This course is designed to provide a comprehensive introduction to data analysis using Python, with a focus on the powerful libraries NumPy and Pandas. Participants will learn how to effectively analyze and manipulate data using these popular Python libraries, and will gain hands-on experience through practical examples and exercises. The course is suitable for beginners who are new to data analysis with Python, as well as for those with some prior experience who want to deepen their understanding of NumPy and Pandas.
Course Outline:
Introduction to NumPy
- Understanding NumPy and its features
- Installing and importing NumPy
- Creating NumPy arrays
- Basic array operations: indexing, slicing, reshaping
- Array arithmetic and broadcasting
- Array manipulation: sorting, filtering, and more
Data Analysis with Pandas
- Introduction to Pandas and its features
- Installing and importing Pandas
- Working with Pandas Series and DataFrames
- Data visualization with Pandas
- Data cleaning and preprocessing with Pandas
- Data aggregation and grouping
- Merging and joining data with Pandas
Exploratory Data Analysis (EDA) with NumPy and Pandas
- Understanding Exploratory Data Analysis (EDA) and its importance
- Descriptive statistics and data visualization with NumPy and Pandas
- Handling missing data
- Data visualization with Matplotlib and Seaborn
- Data exploration techniques with Pandas
Advanced Data Analysis Techniques with NumPy and Pandas
- Advanced data manipulation with NumPy and Pandas
- Working with time series data
- Data analysis with statistical methods and hypothesis testing
- Data visualization with advanced techniques
- Handling large datasets with NumPy and Pandas
Real-world Data Analysis Projects
- Applying NumPy and Pandas to real-world data analysis projects
- Data analysis workflows and best practices
- Building data analysis pipelines with NumPy and Pandas
- Advanced data visualization with interactive tools
- Final project: applying data analysis skills to a real-world dataset
Conclusion
- Recap of key concepts and techniques covered in the course
- Next steps for further learning and application of NumPy and Pandas in data analysis
