Python, renowned for its simplicity and robustness, has become an indispensable language in various fields, including data science, machine learning, and business analytics. Its extensive libraries for data manipulation and analysis make Python a go-to tool for individuals and organizations aiming to derive meaningful insights from data. Geared for technical users new to Python, Hands-On Practical Python for Data Wrangling & Transformation 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 the 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.
Our hands-on approach sets this course apart. A significant portion of the learning experience will be dedicated to practical lab exercises where you will apply Python, along with tools like NumPy, Pandas, Matplotlib, SQLite, and SQLAlchemy, to real-world data scenarios. These labs aim to simulate real job tasks, from data transformation to web scraping, preparing you to handle similar tasks in your current or future roles. The course also includes a few bonus, time-permitting chapters on applying Generative AI / AI / GPT to Python and Data Wrangling.
The course leverages our innovative Learning Experience Platform, promoting an interactive and collaborative learning environment, under the real-time live guidance of our industry expert. Upon course completion, you will have a strong foundation in Python programming and data wrangling, be capable of handling files and databases efficiently, and possess the skills to extract meaningful insights from complex datasets, directly benefiting your professional endeavors.
This course is approximately 50% hands-on, combining expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises. Our engaging instructors and mentors are highly experienced practitioners who bring years of current "on-the-job" experience into every classroom.
Working in a hands-on learning environment, guided by our expert team, attendees will learn to:
- Master the essentials of Python programming: From basic syntax to complex functionalities, you'll develop the skills to create, test, and debug Python programs with ease.
- Get comfortable with Python's built-in data types and structures: You'll understand how to effectively use lists, tuples, sets, and dictionaries in Python, providing the foundational building blocks for data manipulation and analysis.
- Learn to structure and organize your code: We'll help you write clean, efficient, and well-organized Python code, a crucial skill for any programming role.
- Grasp the art of data wrangling: By the end of the course, you'll be able to clean, transform, and enrich raw data to a form that's suitable for analysis – a skill in high demand in today's data-driven world.
- Get hands-on experience with Python libraries: You'll learn to use popular Python libraries such as NumPy, Pandas, and Matplotlib, empowering you to perform complex data analysis and create stunning data visualizations.
- Apply Python skills to real-world scenarios: Through our practical labs and capstone project, you'll get to apply your Python and data wrangling skills to real-world data scenarios. This experience will prepare you to tackle similar challenges in your professional life with confidence.
Please note that this list of topics is based on our standard course offering, evolved from typical industry uses and trends. We can work with you to tune this course and level of coverage to target the skills you need most. Course agenda, topics and labs are subject to adjust during live delivery in response to student skill level, interests and participation.
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