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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鈥檒l explore the essential building blocks of statistics that enable rigorous data analysis. By the end, you鈥檒l 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鈥檒l 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鈥檒l cover key probability rules and concepts like conditional probability and independence, all with real-world examples you鈥檒l encounter as a data analyst. Then you鈥檒l 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鈥檒l 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鈥檒l 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鈥檒l 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鈥檒l also visualize how this powerful technique helps you manage the inherent uncertainty when investigating many business questions. Next, you鈥檒l 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鈥檒l learn tests for means and proportions, as well as how to compare two samples. Throughout the course, you鈥檒l 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.

This comprehensive course guides students through the complete data analytics workflow using Python, combining programming fundamentals with advanced statistical analysis. The curriculum is structured across five interconnected modules that build upon each other, using real-world datasets to provide practical, hands-on experience.

Starting with programming fundamentals, you'll learn essential Python concepts while working with real datasets like public library revenue and restaurant safety inspections. The course introduces the Jupyter Notebook environment and transitions students from spreadsheet-based analysis to powerful programmatic approaches. Students master core programming concepts including variables, functions, and control flow structures. This course helps you bridge the gap between theoretical knowledge and practical application, enabling you to become proficient in using Python for comprehensive data analysis, from basic data manipulation to advanced statistical modeling and forecasting.