Probability and Statistics in Data Science using Python
The University of California, San Diego
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assignment Level : Advanced
chat_bubble_outline Language : English
language Subtitles : English
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Key information

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verified_user Fee-based Certificate
timer 100 hours in total

About the content

The job of a data scientist is to glean knowledge from complex and noisy datasets.

Reasoning about uncertainty is inherent in the analysis of noisy data. Probability and Statistics provide the mathematical foundation for such reasoning.

In this course, part of the Data Science MicroMasters program, you will learn the foundations of probability and statistics. You will learn both the mathematical theory, and get a hands-on experience of applying this theory to actual data using Jupyter notebooks.

Concepts covered included: random variables, dependence, correlation, regression, PCA, entropy and MDL.

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Prerequisite

  1. The previous course in the MicroMasters program: DSE200x 
  2. Undergraduate level education in:
    • Multivariate calculus
    • Linear algebra
       

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Syllabus

  • The mathematical foundations for machine learning
  • Statistics literacy: understand the meaning of statements such as “at a 99% confidence level”
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Intructors

Alon Orlitsky
Professor, Electrical and Computer Engineering
UC San Diego

Yoav Freund
Professor of Computer Science and Engineering
UC San Diego

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Content designer

The University of California, San Diego
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