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. Read the course FAQs or visit the Community Discussion Forum to learn more.
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
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
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 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
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
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
Certificate of Accomplishment
Earn a verified certificate of accomplishment (sample) for FREE by completing all weekly assignments and the course project. The certificate can be added to your LinkedIn profile, linked from your Resume, and downloaded as a PDF.
Instructor - Aakash N S
Aakash N S is the co-founder and CEO of Jovian. Previously, Aakash has worked as a software engineer (APIs & Data Platforms) at Twitter in Ireland & San Francisco and graduated from the Indian Institute of Technology, Bombay. He’s also an avid blogger, open-source contributor, and online educator.
Jovian Mentorship Program
Get access to a private Slack group with the course team, attend weekly office hours on Zoom, and get 1-on-1 guidance for your project by joining the Jovian Data Science Mentorship Program. This is a limited and paid program designed to help you get the most out of this course. Apply here: https://jovian.ai/mentorship .
