// 36 courses indexed
The course directory
Every course we track across 10+ providers, in one searchable index. Want a shortlist picked for your goals? Run a free skill gap analysis.
Business Statistics and Analysis Capstone
The Business Statistics and Analysis Capstone is an opportunity to apply various skills developed across the four courses in the specialization to a real life data. The Capstone, in collaboration with an industry partner uses publicly available ‘Housing Data’ to pose various questions typically a client would pose to a data analyst. Your job is to do the relevant statistical analysis and report your findings in response to the questions in a way that anyone can understand. Please remember that this is a Capstone, and has a degree of difficulty/ambiguity higher than the previous four courses. T
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Using probability distributions for real world problems in R
By the end of this project, you will learn how to apply probability distributions to solve real world problems in R, a free, open-source program that you can download. You will learn how to answer real world problems using the following probability distributions – Binomial, Poisson, Normal, Exponential and Chi-square. You will also learn the various ways of visualizing these distributions of real world problems. By the end of this project, you will become confident in understanding commonly used probability distributions through solving practical problems and you will strengthen your core conc
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Variable Selection, Model Validation, Nonlinear Regression
If you have a technical background in mathematics/statistics/computer science/engineering and or are pursuing a career change to jobs or industries that are data-driven, this course is for you. Those industries might be finance, retail, tech, healthcare, government, or many others. The opportunity is endless. This course will focus on getting you acquainted with the generalized linear model (GLM) through the examples of logistic and Poisson regression. You will also see how simple and multiple linear regression relates to GLM using the link function. We will also study a regression technique
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Predictive Analytics: Apply, Analyze & Interpret
Build practical predictive analytics skills and learn to turn complex data into actionable insights for business, finance, and research. You’ll begin with predictive modeling foundations, regression approaches, ANOVA, control charts, and Minitab before examining observations, NAV results, descriptive statistics, and case-based datasets involving customer complaints, health indicators, and loan applicants. You’ll learn to select, apply, and interpret t-tests, ANOVA, and chi-square tests; evaluate variable relationships through correlation analysis; and use Minitab to interpret statistical resu
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Advanced Statistical Analysis and Tools
This course is designed to guide you on how to prepare for the American Society for Quality Certified Six Sigma Black Belt (ASQ CSSBB) certification, a mark of quality excellence across industries. The course offers insights into the statistical tools for drawing valid conclusions, depicting relationships, analyzing measurement systems, testing hypotheses, designing experiments, applying statistical process control, and leading advanced Six Sigma projects across the enterprise. By the end of this course, you will be able to: - Identify the statistical analysis tools required for
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Foundations of Data Science: K-Means Clustering in Python
Organisations all around the world are using data to predict behaviours and extract valuable real-world insights to inform decisions. Managing and analysing big data has become an essential part of modern finance, retail, marketing, social science, development and research, medicine and government. This MOOC, designed by an academic team from Goldsmiths, University of London, will quickly introduce you to the core concepts of Data Science to prepare you for intermediate and advanced Data Science courses. It focuses on the basic mathematics, statistics and programming skills that are necessary
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Six Sigma Tools for Analyze
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
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Survey Samples: Size and Methods
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
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Managing, Describing, and Analyzing Data
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
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Introduction to Financial Engineering and Risk Management
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
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Lean Six Sigma for IT Process Improvement
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
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Computational and Graphical Models in Probability
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
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Apply Data Analytics with Minitab for Business Decisions
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
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AI Workflow: Business Priorities and Data Ingestion
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
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Statistics Foundations
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
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Introduction to Systems Architecture
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
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Capstone Project: Predicting Safety Stock
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
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Practical Privacy for Products and Services
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-
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Semiconductor Physics
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
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Algebra: Elementary to Advanced - Equations & Inequalities
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,
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Advanced Linear Models for Data Science 1: Least Squares
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
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Statistical Tests for Market Research
This course builds essential statistical analysis capabilities for market research professionals. Learners will develop a strong understanding of statistical package functionality and master techniques for comparing group differences through hypothesis testing. You'll move beyond raw data to defensible insights by learning not just how to run a statistical test, but why it matters and what it means for the business. Through practical, hands-on application in real-world scenarios—such as A/B testing and customer satisfaction analysis—you will build the analytical skills needed to draw statisti
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Building the Core Mechanics of a Pinball Game in UE
Build a fully playable Pinball game in Unreal Engine using Blueprint visual scripting while developing practical game mechanics, interactive systems, and polished user interfaces. Through four hands-on modules, you'll progress from setting up a pinball project and implementing physics-based ball behavior to creating bumpers, flippers, plungers, scoring systems, and responsive player controls. You'll also develop dynamic score displays, player statistics, end-game conditions, high score tracking, and a professional main menu with optimized UI performance. Along the way, you'll apply Blueprint
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Risk Analysis and Stakeholder Engagement
Master quantitative risk analysis and stakeholder management techniques to improve project decision-making, collaboration, and performance. This course provides a comprehensive understanding of how to measure uncertainty, forecast project outcomes, and effectively engage stakeholders throughout the project lifecycle. The course begins with the foundations of Quantitative Risk Management (QRM), introducing learners to structured methodologies for measuring, analyzing, and documenting risks. Learners will explore probability-based approaches, risk assessment frameworks, and data-driven techniqu
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