Data Science Fundamentals Part 2: Unit 2

Overview

Thsi course explores foundational and advanced techniques for making reliable inferences from data, starting with a the history and evolution of statistical analysis. Through hands-on lessons, you’ll learn how to leverage computational and sampling-based methods to draw meaningful conclusions, and gain practical experience with hypothesis testing—a cornerstone skill for optimizing digital experiences, such as through A/B testing. The course emphasizes the importance of understanding and quantifying uncertainty, equipping you with the tools to interpret confidence intervals and make well-informed decisions. You’ll also tackle the critical distinction between correlation and causation, ensuring your analyses are robust and actionable. Whether you’re looking to enhance your analytical toolkit or drive impactful business outcomes, this course teaches essential skills for today’s data-centric world.

Syllabus

Data Science Fundamentals Part 2: Unit 2

This module introduces key concepts in statistical inference, focusing on estimation, hypothesis testing, and evaluation. You’ll explore foundational and modern techniques for drawing conclusions from data, including computational and sampling-based methods. The lessons cover hypothesis tests, confidence intervals, and practical applications like A/B testing for web optimization. Emphasis is placed on understanding uncertainty and distinguishing correlation from causation, equipping you with essential tools for robust data analysis.