Master Pandas: Real-World Data Cleaning, Feature Engineering, Visualisation, Statistical Analysis Challenges
What you'll learn
Be proficient in using the pandas library which includes data loading, exploring, cleaning and a variety of data manipulation techniques.
Develop proficiency in identifying and handling missing or inconsistent data, including techniques for folling, interpolating, and removing missing values.
Learn how to create new features (feature engineering) from existing data to enhance the dataset and improve the quality of the analysis.
Gain the ability to create insightful visualisations using pandas integrated plitting capabilities and additional libraries like Seaborn.
Understand how to perform various statistical tests to analyse the data, including descriptive statistics, correlation, and hypothesis testing.
Apply all the cocepts learned through practical, hands-on experience using a real-world dataset from Kaggle.
Requirements
Basic understanding of Python (data types: familiarity with fundamental data types such as integers, floats, strings, lists and dictoinaries; variables: understanding how to declate and use variables; loops: basic knowledge of for loops; conditional statements: basic knowledge of if-statements; functions: ability to write and call functions, including passing arguments and returning values).
Only a computer and an internet connection needed, as well as a Google account.
Description
Welcome to the "Pandas Masterclass: Hands-On Data Analysis Challenges"! This course is designed to bridge the gap between theoretical knowledge and practical application by immersing you in real-world data analysis from day one. Unlike traditional courses that focus on isolated examples, this masterclass centers around a single dataset that you'll work with throughout the course, mirroring the real-world experience of receiving and analysing data from a client.
You'll dive deep into hands-on coding with Pandas, tackling real-world challenges such as handling missing values, correcting parsing errors, and dealing with incorrectly formatted data. As you progress, you'll learn how to perform essential data analysis tasks, including feature engineering, data visualisation, and basic statistical correlation analysis.
What sets this course apart is its emphasis on truly understanding a dataset, just as you would in a real-world scenario. You'll not only gain technical skills but also develop an analytical mindset, enabling you to approach data problems with
confidence
and
creativity
.
This course is not a substitute for a university-level data analysis class but rather a powerful complement that enhances your practical skills. By the end, you'll have the confidence to handle messy datasets and extract meaningful insights—just like a professional data analyst in the field.
Who this course is for
Aspiring data analysts who want to start from the basics
Intermediate data analysis looking to reifne their skills and learn new techniques
University students who need practical experience with data analysis tools
Researchers and academics who want to incorporate data analysis into their work
Career changers looking to transition into a data-focused role
Hobbysists and enthusiasts with a general interest in data and want to understand how to analyse and interpret data using Python
Homepage
Screenshots

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