This course is part of the Data Science MicroMasters program provided by University of California San Diego. To earn the course certificate, I had to successfully complete twelve assignments and pass the proctored exam.
The course introduced me to the motivation, intuition, and theory behind the probabilistic and statistical foundations of data science. I learned the mathematical theory, as well as applied these concepts via Python programs and the Jupyter Notebook platform.
The course taught me how to visualize, understand, and reason about probabilistic and statistical concepts, and how to apply this knowledge to analyze data sets and draw meaningful conclusions from data.
The key concepts covered in this course include:
- set theory
- random variables
- dependence
- correlation
- regression
- sampling
- discrete and conditional probabilty
Credentials: