According to a recent report by Analytics India Magazine, the most preferred Data Science programming language used across organisations is Python, with 53.3% of the respondents utilising the language. Other languages that follow are R, Matlab, SAS, Scala, Java and more.
The report titled Analytics Platforms and Tools (PlaTo) Survey was conducted to understand the stack of platforms and tools adopted by leading Analytics, AI, & Data Science organisations. It included surveys across a wide range of platforms and tools including open source and commercial analytics platforms.
The survey was sent across the data science community to understand the adoption and usage of various Cloud Service providers, BI tools, Data Science platforms, AI frameworks, DevOp tools, distributed ML platforms, AutoML tools, Data Lake tools, and more. Respondents included a large spectrum of occupations and vocations including students, research scholars, entrepreneurs and senior professionals from various industries such as Domestic IT, BFSI, FMCG, Fintech, Fashion & Apparel and more.
The report suggests that the most preferred Cloud Service Provider is AWS with close to 50% of respondents using the service. Some of the other popular CSP are MS Azure, Google Cloud Platform which are used by 21.9% and 11.2% of the respondents respectively.
In terms of Database tools, MySQL was found to be the most preferred tool with 26.1% of the respondents using it. This is followed by Hadoop, BigQuery, Amazon Redshift and NoSQL. Whereas the most preferred Data Lake tool is AWS Lake Formation with 14.1% of the respondents utilising this tool. This is followed by Cloudera, Teradata, ADLS and others.
In terms of AI Frameworks, Scikit Learn is most preferred and is adopted by 19.9% of the respondents, followed by TensorFlow, Keras, PyTorch and Google ML.
The report aims to enable professionals to identify the most widely used tools in the market and help them build specific analytics capabilities and data science skills. The insights also aim to help organisations developing or creating an analytics function to select the appropriate stack of tools along their data science journeys.