Data Science Fundamentals Part 2: Unit 1
By External Provider
About this course
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.
What you will 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.