This course offers a deep and wide range of skills set from Programming to statistics and machine learning algorithms. The skills you will attain from this course could make you an expert Data Analyst, Quality Analyst and Business Analyst and Statistical Analyst roles. Machine learning algorithms such as Regression, Clustering, Classification and prominent libraries such as Pandas, Matplotlib, SciKit -learn is covered from this course. The main goal of the course is to provide a deeper understanding and hands-on learning experience on the Data Science domain with the help of Python programming language along with real-time Data Science projects to provide an overall knowledge on Data Science domain. This course covers all the topics from Mathematics to Programming to Visualization techniques that are needed for a Data Scientist role.

For this edition of My Journey In Data Science column, Analytics India Magazine got in touch with a data scientist, influencer and blogger. Rahul Agrawal, Data Scientist at Walmart Labs, shared his exciting journey in data science, and also offered advice on best practices for aspirants to thrive in the ever-changing data science landscape. Rahul is a mechanical engineer from IIT Delhi, who started his job in a steel company in 2010, but quit the job since it was not interesting enough. Then he joined Fractal Analytics in 2011 as a business analyst. "Initially, I wrote a lot of SQL and made dashboards – most of the work revolved around reporting. And it was not a love-at-first-sight for me," says Rahul.

Data Scientists help find insights about the market and help make products better. They are responsible for analyzing and handling a massive amount of structured and unstructured data and require various tools to do so. Some of the tools used by Data Scientists to carry out their data operations are mentioned below. Base SAS programming language, which is generally used for statistical modeling is used by SAS. It offers a number of statistical libraries and tools that can be used for modeling and organizing data. SAS is highly reliable, it is also quite expensive and thus is used mainly by larger industries.