Python Pandas for Beginners
Course Description
Welcome to Python Pandas for Beginners! In this comprehensive 10-day course, you will learn the fundamentals of data manipulation and analysis using the Python Pandas library. Whether you’re a data enthusiast, analyst, or aspiring data scientist, this course will equip you with the essential skills to effectively work with data in Python.
Course Plan
Day 1: Introduction to Python Pandas
- Introduction to Python Pandas and its importance in data analysis
- Installation and setup of Python and Pandas
- Exploring Series and DataFrames in Pandas
- Basic data manipulation operations in Pandas
Day 2: Data Loading and Cleaning
- Importing data into Pandas from different file formats (CSV, Excel, etc.)
- Handling missing data and data cleaning techniques
- Data filtering and selection in Pandas
Day 3: Data Manipulation with Pandas
- Data transformation and reshaping using Pandas
- Working with columns, rows, and indexes in DataFrames
- Applying functions and operations on data in Pandas
Day 4: Exploratory Data Analysis (EDA) with Pandas
- Descriptive statistics and summary analysis
- Data visualization using Pandas and Matplotlib
- Extracting insights from data using Pandas
Day 5: Data Aggregation and Grouping
- Aggregating and summarizing data in Pandas
- Grouping and grouping operations
- Pivot tables and cross-tabulations in Pandas
Day 6: Merging and Joining Data
- Combining and merging data from different sources
- Handling duplicate and duplicate data in Pandas
- Joining DataFrames using various techniques
Day 7: Time Series Analysis
- Introduction to time series data and its characteristics
- Handling time series data in Pandas
- Analyzing and visualizing time series data using Pandas
Day 8: Advanced Data Manipulation Techniques
- Advanced data cleaning and preprocessing techniques
- Applying advanced functions and operations on data
- Handling outliers and anomalies in Pandas
Day 9: Working with External Libraries
- Integrating Pandas with other Python libraries (NumPy, SciPy, etc.)
- Leveraging additional functionality for data analysis
- Advanced data visualization with Seaborn and Pandas
Day 10: Final Project and Recap
- Applying the learned concepts to a real-world data analysis project
- Recap of the course topics and key takeaways
- Resources and next steps for further learning