Learn probability theory -- essential for a data scientist -- using a case study on the financial crisis of 2007-2008.
HarvardX Professional Certificate Program in Data Science
This comprehensive course on Probability for Data Science, offered by HarvardX, is an essential component of the Professional Certificate Program in Data Science. It provides a deep dive into the fundamental concepts of probability theory, using the 2007-2008 financial crisis as a compelling backdrop to illustrate the real-world importance of understanding and accurately assessing risk.
The course is designed to equip students with the statistical tools necessary for conducting data analysis, interpreting results, and making informed decisions in various fields, including finance, data science, and risk management. By mastering probability theory, students will gain a solid foundation for statistical inference, which is crucial for analyzing data affected by chance and essential for aspiring data scientists.
This course is designed as an introductory-level program, and there are no specific prerequisites. However, basic familiarity with mathematics and statistics concepts would be beneficial. Students should also be prepared to work with the R programming language for statistical computations.
How learners can use these skills in the real world:
By mastering probability theory, students will be well-equipped to tackle complex data analysis challenges, make informed decisions based on statistical evidence, and contribute meaningfully to their chosen fields. The course's focus on real-world applications, particularly in finance and data science, ensures that learners can immediately apply their knowledge to solve practical problems and advance their careers.
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This lecture series discusses how the concept of probability can be used to handle, control, and exploit uncertainty in the real-world. It is an undergraduate-level lecture series on probability, but is entirely different from the usual courses on probability theory. The lectures cover the basics of probability theory including the relevant mathematics, but instead of focusing on mathematics, the lectures explain how probability theory can help understand real-world uncertainty using various examples. The examples are used to describe how uncertainty can be exploited to implement modern randomized algorithms such as Markov chain Monte Carlo and deep learning.
This course introduces the basic tools and methods of statistical analysis, which can be applied to a wide variety of situations and data encountered in the areas of business and economics. Topics discussed include descriptive statistics, probability, sampling distributions, estimation, and hypothesis testing. By the end of the semester students should be able to compute and interpret basic descriptive statistical measures, understand the basic concepts of probability, and apply techniques of statistical inference.
The first in a series of four courses which help you to master statistics fundamentals and build your quantitative skillset for progression in high-growth careers, or to use as step towards further study at undergraduate level.
