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Data Wrangling with Python

This course assumes a working knowledge of Python basics including data structures, importing and using modules, and creating functions. It then delves into the fundamental tools of data wrangling like NumPy and Pandas libraries.

The course starts with the absolute basics of Python, focusing mainly on data structures. It then delves into the fundamental tools of data wrangling like NumPy and Pandas libraries. You'll explore useful insights into why you should stay away from traditional ways of data cleaning, as done in other languages, and take advantage of the specialised pre-built routines in Python. This combination of Python tips and tricks will also demonstrate how to use the same Python backend and extract/transform data from an array of sources including the Internet, large database vaults, and Excel financial tables. To help you prepare for more challenging scenarios, you'll cover how to handle missing or wrong data, and reformat it based on the requirements from the downstream analytics tool. This data wrangling course will further help you grasp concepts through real-world examples and datasets.

Key Features of this Data Wrangling with Python Training:

  • After-course instructor coaching benefit

Who Should Attend 

Data Wrangling with Python provides a fast-paced, practical approach to the most essential data analysis tools in the shortest possible time. It contains multiple activities to focus on the real-life business scenarios for you to practise and apply your new skills in a highly relevant context.

What is data wrangling in Python?

It is the process of unifying and cleaning raw data sets to make them easier to analyse.

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Learning Objectives

  • Use a diverse array of sources to extract data.
  • Clean, transform and format data efficiently.
  • Use python tricks to transform data into useful and meaningful data sets.


Working knowledge of the Python language and familiarity with the Jupyter Notebook platform are required.

Course Content

Lesson 1: Introduction to Data Structure using Python

  • Python for Data Wrangling
  • Lists, Sets, Strings, Tuples, and Dictionaries

Lesson 2: Advanced Operations on Built-In Data Structure

  • Advanced Data Structures
  • Basic File Operations in Python

Lesson 3: Introduction to NumPy, Pandas, and Matplotlib

  • NumPy Arrays
  • Pandas DataFrames
  • Statistics and Visualisation with NumPy and Pandas
  • Using NumPy and Pandas to Calculate Basic Descriptive Statistics on the DataFrame

Lesson 4: Deep Dive into Data Wrangling with Python

  • Subsetting, Filtering, and Grouping
  • Detecting Outliers and Handling Missing Values
  • Concatenating, Merging, and Joining
  • Useful Methods of Pandas

Lesson 5: Get Comfortable with a Different Kind of Data Sources

  • Reading Data from Different Text-Based (and Non-Text-Based) Sources
  • Introduction to BeautifulSoup4 and Web Page Parsing

Lesson 6: Learning the Hidden Secrets of Data Wrangling

  • Advanced List Comprehension and the zip Function
  • Data Formatting

Lesson 7: Advanced Web Scraping and Data Gathering

  • Basics of Web Scraping and BeautifulSoup libraries
  • Reading Data from XML

Lesson 8: RDBMS and SQL

  • Refresher of RDBMS and SQL
  • Using an RDBMS (MySQL/PostgreSQL/SQLite)

Lesson 9: Application in Real Life and Conclusion of Course

  • Applying Your Knowledge to a Real-life Data Wrangling Task
  • An Extension to Data Wrangling

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