Data science industry is growing at a CAGR (Compounded Annual Growth Rate) of 27%, according to Grand View Research. With this, the demand for data science skills is skyrocketing, and top employers are on the constant lookout for professionals equipped with industry-ready knowledge of concepts and practical applications.

 

It needs a well-structured program to utilize the best of this growing demand. In this exclusive Masterclass session, Kris Ghimire, who is a mentor for MIT IDSS’ Data Science and Machine Learning program, discusses the must-have skills for modern data professionals and the program’s role in delivering them.

 

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Agenda for the session

  • What inspired Kris to become a data science coach
  • Top skills and trends in data science to look out for
  • Elements of a good data science and machine learning program
  • The MIT IDSS' program experience - from an expert’s perspective

About Speakers

Kris Ghimire

Data Scientist at Banner Health

Kris Ghimire is a Data Science professional with 8 years of experience in the healthcare field. He works extensively in Python, R programming, and SAS and has a practical understanding of statistical modeling and machine learning techniques.

Data Science and Machine Learning: Making Data-Driven Decisions Program

The Data Science and Machine Learning: Making Data-Driven Decisions Program has a curriculum carefully crafted by MIT faculty to provide you with the skills & knowledge to apply data science techniques to help you make data-driven decisions.

This data science program has been designed for the needs of data professionals looking to grow their careers and enhance their data science skills to solve complex business problems. In a relatively short period of time, the program aims to build your understanding of most industry-relevant technologies today such as machine learning to deep learning, to network analytics, to recommendation systems, graph neural networks, and time series. Hence, the program is best suited for learners with prior exposure of having worked with data using some tools, and applying basic algorithms and methods.