Overview

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Throughout this course, you will learn the fundamental statistical concepts, analyses, and visualizations that serve as the foundation for a career as a data analyst. Whether you're new to statistics or looking to refresh your skills, this course will equip you with powerful techniques to extract meaningful insights from your data. By the end of this course, you will feel more confident and capable of implementing rigorous statistical analyses in your career as a data analyst! In the first module, you’ll explore the essential building blocks of statistics that enable rigorous data analysis. By the end, you’ll be able to define populations, samples, and sampling methods; characterize datasets using measures of central tendency, variability, and skewness; use correlation to understand relationships between features; and employ segmentation to reveal insights about different groups within your data. You’ll apply these concepts to real-world scenarios: analyzing movie ratings and durations over time, explaining customer behavior, and exploring healthcare outcomes. In the second module, you’ll cover key probability rules and concepts like conditional probability and independence, all with real-world examples you’ll encounter as a data analyst. Then you’ll explore probability distributions, both discrete and continuous. You'll learn about important distributions like the binomial and normal distributions, and how they model real-world phenomena. You’ll also see how you can use sample data to understand the distribution of your population, and how to answer common business questions like how common are certain outcomes or ranges of outcomes? Finally, you’ll get hands on with simulation techniques. You'll see how to generate random data following specific distributions, allowing you to model complex scenarios and inform decision-making. In modules 3 and 4, you'll learn powerful techniques to draw conclusions about populations based on sample data. This is your first foray into inferential statistics. You’ll start by constructing confidence intervals - a way to estimate population parameters like means and proportions with a measure of certainty. You'll learn how to construct and interpret these intervals for both means and proportions. You’ll also visualize how this powerful technique helps you manage the inherent uncertainty when investigating many business questions. Next, you’ll conduct hypothesis testing, a cornerstone of statistical inference that helps you determine whether an observed difference reflects random variation or a true difference. You'll discover how to formulate hypotheses, calculate test statistics, and interpret p-values to make data-driven decisions. You’ll learn tests for means and proportions, as well as how to compare two samples. Throughout the course, you’ll use large language models as a thought partner for descriptive and inferential statistics. You'll see how AI can help formulate hypotheses, interpret results, and even perform calculations and create visualizations for those statistics.

Syllabus

Foundational statistical techniques

This module introduces core statistical concepts and techniques used to explore, summarize, and analyze data. Learners will start with examining sampling methods, best practices, and potential biases. They will also see how to use GenAI to troubleshoot spreadsheet formulas and errors to enhance their analytical workflows. Moreover, they will apply measures of central tendency, variability, and skewness to interpret data distributions and visualize insights using histograms, box plots, and bar charts. Lastly, the module will show how to conduct correlation analysis and data segmentation using spreadsheets.

Probability and simulation

This module covers fundamental probability concepts and their applications in data analysis and decision-making. Learners will explore probability rules, distributions, and key statistical principles used to quantify uncertainty. They will distinguish between different types of events, compare discrete and continuous distributions, and apply the normal distribution to real-world datasets. The module also introduces simulation techniques, including random variate generation, to model uncertainty and support data-driven decisions.