Applied Statistics for Data Analytics

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 fo