Data Wrangling with Python

This 4-day course is geared for technical users new to Data Wrangling with Python is a four-day, comprehensive hands-on course that will provide you with the hands-on practice and foundational skills needed to navigate Python programming and data wrangling effectively.

Throughout this course you’ll explore critical topics such as leveraging Python’s built-in types, structuring and organizing code, manipulating file code, and deep-diving into data wrangling. You will also gain exposure to advanced topics, including SQL and RDBMS, and their integration with Python for efficient data handling and management. The focus remains firmly on delivering practical skills that can be directly applied in a professional setting.

Hands On Environment

Hands-on activities are included allowing attendees to reinforce the concepts covered in the course content.

Course Outline  Module 1: Introduction to Python  

  • Why Python?
  • 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

Module 2: Organizing and Structuring Code

  • Python Code Best Practices
  • Error Handling and Exceptions 
  • Writing Functions and Classes
  • Modules and Packages
  • Lab: Code organization and modularization

Module 3: Manipulating Files

  • File handling in Python
  • Reading and Writing Text Files
  • File Operations and Manipulation
  • Working with JSON and CSV Files
  • Directory Operations
  • Lab: File operations and data extractions

Module 4: Introduction to Data Wrangling with Python

  • Introduction to Data Wrangling
  • Loading and Viewing Data
  • Data Cleaning Techniques
  • Data Transformation
  • Lab: Initial data wrangling exercises

Module 5: Deep Dive into NumPy, Pandas, and Matplotlib

  • Python Libraries
  • Introduction to NumPy
  • Introduction to Pandas 
  • Introduction to Matplotlib
  • Data Analysis and Visualization 
  • Lab: Data manipulation and visualization tasks using Pandas, NumPy, Matplotlib

Module 6: Advanced Data Wrangling with Python

  • What are DataFrames?
  • Merging and Joining DataFrames
  • Handling Missing Data
  • Date and Time Data
  • String Manipulations
  • Lab: Advanced data wrangling tasks using Python and Pandas

Module 7: Web Scraping and Data Gathering

  • Introduction to Web Scraping 
  • Using BeautifulSoup
  • Regular Expressions in Python 
  • APIs and JSON
  • Lab: Web scraping tasks

Module 8: Introduction to SQL and RDBMS

  • SQL Basics
  • Python’s sqlite3 module
  • SQL vs. NoSQL
  • Using SQLAlchemy with Python
  • Lab: Database interactions and data extraction tasks

Module 9: Real-world Data Wrangling

  • Best Practices in Data Wrangling
  • Dealing with Large Datasets
  • Building a Data Wrangling Pipeline
  • Lab: Real-world data wrangling task

Module 10: Machine Learning with Python

  • The Machine Learning Process
  • Python Machine Learning Libraries
  • Selecting an Algorithm
  • Training and Evaluating the Model
  • Lab: Use Scikit-learn to create a basic Machine Learning model and evaluate the model.

Capstone Project

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

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