Data Analysis with Pandas and NumPy
This course covers essential data analysis techniques using Pandas and NumPy, two core Python libraries for data manipulation and numerical computing. These tools are widely used in data science, analytics, and machine learning. Students will learn:
- Introduction to Pandas and NumPy: Students will become familiar with Pandas for data manipulation and NumPy for numerical computations, understanding how to handle data arrays and tabular data.
- DataFrames and Series in Pandas: This section covers working with Pandas’ DataFrames and Series, which are essential for managing and analyzing structured data.
- Data Cleaning and Transformation: Students will learn data cleaning techniques to handle missing data, duplicates, and outliers, as well as methods for transforming data for analysis.
- Data Aggregation and Grouping: The course teaches students to use aggregation functions and group data based on categories, making it easier to analyze and summarize large datasets.
- Merging and Joining Data: Students will understand how to combine datasets using merge and join functions, allowing them to work with multiple data sources seamlessly.
- Statistical Analysis with NumPy: The course covers basic statistical functions in NumPy for calculating means, medians, variances, and more, giving students a solid foundation in data analysis.
- Time Series Analysis: Students will learn to analyze time-based data, including resampling and handling datetime formats, enabling them to work with time series data.
This course is suitable for students interested in data analysis and data science. By the end, students will be able to handle and analyze data efficiently, making Pandas and NumPy essential tools for any data-driven project.