The 10 best Statistics courses in 2026

This course takes a deep dive into the statistical foundation upon which data analytics is built. The first part of this course will help you to thoroughly understand your dataset and what the data actually means. Then, it will go into sampling including how to ask specific questions about your data and how to conduct analysis to answer those questions. Many of the mistakes made by data analysts today are due to a lack of understanding the concepts behind the tests they run, leading to incorrect tests or misinterpreting the results. This course is tailored to provide you with the necessary ba

By the end of this course, learners will be able to apply statistical data analytics techniques using Minitab and Excel, interpret business data through descriptive and inferential methods, evaluate relationships using correlation and ANOVA, and support data-driven business decisions through real-world case studies. This course provides a practical, industry-focused introduction to data analytics using Minitab, designed for learners who want to move beyond theory and develop job-ready analytical skills. Learners will explore core statistical concepts such as descriptive statistics, hypothesis

This is the first course of a six part specialization.  You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. This first course in the IBM AI Enterprise Workflow Certification specialization introduces you to the scope of the specialization and prerequisites.  Specifically, the courses in this specialization are meant for practicing data scientists who are knowledgeable about probability, statistics, linear algebra, and Python tooling for data science and machine lear

The course "Computational and Graphical Models in Probability" equips learners with essential skills to analyze complex systems through simulation techniques and network analysis. By exploring advanced concepts such as Exponential Random Graph Models and Probabilistic Graphical Models, students will learn to model and interpret intricate social structures and dependencies within data. What sets this course apart is its emphasis on practical applications using the R programming language, empowering students to simulate random variables effectively and construct sophisticated models for real-wo

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

Learn how to reduce process waste, improve operational performance, and make data-driven decisions using Lean Six Sigma methodologies. This comprehensive course equips learners with practical skills in process measurement, statistical analysis, root cause identification, process optimization, and organizational change management. The course begins with the foundations of Lean Six Sigma, introducing value creation, waste elimination, quality improvement principles, and the role of Lean thinking in driving operational excellence. Learners will understand how Lean and Six Sigma work together to

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