What you'll learn

Skills you'll gain

Professional Certificate - 5 course series

Future-ready analytics skills. Gain experience in AI-augmented workflows, preparing you for the cutting edge of data analytics, using AI to help speed up and improve analysis.

This course is the first in a series designed to prepare you for an entry level data analyst role. You don’t need any prior experience with analytics software, programming, or even data to succeed in this course. Whether you’re looking to start a career in data analytics or level up in your current role, this course is for you. It’s designed to take you from no prior experience to leading your own end to end projects. And, if you’re already working as a data analyst or in a similar role, you’ll find new strategies and insights to continue growing in your career. Starting out, you’ll learn what data is & the many forms it can take. Then, you’ll get hands on with spreadsheets, a powerful tool for analyzing and visualizing data. You’ll explore real-world datasets throughout the video demos and the interactive labs, including hotel bookings, baby names, and home sales. Finally, you’ll learn a structured approach for data analytics projects that works across industries. Plus, throughout this course, you’ll get hands-on with large language models, which are changing the nature of work. They are not a replacement for your perspective, but they can augment your skills, serving as a thought partner for your practice. In this course, you’ll use LLMs to interpret data visualizations, run analyses, and more. Data analytics is both analytical and creative. While you will crunch numbers, and that’s fun in its own right, you’ll also craft compelling stories to inspire action. You’ll discover new things every day, work with people from all backgrounds, and see the real world impacts of your expertise.

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.

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.

Applied Statistics for Data Analytics

Python for Data Analytics

Data I/O and Preprocessing with Python and SQL

Data Storytelling