- Data Visualization Software
- Data Transformation
- Statistical Hypothesis Testing
- Pandas (Python Package)
- Business Analytics
- Data Manipulation
- Descriptive Statistics
- Data Wrangling
- Data Analysis
- Matplotlib
- Data Science
- Data Preprocessing
Python for Data Science
Completed by Jyoti Raul
September 6, 2024
39 hours (approximately)
Jyoti Raul'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
