Python Data Science Course: How it Raised Opportunities in Employability

Python Data Science Course

Millions of dollars are at stake in the data science job market. Every year, India produced 50,000 qualified data science engineers and close to 4.5 lac engineers. The ratio of data science engineers to professional engineers is heavily skewed in favor of the traditional branches such as IT, Mechanical, Electronics and Telecommunications, Computer Science, Civil, Aeronautics and Electrical. But, the job market embraces Data Science engineers with an open arm, placing them with nearly 100% hiring rate within six to eight months of their graduation. That’s why Python Data Science Course is so important for regular engineering students. This course can bridge the gap between data science expertise and the job market demands.

So, what would a fresher Engineer do with a Python Data Science Course in 2019?

According to the recent job openings placed by world’s most advanced AI and analytics companies such as Hexaware, Cloudera, IBM, SAP, Google and SAS, there are at least 30,000 upcoming positions left vacant across various locations in the world. Of these, close to 10-12% are in India, specifically in Chennai, Bangalore, Hyderabad and Pune – the IT centers of India. Rest are in the US, Israel, Russia and China.

In a fast-growing agri and IT-based economy such as in India, Python data engineering course is disrupting the job market like never before. It brings in a sound blend of high-value analytical skills to solve complex and age-old problems in the industry.

Take for example, the Banking, Financial Services and Insurance industry, also called the BFSI sector. Data Analytics, Machine Learning and AI, when combined together with Python and related Cloud Computing platforms, help to build an advanced business analytics and customer service company. In India, AI can be applied to every aspect of the BFSI sector and Python engineers help to model correlation and statistical models to discover various meaningful relationships between events, customer behaviors and financial products. When ‘demonetization’ happened, the Union Government of India leveraged countless data points related to the financial sector to arrive at the decision. Banking and Insurance companies leveraged Data Science to accurately get a sense of the real impact of the demonetization on their churn, revenue and cost of Operations.

From scarping through social media posts to digging through banking transactions, BFSI analysts made a smart sense of how several events can lead to future market trends based on structured and unstructured sets of data using cutting edge AI/ML models.

Similarly, AI/ML with Python data science are used in agriculture, automotive, pharma and ecommerce sectors in India where ‘personalization’ has arrived as a key differentiator in the product marketing life cycle. If you are working with Python computing today, you are better placed in the market to become more sophisticated with super-agent skills that is suitable for any industry, any time of the year.

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