Data Analysis with Python: Zero to Pandas
Offered By: Jovian
Course Description
Overview
"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.
Syllabus
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
- 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
Taught by
Aakash N S
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