Hands-On Practical Python for Data Wrangling & Transformation (TTPS4880)

1.       Introduction to Python

·         Understand Python’s significance and its application in modern enterprises.

·         Python Basics and Syntax

·         Python Built-in Types

·         Variables, Lists, Dictionaries, and Tuples • Control Structures: If, For, While

·         Lab: Hands-on Python basics using Python, Jupyter Notebook

2.       Organizing and Structuring Code

·         Gain skills to write efficient and organized Python code.

·         Writing Functions and Classes

·         Modules and Packages

·         Error Handling and Exceptions • Pythonic Coding Practices

·         Lab: Code organization and modularization

3.       Manipulating Files

·         Learn file handling in Python for reading and writing data

·         Reading and Writing Text Files

·         File Operations and Manipulation

·         Working with JSON and CSV Files

·         Directory Operations

·         Lab: File operations and data extraction

4.       Introduction to Data Wrangling with Python

·         Grasp the concept of Data Wrangling and its importance in Python.

·         Introduction to Data Wrangling

·         Loading and Viewing Data

·         Data Cleaning Techniques

·         Data Transformation

·         Lab: Initial data wrangling exercises

5.       Deep Dive into NumPy, Pandas, and Matplotlib

·         Discover essential Python libraries for data analysis and visualization.

·         Introduction to NumPy

·         Introduction to Pandas • Introduction to Matplotlib

·         Data Analysis and Visualization Using Above Libraries

·         Lab: Data manipulation and visualization tasks using Pandas, NumPy, Matplotlib

6.       Advanced Data Wrangling with Python

·         Gain advanced skills for wrangling data using Python.

·         Merging and Joining DataFrames

·         Handling Missing Data

·         Date and Time Data

·         String Manipulations

·         Lab: Advanced data wrangling tasks using Python and Pandas

7.       Web Scraping and Data Gathering

·         Learn the techniques to extract data from the web.

·         Introduction to Web Scraping • Using BeautifulSoup

·         Regular Expressions in Python • APIs and JSON

·         Lab: Web scraping tasks

8.       Introduction to SQL and RDBMS

·         Understand SQL’s role in data wrangling and Python’s integration with it.

·         SQL Basics

·         Python’s sqlite3 module

·         SQL vs. NoSQL

·         Using SQLAlchemy with Python

·         Lab: Database interactions and data extraction tasks

9.       Real-world Data Wrangling 

·         Apply learned skills to real-world data wrangling scenarios.

·         Case Studies in Data Wrangling

·         Best Practices in Data Wrangling

·         Dealing with Large Datasets

·         Building a Data Wrangling Pipeline

·         Lab: Real-world data wrangling task

10.    Next Steps in Python and Data Wrangling

·         Overview of Advanced Python Topics

·         Overview of Machine Learning with Python

·         Overview of Big Data Tools (e.g., Spark)

·         Lab: Exploring Machine Learning and Big Data Tools:  Use Scikit-learn to create a basic Machine Learning model and then apply PySpark to handle a small simulated Big Data task.

11.    Capstone Projects / Optional

·         Lab Project: Hands-on Real-world Data Wrangling Project – Apply the skills learned throughout the course in a practical project.

·         Project 1: Building a Data Pipeline – Extract, transform, and load data from multiple sources.

·         Project 2: Web Scraping and Data Analysis – Extract data from the web and perform analysis.

Addendum: Post-Training Skills Development 

·         Continued Learning Resources

·         Suggestions for Practical Applications of Skills Learned

·         Recommended Python and Data Science Communities and Forums

·         Additional Tools for Data Science (e.g., Scikit-Learn, TensorFlow, PyTorch, etc.)

·         Contributing to Open-Source Projects

Bonus Chapters:  (Optional / Time Permitting)

12.    Bonus: Generative AI for Python Programming and Data Wrangling

·         Understand the role of AI in code generation and its applications in Python and Data Wrangling.

·         Introduction to Generative AI •

·         Overview of GPT Technology

·         GPT Applications in Python Programming and Data Wrangling

·         Using AI for Code Completion, Error Detection, and Data Analysis

·         Lab: Exploring AI-assisted Python programming and data wrangling with GPT technology

13.    Bonus: Advanced Python Skills Using AI Technologies

·         Enhance Python skills and productivity using AI-powered tools.

·         Overview of AI Tools for Python

·         AI for Automated Testing and Debugging

·         Using AI for Code Optimization • Machine Learning-based Predictive Analytics with Python

Lab: Apply AI tools to improve Python programming and perform predictive analytics