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.

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, students 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 bridges the gap between theoretical knowledge and practical application, enabling students to become proficient in using Python for comprehensive data analysis, from basic data manipulation to advanced statistical modeling and forecasting.

This course focuses on collecting and preprocessing real-world data, moving beyond the clean datasets that learners have encountered in earlier coursework. The core narrative is about handling data as it exists "in the wild" - messy, inconsistent, and coming from various sources.

The course includes three modules focusing on data from different sources: web scraping, APIs, and databases. It begins with web scraping, including how to extract data from websites using tools like Pandas and Beautiful Soup, while considering ethical implications. You’ll also learn how to clean and preprocess text data, the primary type of data you’ll encounter on the web. Module 2 introduces APIs, a method of getting real-time data from online sources. You’ll learn how to parse JSON data and authenticate your access with API keys. You’ll also explore broader data cleaning concepts, particularly around handling numerical data, including normalization and other techniques. The final module focuses on databases. You’ll learn how databases are structured, and how to access them using SQL queries. You’ll learn when to choose SQL versus Python for different data cleaning tasks. You’ll also cover the core join operations that allow you to combine database tables, which make up many interview questions. The course aims to prepare you for real-world scenarios where data rarely comes in a perfect, analysis-ready format.

In this course, you'll learn how to transform your data analysis into compelling stories that drive real business decisions. You'll move beyond just analyzing data to effectively communicating your findings in ways that resonate with different audiences.

You'll start by learning the fundamentals of data storytelling - how to craft narratives that give your data meaning and context. You'll discover how to choose the right medium for your message, whether that's a memo, presentation, or interactive dashboard, based on your audience's needs and the story you're trying to tell. Next, you'll dive into the art of presenting insights effectively. You'll explore various types of visualizations and learn when to use each one to make your data most impactful. You'll also get hands-on experience with Tableau, where you'll learn to create powerful interactive dashboards from start to finish. You'll master everything from connecting data sources to designing user-friendly interfaces that stakeholders can easily navigate. The course culminates in a capstone project where you'll put all your skills together. You'll work through a complete analysis workflow - cleaning and analyzing data with Python and SQL, creating visualizations with Seaborn, and crafting a compelling narrative around your findings. Throughout the course, you'll focus on real-world applications, learning to align your analysis with business objectives and translate technical findings into actionable insights.