning space to know R. Two of Aryanto\u2019s current team members don\u2019t even have a background in it. \u201cIn a few years, could R gradually be de-emphasized in favor of Python and eventually fall off the map for r\u00e9sum\u00e9s? Yes, I guess it could,\u201d he said. \u201cBut for me, the most important thing is finding someone who\u2019s smart enough to learn new languages to solve new problems.\u201d Python may seem very hot right now, he cautioned, \u201cBut in ten years that might not be the case. The people we hire have to be able to adjust and adapt to change. Ultimately, it goes back to solving our most important problems, whatever they may be, in the quickest, most scalable and easiest way.\u201d All of which underscores a last point: when it comes to recruiting tech pros, pay attention to their ingenuity and problem-solving abilities, which could prove more useful (not to mention durable) than any specific skill.<\/p>\n","protected":false},"excerpt":{"rendered":"

If you\u2019re helping tech firms recruit data analysts and data scientists, as well as artificial intelligence (A.I.) experts, you may know that these highly specialized tech pros rely on a small handful of well-known languages to develop machine-learning (ML) apps for various industries. Even if you\u2019re on top of that, it pays to know that […]<\/p>\n","protected":false},"author":132,"featured_media":999882257,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[2155,2154],"class_list":["post-999882114","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-hiring-candidates","category-sourcing-candidates"],"acf":[],"yoast_head":"\nRecruiting in the Data Science Space? 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