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CS-401: Applied data analysis

Summary

This course teaches the basic techniques, methodologies, and practical skills required to draw meaningful insights from a variety of data, with the help of the most acclaimed software tools in the data science world (pandas, scikit-learn, Spark, etc.)

Lectures

Instructor

Maria Brbic

Maria Brbic is an assistant professor in computer science at EPFL. Prior to joining EPFL, Maria was a postdoctoral researcher in Computer Science at Stanford University working with Jure Leskovec. She received her PhD degree from University of Zagreb in 2019, while also researching at Stanford University and University of Tokyo. Her research was awarded with the Fulbright Scholarship, L’Oreal UNESCO for Women in Science Scholarship, Branimir Jernej award for outstanding publication in biology and biomedicine, and Josip Loncar Silver Plaque award for the best doctoral dissertation. She has been named a Rising Star in EECS by MIT in 2021. Her research is focused on developing new machine learning methods and applying her methods to advance biomedical research.

Official source

https://edu.epfl.ch/coursebook/en/applied-data-analysis-CS-401

Moodle Page

http://go.epfl.ch/CS-401

About this result

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Summary

Lectures

Instructor

Maria Brbic

Official source

https://edu.epfl.ch/coursebook/en/applied-data-analysis-CS-401

Moodle Page

http://go.epfl.ch/CS-401

About this result

Lectures in this course (80)

Data Loading and Filtering

Covers loading compressed files, filtering dataframes, and analyzing data distribution for effective analysis.

Introduction to Jupyter Notebook

Introduces setting up the environment, Git basics, and Jupyter Notebook features with practical examples.

Describing Data: Statistics and Hypothesis Testing

Covers descriptive statistics, hypothesis testing, and correlation analysis with various probability distributions and robust statistics.

Linear Regression Analysis

Introduces linear regression analysis, covering model building, predictors, coefficients, and outcome interpretation.

Observational Studies: Pitfalls and Solutions

Explores observational studies, pitfalls, solutions, and the importance of valid conclusions in research projects.

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