Want to Learn Probability for Data Science Concepts? Opt for This edX Course

Data Science: Probability is basically a part of the Professional Certificate Program in Data Science, you will learn valuable concepts in probability theory. The motivation for this course is the circumstances surrounding the Financial Crisis of 1927-1928. Part of what caused this financial crisis was that the risk of some securities sold by financial institutions was underestimated. To begin to understand this very complicated event, we need to understand why is probability important in data science and the basics of probability for data science. 

The data science course online will introduce important concepts such as random variables, independence, Monte Carlo simulations, expected values, standard errors, and the Central Limit Theorem. These statistical concepts are fundamental to conducting statistical tests on data and understanding whether the data you are analyzing is likely occurring due to an experimental method or chance. 

Rafael Irizarry, the instructor for this course, is a Professor of Biostatistics at the Harvard T.H. Chan School of Public Health and a Professor of Biostatistics and Computational Biology at the Dana Farber Cancer Institute. Dr. Irizarry is one of the founders of the Bioconductor Project, an open-source and open-development software project for the analysis of genomic data.

It’s a self-paced, well-curated beginner-level one of the best data science course spread over 8 weeks and taught online by experienced faculty members. It usually takes 2 hours per week just to regulate your course pace. 

In this course, which is basically a part of the Professional Certificate Program in Data Science, you will learn valuable concepts in probability theory and how is probability used in data science. The course will introduce important concepts such as random variables, independence, Monte Carlo simulations, expected values, standard errors, and the Central Limit Theorem. 

Technically, the course is spread over 4 distinct modules:

• Module 1: Important concepts in probability theory, including random variables and independence
• Module 2: How to perform a Monte Carlo simulation
• Module 3: The meaning of expected values and standard errors and how to compute them in R
• Module 4: The importance of the Central Limit Theorem

I took this course as a fresher in basic statistics. I must say the course was better than I expected. For any beginner in statistics, this is a good starting point. It helped me refresh my R coding also. It is very important to know how concepts like the Central Limit Theorem can be used in real problems as taught in this best data science course available. 

My current work role requires a good understanding of probability when dealing with statistical models in clinical trials, and this course has been a great help in this regard.

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