His professionals data science (data science) and machine learning (machine learning) adopt the programming language Python. However, data science and machine learning still lack basic tools in business and have growth margin before they become necessary for decision making, According to Anaconda.
Python could soon be the most popular programming language. But while Python adoption is booming, the fields that lead to it - data science and machine learning - are still in their first steps.
More than a third (37%) of the 4.999 data science professionals, students and academics who responded to web research of Anaconda in April to May, stated that their organizations reduced investment in data science, while 26% increased investment and 24% said the investment was fixed. It is not clear what impact the pandemic had in investments in data science tools and technology.
However, about 39% said that many of their business decisions are based on data science, while 35% said that only some business decisions are based on their team knowledge.
A quarter of respondents said that did not have the resources for effective analysis, another quarter said decision-makers in their organization struggle with data literacy, and 11% said they or their team could not have a business impact.
Only 36% described them decision makers of their organism as "Very informed" and really understood visualization and data models. Just over half (52%) stated that decision makers were "Mainly data connoisseurs".
Anaconda also asked respondents to identify all the skills they think are missing from their body. The top skill who reported missing was in "Big data management" to 38%, while 26% stated that their body is deprived advanced mathematics and a quarter reported that the "Business knowledge" was incomplete.
Other skills whose supply is insufficient are deep learning (27%), communication skills (22%), data visualization (22%), machine learning (21%), Python (20%) and probability and statistics data (19%).
The top problem that most scientists thought needed to be tackled in artificial intelligence and machine learning were the "Social effects of prejudice on data and models" (31%), followed by "Effects on individual privacy". Both of these issues have been highlighted by the adoption of artificial intelligence and face recognition in public surveillance systems.
Microsoft President Brad Smith recently called on the US government to introduce face recognition regulations due to racial prejudice.
Other Mr.Concerns included job losses from automation (19%), advanced information warfare (15%) and lack of diversity and integration into the profession (10%).
Only 10% of respondents said their organization had implemented a solution to ensure justice and alleviate prejudice, but Anaconda found that 30% planned to implement a step next year.
The explanation and interpretation of ML models it was another big gap. About 31% said their organization had no plans to provide explanation and interpretation, but 41% said the plans were able to implement some steps over the next 12 months or already have a step.
Most respondents (65%) said that their employers encouraged them to contribute to open source projects, but 18% of respondents said that employers' support for open source decreased due to COVID-19 or other factors.
Finally, about 41% said that security flaws in open source software was main obstacle faced by their organization in using open source software. Python and many of the popular data science and machine learning packages / libraries, such as NumPy and TensorFlow, are open source projects.
Source of information: zdnet.com