2025 | Pandas Bootcamp | Data Analysis with Pandas Python3

Based on the outline provided for the Pandas course for data science, here's a structured syllabus that can be used to organize the content of the course:

Course Title: The Pandas Bootcamp | Data Analysis with Pandas Python3

Course Description: This comprehensive course is designed to equip students with the skills and knowledge necessary to perform robust data analysis using Pandas, one of the most powerful libraries for data manipulation and analysis in Python 3. Through hands-on practice, real-world examples, and quizzes, learners will mastering data structures, applying functions, visualizing data, and more. This course is ideal for students, data analysts, business professionals, and aspiring data scientists who aim to excel in the field of data analysis using Python 3. Week 1: Introduction to Pandas

• Overview of Pandas
• Installing Pandas
• Basic Data Types (Series, DataFrame)
• Indexing and selecting data
• Reading data from different sources

Week 2: Advanced Data Handling

• Time series data handling
• Merging and concatenation of datasets
• Data filtering using boolean indexing
• Applying functions to data efficiently

Week 3: In-depth Data Analysis Techniques

• Data alignment and comparison
• Advanced GroupBy operations
• Handling missing data (NaN)
• Handling categorical data effectively

Week 4: Time Series and Categorical Data

• Working with dates and times
• Date and time manipulation and formatting
• Datetime indexing and filtering

Week 5: Data Visualization and Presentation

• Creating different types of visualizations (line, bar, histogram, scatter plot, boxplot, area plot)
• Advanced plotting techniques
• Data density plots
• Interactive plots using libraries like plotly or Bokeh

Week 6: Data Import/Export Tools

• Reading and writing CSV files
• Reading Excel files and writing back to Excel format
• Importing JSON data into Pandas for analysis

Week 7: Data Analysis with Descriptive Statistics

• Calculating descriptive statistics (mean, median, mode, variance, standard deviation)
• Using aggregate functions like sum, count, mean, etc.
• Performing cumulative and running statistical calculations

Week 8: Text and String Manipulation in Pandas

• Working with text data within Pandas
• Text string manipulation and formatting techniques

Week 9: File Handling with Pandas

• Reading large files efficiently (chunk reading)
• Writing large datasets to different file formats efficiently

Week 10: Advanced Categorical Data Techniques

• Effectively working with categorical data
• Encoding and decoding categorical data
• Using the Categorical data types in Pandas

Week 11: Working with Large Datasets (Big Data)

• Introduction to large datasets
• Handling big data efficiently using Pandas
• Performance tuning of Pandas operations on large datasets

Week 12: Final Project and Course Summary

• Applying all learned skills in a comprehensive real-world final project
• Summary and recap of key concepts covered throughout the course

Course Features:

• Expert instructor-led sessions
• Interactive hands-on practice exercises
• Real-world examples and case studies
• Comprehensive course materials and resources
• Assignments, quizzes, and projects to test knowledge and application of skills learned
• 30-day money-back guarantee for peace of mind

Who Should Take This Course?

• Data Science enthusiasts
• Aspiring data scientists
• Data analysts
• Business intelligence professionals
• Students with an interest in data analysis and statistics
• Professionals from various domains looking to leverage data in their roles or businesses

This syllabus provides a structured approach to teaching Pandas for data science. It covers the essential topics that will enable learners to effectively perform data analysis using Python 3 and Pandas. The course is designed to be engaging, and practical, with a strong emphasis on real-world applications and scenarios.