Data Analysis with Python: Zero to Pandas

"Data Analysis with Python: Zero to Pandas" is a practical and beginner-friendly introduction to data analysis covering the basics of Python, Numpy, Pandas, Data Visualization, and Exploratory Data Analysis.

• Watch hands-on coding-focused video tutorials
• Practice coding with cloud Jupyter notebooks
• Build an end-to-end real-world course project
• Earn a verified certificate of accomplishment

The course is self-paced and there are no deadlines. There are no prerequisites for this course.

• 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

• Branching with conditional statements and loops
• Write reusable code with Functions
• Working with the OS & Filesystem
• Assignment and course forum walkthrough

• 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

• Going from Python lists to Numpy arrays
• Working with multi-dimensional arrays
• Array operations, slicing and broadcasting
• Working with CSV data files

• Explore the Numpy documentation website
• Demonstrate usage 5 numpy array operations
• Publish a Jupyter notebook with explanations
• Share your work with the course community

• 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

• 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

• Basic visualizations with Matplotlib
• Advanced visualizations with Seaborn
• Tips for customizing and styling charts
• Plotting images and grids of charts

• 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

• Finding a good real-world dataset for EDA
• Data loading, cleaning and preprocessing
• Exploratory analysis and visualization
• Answering questions and making inferences