Bridging the Gap Between ML and Data Science Skills Among Indian Techies

Data Science Skills

AI and machine learning are driving Indian businesses towards success. Even though Indians have abundant knowledge and skills about ML and math, Coursera finds out that they lag due to insufficient data science skills.

Data Science is one of the most demanded domains across all industrial platforms. Over the years, the field of data science has grown, and so have the related areas. Data science involves the study of scientific methods, algorithms, processes, and systems that draw meaningful insights from structured and unstructured data.

Several companies in India are adopting this new technology to expand their businesses by gathering more data and drawing accurate insights to drive faster decision-making. This has led to an increase in the demand for data scientists in India. According to reports, India is the second-largest recruiter in data science, with over 50000 posts available as of January this year.

Recently, a study conducted by Coursera, for their Global Skills Report, reveals that despite having high proficiency in maths and machine learning, India falls behind in data skills. In Asia, India ranks low at 16, which is ahead of countries like the Philippines and Thailand, but below other countries like Japan and Singapore. Coursera mentioned that over 5.7 million candidates joined Coursera in 2020, but it continues to witness gaps in their digital skills.

Globally, the demand for data scientists overtakes its supply, similarly even after thousands of job openings for the posts of data scientists, India suffers from the lack of data science professionals. A report by AWS states that India’s workforce represents only 12% of digitally skilled workers, and the demand for digital skills is going to increase nine-fold by 2025.

To bridge these gaps, academic institutions, governments, as well as other organizations should devise creative ways to support the young generation and derive talented individuals to fulfill the demand for data science professionals.

Below are mentioned some of the key skills that are required to become successful data, science professionals.

  • Programming: Data science professionals should be adept with various programming languages, like Python, Perl, C/C++, Java, and others. These programming languages are crucial for organizing unstructured data.
  • Expert in working with unstructured data: It is easy to work with structured data, but data scientists should also be adept with unstructured data that are gathered from different sources and social media networks. Therefore, the candidate must also know how to handle social media data.
  • Knowledge of SAS and analytical tools: Understanding analytical tools will help data scientists to extract information from organized data sets. SAS, Spark, Hike, Hadoop are some of the analytical tools, popular among data professionals.
  • A strong business sense: The best way to portray technical skills in data science is to have a sound business sense. It will be difficult for an aspiring data scientist to analyze the market threats and changes in market patterns and devise an inclusive strategy if one cannot understand the basics of business strategies.
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