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DATA-WRGLG-PYTHON.AJ1
Data Wrangling with Python
Achieve proficiency in the data analysis process in no time!
- Practice in 45 Hands-On Labs — nothing to install
- 10 Interactive Lessons and 51 topics mapped to the official exam objectives
- 98 Practice Test Questions
Intermediate Self-paced · 1 year access 4.7/5 (212 Reviews)
45 Hands-On LiveLabs
Practice real IT tasks in guided environments.
- Real environments
- Auto-graded
- No installation
01 / Skills you'll get
What you will be able to do
Course Highlights
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10 Structured Lessons Comprehensive coverage of core course objectives
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45 Hands-On LiveLabs Interactive guided scenarios with instant evaluation
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98 Practice Questions Assessment tests with detailed answer rationales
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1 Year Full Access Self-paced learning accessible anytime on all devices
02 / Lessons & labs
See exactly what you will learn and practice
Lessons
10 Interactive Lessons · 51 topics01 Introduction 8 topics +
- About the Course
- Learning Objectives
- Approach
- Audience
- Minimum Hardware Requirements
- Software Requirements
- Conventions
- Installation and Setup
02 Introduction to Data Wrangling with Python 4 topics · 5 LiveLab +
- Introduction
- Python for Data Wrangling
- Lists, Sets, Strings, Tuples, and Dictionaries
- Summary
5 LiveLab in this lesson — see the labs panel →
03 Advanced Data Structures and File Handling 4 topics · 5 LiveLab +
- Introduction
- Advanced Data Structures
- Basic File Operations in Python
- Summary
5 LiveLab in this lesson — see the labs panel →
04 Introduction to NumPy, Pandas, and Matplotlib 5 topics · 6 LiveLab +
- Introduction
- NumPy Arrays
- Pandas DataFrames
- Statistics and Visualization with NumPy and Pandas
- Summary
6 LiveLab in this lesson — see the labs panel →
05 A Deep Dive into Data Wrangling with Python 6 topics · 7 LiveLab +
- Introduction
- Subsetting, Filtering, and Grouping
- Detecting Outliers and Handling Missing Values
- Concatenating, Merging, and Joining
- Useful Methods of Pandas
- Summary
7 LiveLab in this lesson — see the labs panel →
06 Getting Comfortable with Different Kinds of Data Sources 4 topics · 4 LiveLab +
- Introduction
- Reading Data from Different Text-Based (and Non-Text-Based) Sources
- Introduction to Beautiful Soup 4 and Web Page Parsing
- Summary
4 LiveLab in this lesson — see the labs panel →
07 Learning the Hidden Secrets of Data Wrangling 5 topics · 5 LiveLab +
- Introduction
- Advanced List Comprehension and the zip Function
- Data Formatting
- Identify and Clean Outliers
- Summary
5 LiveLab in this lesson — see the labs panel →
08 Advanced Web Scraping and Data Gathering 6 topics · 6 LiveLab +
- 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)
- Summary
6 LiveLab in this lesson — see the labs panel →
09 RDBMS and SQL 5 topics · 7 LiveLab +
- Introduction
- Refresher of RDBMS and SQL
- Using an RDBMS (MySQL/PostgreSQL/SQLite)
- Reading Data from a Database in SQLite
- Summary
7 LiveLab in this lesson — see the labs panel →
10 Application of Data Wrangling in Real Life 4 topics · 1 LiveLab +
- Introduction
- Applying Your Knowledge to a Real-life Data Wrangling Task
- An Extension to Data Wrangling
- Summary
1 LiveLab in this lesson — see the labs panel →
Hands-On Labs Our edge
45 LiveLabs- Sorting a List
- Generating a List
- Deleting a Value from a Dictionary
- Accessing and Setting Values in a Dictionary
- Slicing a String
- Implementing a Queue
- Splitting a String
- Implementing Multi-Element Membership Checking
- Implementing a Stack
- Opening a File and Printing its Content
- Generating Arrays Using arange and linspace
- Multiplying Two Arrays
- Adding Two NumPy Arrays
- Creating a NumPy Array
- Filtering Elements from a Matrix
- Stacking Arrays
- Subsetting a DataFrame
- Grouping a DataFrame
- Dropping the Missing Values
- Replacing Missing Values in a DataFrame
- Joining DataFrames
- Concatenating Data Frames
- Counting Values
- Bypassing the Headers of a CSV File
- Reading Data from a CSV File
- Stacking URLs from a Document Using bs4
- Counting Tags
- Using the zip Function
- Using a One-Liner Generator Expression
- Using a Generator Expression
- Using the format Function
- Using a Box Plot
- Checking the Status of the Web Request
- Extracting Text from a Section
- Traversing an XML Tree
- Checking Whether the Input String Begins with a Specific Word
- Matching Pattern
- Finding the Number of Words in a List That End with ing
- Deleting the Data
- Using Joins
- Using the Foreign Key
- Updating Data
- Using the ORDER BY Clause
- Using the SELECT Statement
- Using the SELECT Statement
- Skipping the First Row of the Data Set
03 / FAQs
Questions before you start
How do I clean data using Python? +
Do I need prior programming experience to take a data wrangling course? +
What are the best Python libraries for data wrangling? +
The top Python libraries for data wrangling include:
Pandas: For data manipulation and analysis
NumPy: For numerical operations
Matplotlib and Seaborn: For data visualization
PyJanitor: For extended data cleaning functions
What is the difference between data cleaning and data wrangling? +
Data Cleaning is the process of identifying and correcting errors in the data.
Data Wrangling is a broader process that includes data cleaning, transforming, and mapping raw data into a more useful format for analysis.
What are some common data wrangling techniques? +
Common data wrangling techniques in Python include:
Data Merging: Combining multiple data sources into one dataset.
Data Transformation: Changing the format or structure of the data.
Data Subsetting: Selecting specific rows or columns of interest.
Handling Outliers: Identifying and correcting outliers in the data.
Data Aggregation: Summarizing data by grouping and calculating statistics.
What is the role of NumPy and Pandas in data wrangling? +
NumPy provides support for numerical operations on large, multi-dimensional arrays and matrices, which are essential for efficient data manipulation.
Pandas offers data structures and functions designed to make data manipulation and analysis easy, such as DataFrames for handling tabular data.
Which roles can I pursue after completing a data wrangling course?+
Career opportunities after completing our Python for data wrangling course include roles such as:
- Data Analyst
- Data Scientist
- Data Engineer
- Business Analyst
- ML Engineer
Cleaning & Transforming Data Simplified
Learn quick Python data wrangling techniques to turn raw data into clear, actionable insights.
- 1 year of full access
- 45 LiveLab included
- Certificate of completion
No credit card required