The 10 best Statistics courses in 2026

Welcome to the Advanced Linear Models for Data Science Class 1: Least Squares. This class is an introduction to least squares from a linear algebraic and mathematical perspective. Before beginning the class make sure that you have the following: - A basic understanding of linear algebra and multivariate calculus. - A basic understanding of statistics and regression models. - At least a little familiarity with proof based mathematics. - Basic knowledge of the R programming language. After taking this course, students will have a firm foundation in a linear algebraic treatment of regression mo

The U.S. Bureau of Labor Statistics projects about 12-13% job growth for computer systems and network architects in the coming years. This course is your first step toward entering the field of IT systems architecture. You’ll begin looking at systems and solutions architects’ roles, responsibilities, and skills, and exploring career paths with certifications to enhance your expertise. You'll then explore systems thinking, identify key system components, and learn how they interact within an organization. You'll also analyze business processes and apply the systems development life cycle (S

In this course, we'll make predictions on product usage and calculate optimal safety stock storage. We'll start with a time series of shoe sales across multiple stores on three different continents. To begin, we'll look for unique insights and other interesting things we can find in the data by performing groupings and comparing products within each store. Then, we'll use a seasonal autoregressive integrated moving average (SARIMA) model to make predictions on future sales. In addition to making predictions, we'll analyze the provided statistics (such as p-score) to judge the viability of usin

Data is one of the most valuable assets your organization holds, and it is also one of its greatest liabilities. Every customer record, employee file, and behavioral data point creates legal, ethical, and operational obligations, and the consequences of getting privacy wrong range from regulatory sanctions to permanent damage to the trust your customers place in you. In this course, you'll identify what counts as personal data across your organization, classify it by sensitivity, and map every third party that touches it. You'll assess data processing risks using a structured probability-and-

This course can also be taken for academic credit as ECEA 5630, part of CU Boulder’s Master of Science in Electrical Engineering degree. This course introduces basic concepts of quantum theory of solids and presents the theory describing the carrier behaviors in semiconductors. The course balances fundamental physics with application to semiconductors and other electronic devices. At the end of this course learners will be able to: 1. Understand the energy band structures and their significance in electric properties of solids 2. Analyze the carrier statistics in semiconductors 3. Analyze t

This course is intended for students looking to create a solid algebraic foundation of fundamental mathematical concepts from which to take more advanced courses that use concepts from precalculus, calculus, probability, and statistics. This course will help solidify your computational methods, review algebraic formulas and properties, and apply these concepts model real world situations. This course is for any student who will use algebraic skills in future mathematics courses. Topics include: the real numbers, equalities, inequalities, polynomials, rational expressions and equations, graphs,

This course will cover the Measure phase and portions of the Analyze phase of the Six Sigma DMAIC (Define, Measure, Analyze, Improve, and Control) process. You will learn about lean tools for process analysis, failure mode and effects analysis (FMEA), measurement system analysis (MSA) and gauge repeatability and reproducibility (GR&R), and you will be introduced to basic statistics. This course will outline useful measure and analysis phase tools and will give you an overview of statistics as they are related to the Six Sigma process. The statistics module will provide you with an overview o

Survey Samples: Size and Methods is a foundational course for aspiring market research analysts and professionals designing statistically sound studies. It builds essential quantitative skills to move from guesswork to confident, data-driven research design. You will learn the key differences between probability and non-probability sampling and how to select the proper method for any research objective. The course emphasizes practical application, guiding you through calculating valid sample sizes using confidence levels and margins of error, with hands-on practice using a sample size calcula

In this course, you will learn the basics of understanding the data you have and why correctly classifying data is the first step to making correct decisions. You will describe data both graphically and numerically using descriptive statistics and R software. You will learn four probability distributions commonly used in the analysis of data. You will analyze data sets using the appropriate probability distribution. Finally, you will learn the basics of sampling error, sampling distributions, and errors in decision-making. This course can be taken for academic credit as part of CU Boulder’s M

Introduction to Financial Engineering and Risk Management course belongs to the Financial Engineering and Risk Management Specialization and it provides a fundamental introduction to fixed income securities, derivatives and the respective pricing models. The first module gives an overview of the prerequisite concepts and rules in probability and optimization. This will prepare learners with the mathematical fundamentals for the course. The second module includes concepts around fixed income securities and their derivative instruments. We will introduce present value (PV) computation on fixed i