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Introduction to Python for Data Analysis
Python is a versatile, beginner-friendly programming language and one of the most widely used tools for data analysis, thanks to its powerful libraries. In this path, you’ll learn Python from the ground up, then use NumPy and pandas to import, clean, transform, and combine data. You’ll visualize results, apply descriptive statistics and trend analysis, and build basic clustering and regression models to answer business questions.
**This learning path is actively in production. More content will be added to this path as it gets published and becomes available in the library. To view the full list of content to be included in this Path, click the "About this path" tab below.**
Content in this path
Python Data Essentials
Learn the core building blocks of Python, from setting up a notebook environment to working with data types, collections, loops, and functions. This section lays the foundation for every data analysis task in the path.
Introduction to Analytics Modeling in Python
Take a first step into modeling by grouping data with clustering and predicting outcomes with regression and classification models.
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What You'll Learn
- How to write Python code in Jupyter notebooks using core Python building blocks such as data types, collections, control flow, and functions.
- How to prepare data for analysis with Python by importing it from files and databases, then cleaning, transforming, and combining it.
- How to answer business questions with data in Python using charts, descriptive statistics, trend analysis, and basic clustering and regression models.
- \--- Python Data Essentials ---\
- 1. Python Data Essentials: Python Introduction \
- 2. Perform Basic Calculations for Data Analysis in Python \
- 3. Python Data Essentials: Data Structures \
- 4. Create Basic Lists and Dictionaries for Data Analysis in Python \
- 5. Python Data Essentials: Programming Fundamentals \
- 6. Write Basic Loops and Functions for Data Analysis in Python \
- \
- -- Work with Data in Python ---\
- 7. Introduction to NumPy \
- 8. Work with Basic NumPy Arrays in Python\
- 9. Introduction to pandas \
- 10. Explore a Dataset with pandas in Python \
- 11. Import Data from Files and SQL Databases into Python \
- 12. Perform a Basic Data Import for Data Analysis in Python \
- \
- -- Clean and Manipulate Data in Python ---\
- 13. Clean Data in Python \
- 14. Perform Basic Data Cleaning for Data Analysis in Python\
- 15. Transform Data in Python \
- 16. Create Basic Calculated Columns for Data Analysis in Python \
- 17. Combine and Summarize Data in Python \
- 18. Perform Basic Joins and Aggregations for Data Analysis in Python \
- \
- -- Explore and Analyze Data in Python ---\
- 19. Introduction to Data Visualization in Python \
- 20. Create Basic Charts for Data Analysis in Python \
- 21. Introduction to Statistical Analysis in Python \
- 22. Calculate Basic Descriptive Statistics for Data Analysis in Python \
- 23. Introduction to Time Series Analysis in Python \
- 24. Perform Basic Time Series Analysis in Python \
- 25. Exploratory Data Analysis in Python \
- 26. Perform Basic Exploratory Data Analysis in Python \
- \
- -- Introduction to Analytics Modeling in Python ---\
- 27. Introduction to Clustering in Python \
- 28. Build a Basic Clustering Model for Data Analysis in Python \
- 29. Introduction to Predictive Modeling in Python \
- 30. Build a Basic Regression Model for Data Analysis in Python
- Learners interested in this path should be familiar with core data analysis concepts, but no prior programming or Python experience is required.
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