Data Analysis with Python: Zero to Pandas "Data Analysis with Python: Zero to Pandas" is a practical, beginner-friendly and coding-focused introduction to data analysis covering the basics of Python, Numpy, Pandas, data visualization and exploratory data analysis.
Module
Content
Lesson 1 Introduction to Programming with Python Course overview & curriculum walkthrough First steps with Python and Jupyter notebooks A quick tour of variables and data types Branching with conditional statements and loops
Assignment 1 Python Basics Practice Solve word problems using variables & arithmetic operations Manipulate data types using methods & operators Use branching and iterations to translate ideas into code Explore the documentation and get help from the community
Lesson 2 Next Steps with Python Branching with conditional statements and loops Write reusable code with Functions Working with the OS & Filesystem Assignment and course forum walkthrough
Lesson 3 Numerical Computing with Numpy Going from Python lists to Numpy arrays Working with multi-dimensional arrays Array operations, slicing and broadcasting Working with CSV data files
Assignment 2 Numpy Array Operations Explore the Numpy documentation website Demonstrate usage 5 numpy array operations Publish a Jupyter notebook with explanations Share your work with the course community
Lesson 4 Analyzing Tabular Data with Pandas Reading and writing CSV data with Pandas Querying, filtering and sorting data frames Grouping and aggregation for data summarization Merging and joining data from multiple sources
Assignment 3 Pandas Practice Create data frames from CSV files Query and index operations on data frames Group, merge and aggregate data frames Fix missing and invalid values in data
Lesson 5 Visualization with Matplotlib and Seaborn Basic visualizations with Matplotlib Advanced visualizations with Seaborn Tips for customizing and styling charts Plotting images and grids of charts
Lesson 6 Exploratory Data Analysis - A Case Study Finding a good real-world dataset for EDA Data loading, cleaning and preprocessing Exploratory analysis and visualization Answering questions and making inferences
Course Project Exploratory Data Analysis Find a real-world dataset of your choice online Use Numpy & Pandas to parse, clean & analyze data Use Matplotlib & Seaborn to create visualizations Ask and answer interesting questions about the data Project is available in another repo @