Summary:

Short Course Content 

Schedule

Details of program

**Day 1: Introduction to Pandas**

- Introduction to Pandas: What are Pandas, why it is used, and its advantages

- Installing Pandas: How to install Panda’s library using pip or conda

- Series and Data Frames: Understanding the core data structures in Pandas

- Creating Series and Data Frames: How to create Series and Data Frames from various data structures like lists, dictionaries, and NumPy arrays

- Basic operations: Indexing, slicing, and basic operations on Series and Data Frames

- Handling missing data: How to handle missing data using Pandas

- Exercises: Hands-on exercises to practice creating Series and Data Frames, performing basic operations, and handling missing data

**Day 2: Data Manipulation with Pandas**

- Reading and writing data: How to read data from various file formats like CSV, Excel, JSON, and SQL databases, and how to write data to these formats

- Data manipulation: Manipulating and transforming data using Pandas functions like filtering, sorting, grouping, and aggregating

- Applying functions: Using apply (), map (), and apply map () functions for element-wise operations

- Combining Data Frames: Concatenating, merging, and joining Data Frames

- Exercises: Hands-on exercises to practice reading and writing data, data manipulation, and combining Data Frames

**Day 3: Data Analysis with Pandas**

- Exploratory Data Analysis (EDA): Understanding the data using descriptive statistics, visualization, and summary functions

- Statistical analysis: Performing statistical analysis using Pandas functions like mean, median, mode, variance, and correlation

- Data visualization: Visualizing data using Pandas built-in visualization capabilities and Matplotlib integration

- Exercises: Hands-on exercises to practice exploratory data analysis, statistical analysis, data visualization, and time series analysis

**Day 4: Advanced Topics**

- Reshaping data: Reshaping and pivoting data using melt (), pivot (), stack (), and unstack () functions

- Handling large datasets: Techniques for handling large datasets efficiently with Pandas

- Advanced indexing: multi-level indexing, Boolean indexing, and setting and resetting index

- Group by: Performing group-wise operations using group by () function

- Case study: Real-world case study applying Pandas for data analysis and manipulation

- Q&A and wrap-up: Open Q&A session to address any remaining questions and wrap-up of the workshop