- Data Transformation
- Data Preprocessing
- Statistics
- Descriptive Statistics
- Seaborn
- Data Analysis
- Jupyter
- Feature Engineering
- Exploratory Data Analysis
- Statistical Analysis
- Data Manipulation
- Pandas (Python Package)
Python for Data Science
Completed by Artūrs Buls
November 20, 2024
39 hours (approximately)
Artūrs Buls's account is verified. Coursera certifies their successful completion of Python for Data Science
What you will learn
Build pandas pipelines to clean, transform, and aggregate real‑world datasets.
Perform EDA and compute descriptive statistics to summarize data quality and behavior.
Apply hypothesis tests (t‑test/chi‑square) and interpret results for business decisions.
Create publication‑quality charts (bar/line/box/heatmaps) with matplotlib & seaborn.
Skills you will gain
