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Course Skill Level:


Course Duration:

2 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    Big Data & Data Science

  • Course Code:


Who should attend & recommended skills:

Those with basic Developing, Python, and spreadsheet experience

Who should attend & recommended skills

  • This course is geared for Python experienced developers, analysts or others who wish to Simplify your ETL processes with these hands-on data hygiene tips, tricks, and best practices.
  • Skill-level: Foundation-level Data Wrangling with Python skills for Intermediate skilled team members. This is not a basic class.
  • Developing: Basic (1-2 years’ experience)
  • Python: Basic (1-2 years’ experience)
  • Spreadsheet software: Basic to Intermediate (1-5 years’ experience)

About this course

For data to be useful and meaningful, it must be curated and refined. Data Wrangling with Python teaches you the core ideas behind these processes and equips you with knowledge of the most popular tools and techniques in the domain. 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 specialized 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. The course will further help you grasp concepts through real-world examples and datasets. By the end of this course, you will be confident in using a diverse array of sources to extract, clean, transform, and format your data efficiently.

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by our Data Wrangling with Python expert instructor, students will learn about and explore:
  • Focusing on the basics of data wrangling
  • Studying various ways to extract the most out of your data in less time
  • Boosting your learning curve with bonus topics like random data generation and data integrity checks
  • Using and manipulate complex and simple data structures
  • Harnessing the full potential of DataFrames and numpy.array at run time
  • Performing web scraping with BeautifulSoup4 and html5lib
  • Executing advanced string search and manipulation with RegEX
  • Handling outliers and perform data imputation with Pandas
  • Using descriptive statistics and plotting techniques
  • Practicing data wrangling and modeling using data generation techniques

Course breakdown / modules

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

  • Introduction
  • Advanced Data Structures
  • Basic File Operations in Python

  • Introduction
  • NumPy Arrays
  • Pandas DataFrames
  • Statistics and Visualization with NumPy and Pandas

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

  • Introduction
  • Reading Data from Different Text-Based (and Non-Text-Based) Sources
  • Introduction to Beautiful Soup 4 and Web Page Parsing

  • Introduction
  • Advanced List Comprehension and the zip Function
  • Data Formatting
  • Identify and Clean Outliers
  • Activity 8: Handling Outliers and Missing Data

  • Introduction
  • The Basics of Web Scraping and the Beautiful Soup Library
  • Reading Data from XML
  • Reading Data from an API
  • Fundamentals of Regular Expressions (RegEx)

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

  • Introduction
  • Applying Your Knowledge to a Real-life Data Wrangling Task
  • Activity 12: Data Wrangling Task – Fixing UN Data
  • Activity 13: Data Wrangling Task – Cleaning GDP Data
  • Activity 14: Data Wrangling Task – Merging UN Data and GDP Data
  • Activity 15: Data Wrangling Task – Connecting the New Data to the Database
  • An Extension to Data Wrangling