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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