Data science and machine learning professionals have driven adoption of the Python programming language, but data science and machine learning are still lacking key tools in business and has room to grow before becoming essential for decision-making, according to Anaconda, the maker of a data science distribution of Python.

Python could soon be the most popular programming language, battling it out for top spot with JavaScript, Java and C, depending on which language ranking you look at. But while Python adoption is booming, the fields that are driving it — data science and machine learning — are still in their infancy.

Most respondents (63%) said they used Python frequently or always while 71% of educators said they’re teaching machine learning and data science with Python, which has become popular because of its ease of use and easy learning curve. An impressive 88% of students said they were being taught Python in preparation to enter the data science/machine learning field.

Given Anaconda’s audience, it’s not surprising Python was by far the most popular language used. It was followed by SQL, R, JavaScript, HTML/CSS, Java, Bash/Shell, C/C++, C·, Typescript, PHP, Rust, Julia, and Go.

Over a third (37%) of 4,299 data science professionals, students and academics who responded to Anaconda’s online survey this April to May said their organizations decreased investments in data science, while 26% increased their investment and 24% said investments were flat. It’s not clear what impact the pandemic has had on investments in data science tools and technology.

Still, some 39% said reported that “many” of their business decisions rely on data science, while 35% said only some business decisions were based on insights from their team.

A quarter of respondents said they lacked the resources for effective analysis, while another quarter said decision-makers at their organization struggle with data literacy, and 11% said they or their team couldn’t demonstrate a business impact.

Only 36% described their organization’s decision-makers as “very data literate” and actually understood data visualization and models. Just over half (52%) said decision-makers were “mostly data literate”.

Anaconda also asked respondents to nominate all the skills they believe their organization were currently lacking. The top missing…

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