This course teaches how to ask the right questions and extract meaningful insights from complex datasets. Through hands-on lessons, you’ll master industry-standard Python libraries such as matplotlib and seaborn, enabling you to create compelling visualizations like histograms, boxplots, and scatter plots. You'll learn to uncover patterns, relationships, and correlations within your data, and develop a critical eye for interpreting results. You'll also learn about the pitfalls of data interpretation, including the impact of mixed effects and the nuances of Simpson’s Paradox, ensuring you can navigate and communicate findings with confidence. Whether you’re a beginner or looking to deepen your analytical toolkit, this course will empower you to transform raw data into actionable knowledge.
Data Science Fundamentals Part 2: Unit 1
This course is part of Data Science Fundamentals, Part 2 Specialization
Instructor: Pearson
Access provided by Kalinga Institute of Industrial Technology
Recommended experience
What you'll learn
Gain a foundational understanding of Exploratory Data Analysis (EDA) and its historical context.
Develop practical skills in Python data visualization using matplotlib and seaborn.
Learn to identify and interpret relationships and correlations within datasets using advanced charting techniques.
Recognize and avoid common pitfalls in data analysis, including mixed effects and Simpson’s Paradox.
Skills you'll gain
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August 2025
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There is 1 module in this course
This module introduces Exploratory Data Analysis (EDA), emphasizing its historical context and importance in asking the right questions of data. Learners will use Python’s matplotlib and seaborn libraries to visualize and analyze data, starting with single-variable plots like histograms and boxplots, then advancing to multi-dimensional visualizations such as scatter plots. The module also covers identifying relationships and correlations between variables, and concludes with a discussion of statistical pitfalls like Simpson’s Paradox, highlighting the need for careful interpretation of data.
What's included
20 videos1 assignment
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