Why Python is outpacing R and SQL in data science

Why Python is outpacing R and SQL in data science

Data scientists are adopting specialized skillsets and programming languages to differentiate themselves in a growing field, according to Harnham.

With more professionals entering the field, the data science industry is moving from traditional “core” data scientists toward those with more specialized skillsets, the report noted. And in an active market, candidates are often able to be selective about the jobs they take. Data scientists move between roles more quickly than professionals in any other part of the tech industry, averaging less than two years in each position, the report found.

When it comes to tools, a common debate in the data science realm is whether or not Python or R is a better programming language for data work. While both languages have pros and cons, the Harnham report chooses a winner, at least in terms of popularity: Python.

  1. Python
  2. R
  3. TensorFlow
  4. Amazon Web Services (AWS)
  5. SQL

Python also outpaced R and SQL, when it comes to the data and analytics industry as a whole, the report found.

“We’re seeing a rise in what businesses are expecting from prospective employees, with the ability to contribute in more than one way becoming a near-necessity,” Sam Brown, managing consultant at Harnham, wrote in the report. “This could be the ability to code in more than one language, experience in converting research into production code or simply a degree of commercial acumen. There has also been a sharp rise in demand for Python-based Deep Learning experience, so familiarity with tools like TensorFlow, Caffe and Torch is increasingly more attractive to Hiring Managers.”

Python is more elegant than R, and wins out in terms of machine learning work, language unity, and linked data structures, according to a post comparing the two languages from Norm Matloff, a professor of computer science at the University of California Davis. However, R offers several advantages as well, including an easier learning curve, statistical correctness, and object orientation, Matloff wrote. Ultimately, both languages are useful tools for data science work.

Source: techrepublic