Harvard University - Data Science: Probability
- Offered byedX
Data Science: Probability at edX Overview
Data Science: Probability
at edX
Learn probability theory -- essential for a data scientist -- using a case study on the financial crisis of 2007-2008.
Duration | 9 hours |
Total fee | Free |
Mode of learning | Online |
Schedule type | Self paced |
Difficulty level | Beginner |
Official Website | Explore Free Course  |
Credential | Certificate |
Data Science: Probability at edX Highlights
Data Science: Probability
at edX
- 20% got a tangible career benefit from this course
- Instructor -
Rafael Irizarry - Effort - 1?2 hours per week
- FREE
Add a Verified Certificate for ?3,656
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Data Science: Probability at edX Course details
Data Science: Probability
at edX
Skills you will learn
Who should do this course?
- This course is designed for those who want to become an expert data scientist
More about this course
- In this course, 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 2007-2008. 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, you need to understand the basics of probability. The course 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 to chance. Probability theory is the mathematical foundation of statistical inference which is indispensable for analyzing data affected by chance, and thus essential for data scientists.
Data Science: Probability at edX Curriculum
Data Science: Probability
at edX
Important concepts in probability theory including random variables and independence
How to perform a Monte Carlo simulation
The meaning of expected values and standard errors and how to compute them in R
The importance of the Central Limit Theorem
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