Python for Data Analysis / Data Science: A Crash Course

🌟 Crash Course: Data Analysis using Pandas in Python 🌟

Course Description:

Embark on a transformative journey into the world of data analysis with our comprehensive “Crash Course: Data Analysis using Pandas in Python.” This course is meticulously designed to guide you through every crucial aspect of data manipulation and analysis. Whether you’re a beginner or looking to sharpen your skills, this course promises to equip you with the knowledge and techniques essential for effective data handling.

What You Will Learn:

• Section 1: Getting Started with Python 🚀
  • Installation of Anaconda distribution and writing your first code.
  • A comprehensive walkthrough of the Spyder Platform to familiarize you with your new data analysis environment.
• Section 2: Working on Data 📊
  • Running SQL commands from within Python.
  • Understanding dataset contents and adding comments for better code readability.
  • Handling missing values, whether numeric or date-related, and creating copies of dataframes while filtering out records with missing values.
  • Mastering numerical variable analysis, including group by operations and transposing results.
  • Executing frequency distribution counts, including the percentage of missing values, and delving into functions and substring manipulations.
• Section 3: Working with Multiple Datasets 🛠️
  • Creating dataframes dynamically and appending or concatenating them.
  • Merging datasets and mastering the art of removing duplicates.
  • Advanced sorting techniques, finding records for max/min values, and leveraging iterrows to solve complex problems.
  • Deriving new variables from both numerical and character fields, as well as analyzing data based on date fields.
• Section 4: Data Visualization 🎨
  • Generating histograms, bar charts, line charts, pie charts, and box plots to visualize your data effectively.
  • Revisiting some Python fundamentals to ensure you have a solid grasp of the language’s capabilities.
  • Understanding variable scope and utilizing range objects, casting, string slicing, and lambda functions.
• Section 5: Statistical Procedures & Advanced Topics 🔍
  • Identifying and treating outliers.
  • Creating Excel-formatted reports.
  • Crafting pivot tables on pandas dataframes, renaming column names, reading from/writing to SQLite databases, and more!
  • Performing linear regression and conducting a chi-square test of independence.

Course Highlights:

✅ Practical Approach: Learn by doing with real-world datasets and hands-on exercises.

✅ Expert Guidance: Gopal Prasad Malakar, an experienced instructor, will be your guide through this data analysis journey.

✅ Flexible Learning: Access the course materials at your convenience and learn at your own pace.

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