We’re getting Prabakaran Chandran on board to lead an interactive DataHour session with us. He has been working with Mu Sigma, a prestigious company as a Data and Decision Scientist that specializes in problem-solving, since 2019. He is skilled in SQL, Python, R, Advanced Analytics, and Statistics. In the fields of computer vision, natural language processing, and deep learning, he worked with a team of two people to develop AI-based solutions for Fortune 500 companies. He will be explaining Causal Inference and demonstrate how it may be applied to a specific use case in Python. So, Register now! We’ve got you covered and vow not to cause FOMO.
BOOK YOUR SEAT NOW!
Causal inference is defined as an intellectual subject that examines the assumptions, study designs, and estimating methodologies that enable researchers to draw causal conclusions from data.
Causation illuminates the link between cause and effect. Causal inference is a new field that helps us comprehend and explain why there is a relationship between two variables.
In this DataHour, Join Prabakaran Chandran in explaining Causal Inference and demonstrating how it may be applied to a specific use case in Python.
About the Speaker
Prabakaran has been working as a Data and Decision Scientist at Mu Sigma, a prominent problem-solving firm, since 2019. He is knowledgeable in Advanced Analytics, Statistics, Python, R, and SQL. He was part of a two-person team that developed AI-based solutions for Fortune 500 companies in the areas of computer vision, natural language processing, and deep learning.
An interest in learning Data Science and the fundamentals of statistics.
Who is this DataHour for?
- Students and recent graduates interested in pursuing a career in data science
- Working professionals interested in a career in data science
- Data science experts seeking to advance their careers
Drop us an email at [email protected] If you have any queries, or you’re facing difficulty in registering for the session.
Register for the DataHour Here to take advantage of this wonderful opportunity.
Visit our YouTube Channel to view the recordings if you missed any of the previous episodes of “The DataHour.” On our blog, you may read a summary of the DataHour sessions that have already been held here.