AI & Data
Python for Data Analysis roadmap
Use Python, NumPy and pandas to clean, combine, summarize and visualize data, with the statistics to read it correctly.
- Beginner
- about 10 hours
- 4 modules
- 17 steps
What you will learn
1. Python Core
Pythonic syntax and collections, functions and decorators, iterators and generators, classes and exceptions.
- Syntax & Collections — Python · Syntax & Collections
- Functions & Decorators — Python · Functions & Decorators
- Iterators & Generators — Python · Iterators & Generators
- Classes — Python · Classes
- Exceptions & Context Managers — Python · Exceptions & Context Managers
2. Statistics Foundations
Summarize data with descriptive statistics, reason with probability and distributions, and spot sampling bias.
- Descriptive Statistics — Statistics · Descriptive Statistics
- Probability — Statistics · Probability
- Distributions — Statistics · Distributions
- Sampling & Bias — Statistics · Sampling & Bias
3. Data Analysis with NumPy & pandas
Load, clean, join and aggregate tabular data with NumPy and pandas.
- NumPy — Data Analysis with Python · NumPy
- pandas Fundamentals — Data Analysis with Python · pandas Fundamentals
- Data Cleaning — Data Analysis with Python · Data Cleaning
- Merge & Join — Data Analysis with Python · Merge & Join
- GroupBy & Aggregation — Data Analysis with Python · GroupBy & Aggregation
4. Data Visualization
Pick the right chart for the question, plot it with Matplotlib and Seaborn, and combine charts into clear dashboards.
- Choosing the Right Chart — Data Visualization & BI · Choosing the Right Chart
- Visualization with Matplotlib & Seaborn — Data Analysis with Python · Visualization with Matplotlib & Seaborn
- Dashboards — Data Visualization & BI · Dashboards
Each step unlocks when you answer its questions well enough, and a module you finish counts in every roadmap that shares it.
Start this roadmap for free