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Our Courses
The Power of Statistics
This is the fourth of seven courses in the Google Advanced Data Analytics Certificate. In this course, you’ll discover how data professionals use statistics to analyze data and gain important insights. You'll explore key concepts such as descriptive and inferential statistics, probability, sampling, confidence intervals, and hypothesis testing. You'll also learn how to use Python for statistical analysis and practice communicating your findings like a data professional.
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Course by
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Self Paced
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37 hours
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English
Analytic Combinatorics
Analytic Combinatorics teaches a calculus that enables precise quantitative predictions of large combinatorial structures. This course introduces the symbolic method to derive functional relations among ordinary, exponential, and multivariate generating functions, and methods in complex analysis for deriving accurate asymptotics from the GF equations.
All the features of this course are available for free. It does not offer a certificate upon completion.
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Course by
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Self Paced
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17 hours
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English
Resampling, Selection and Splines
"Statistical Learning for Data Science" is an advanced course designed to equip working professionals with the knowledge and skills necessary to excel in the field of data science. Through comprehensive instruction on key topics such as shrink methods, parametric regression analysis, generalized linear models, and general additive models, students will learn how to apply resampling methods to gain additional information about fitted models, optimize fitting procedures to improve prediction accuracy and interpretability, and identify the benefits and approach of non-linear models.
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Course by
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Self Paced
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16 hours
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English
Calculus through Data & Modeling: Applying Differentiation
As rates of change, derivatives give us information about the shape of a graph. In this course, we will apply the derivative to find linear approximations for single-variable and multi-variable functions. This gives us a straightforward way to estimate functions that may be complicated or difficult to evaluate. We will also use the derivative to locate the maximum and minimum values of a function. These optimization techniques are important for all fields, including the natural sciences and data analysis.
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Course by
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Self Paced
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7 hours
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English
Data Science Fundamentals for Data Analysts
In this course we're going to guide you through the fundamental building blocks of data science, one of the fastest-growing fields in the world!
With the help of our industry-leading data scientists, we’ve designed this course to build ready-to-apply data science skills in just 15 hours of learning. First, we’ll give you a quick introduction to data science - what it is and how it is used to solve real-world problems. For the rest of the course, we'll teach you the skills you need to apply foundational data science concepts and techniques to solve these real-world problems.
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Course by
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Self Paced
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19 hours
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English
Introduction to Predictive Modeling
Welcome to Introduction to Predictive Modeling, the first course in the University of Minnesota’s Analytics for Decision Making specialization.
This course will introduce to you the concepts, processes, and applications of predictive modeling, with a focus on linear regression and time series forecasting models and their practical use in Microsoft Excel. By the end of the course, you will be able to:
- Understand the concepts, processes, and applications of predictive modeling.
- Understand the structure of and intuition behind linear regression models.
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Course by
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Self Paced
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12 hours
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English
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.
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Course by
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Self Paced
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9 hours
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English
Building Students Math Skills with iKnowit
By the end of this project, you will be fully prepared to use iKnowit with your students to improve their math skills.
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Course by
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Self Paced
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3 hours
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English
Mathematical Biostatistics Boot Camp 1
This class presents the fundamental probability and statistical concepts used in elementary data analysis. It will be taught at an introductory level for students with junior or senior college-level mathematical training including a working knowledge of calculus. A small amount of linear algebra and programming are useful for the class, but not required.
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Course by
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Self Paced
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13 hours
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English
Calculus: Single Variable Part 4 - Applications
Calculus is one of the grandest achievements of human thought, explaining everything from planetary orbits to the optimal size of a city to the periodicity of a heartbeat. This brisk course covers the core ideas of single-variable Calculus with emphases on conceptual understanding and applications. The course is ideal for students beginning in the engineering, physical, and social sciences.
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Course by
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Self Paced
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21 hours
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English
Essential Linear Algebra for Data Science
Are you interested in Data Science but lack the math background for it? Has math always been a tough subject that you tend to avoid? This course will teach you the most fundamental Linear Algebra that you will need for a career in Data Science without a ton of unnecessary proofs and concepts that you may never use. Consider this an expressway to Data Science with approachable methods and friendly concepts that will guide you to truly understanding the most important ideas in Linear Algebra.
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Course by
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Self Paced
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8 hours
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English
Algebra: Elementary to Advanced - Functions & Applications
After completing this course, students will learn how to successfully apply functions to model different data and real world occurrences. This course reviews the concept of a function and then provide multiple examples of common and uncommon types of functions used in a variety of disciplines. Formulas, domains, ranges, graphs, intercepts, and fundamental behavior are all analyzed using both algebraic and analytic techniques. From this core set of functions, new functions are created by arithmetic operations and function composition.
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Course by
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Self Paced
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6 hours
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English
Simulation Models for Decision Making
This course is primarily aimed at third- and fourth-year undergraduate students or graduate students interested in learning simulation techniques to solve business problems.
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Course by
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Self Paced
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17 hours
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English
Probability and Statistics: To p or not to p?
We live in an uncertain and complex world, yet we continually have to make decisions in the present with uncertain future outcomes. Indeed, we should be on the look-out for "black swans" - low-probability high-impact events.
To study, or not to study? To invest, or not to invest? To marry, or not to marry?
While uncertainty makes decision-making difficult, it does at least make life exciting! If the entire future was known in advance, there would never be an element of surprise. Whether a good future or a bad future, it would be a known future.
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Course by
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Self Paced
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16 hours
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English
Differential Equations Part III Systems of Equations
This introductory courses on (Ordinary) Differential Equations are mainly for the people, who need differential equations mostly for the practical use in their own fields. So we try to provide basic terminologies, concepts, and methods of solving various types of differential equations as well as a rudimentary but indispensable knowledge of the underlying theory and some related applications.
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Course by
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Self Paced
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10 hours
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English
Calculus: Single Variable Part 3 - Integration
Calculus is one of the grandest achievements of human thought, explaining everything from planetary orbits to the optimal size of a city to the periodicity of a heartbeat. This brisk course covers the core ideas of single-variable Calculus with emphases on conceptual understanding and applications. The course is ideal for students beginning in the engineering, physical, and social sciences.
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Course by
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Self Paced
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17 hours
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English
Factorial and Fractional Factorial Designs
Many experiments in engineering, science and business involve several factors. This course is an introduction to these types of multifactor experiments. The appropriate experimental strategy for these situations is based on the factorial design, a type of experiment where factors are varied together. This course focuses on designing these types of experiments and on using the ANOVA for analyzing the resulting data. These types of experiments often include nuisance factors, and the blocking principle can be used in factorial designs to handle these situations.
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Course by
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Self Paced
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12 hours
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English
Improving Math Engagement with Prodigy
By the end of this project, you will have discovered an excellent math website that has been proven to increase student engagement and even improve their math skills.
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Course by
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Self Paced
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3 hours
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English
Random Models, Nested and Split-plot Designs
Many experiments involve factors whose levels are chosen at random. A well-know situation is the study of measurement systems to determine their capability. This course presents the design and analysis of these types of experiments, including modern methods for estimating the components of variability in these systems. The course also covers experiments with nested factors, and experiments with hard-to-change factors that require split-plot designs.
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Course by
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Self Paced
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9 hours
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English
Causal Inference
This course offers a rigorous mathematical survey of causal inference at the Master’s level.
Inferences about causation are of great importance in science, medicine, policy, and business. This course provides an introduction to the statistical literature on causal inference that has emerged in the last 35-40 years and that has revolutionized the way in which statisticians and applied researchers in many disciplines use data to make inferences about causal relationships.
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Course by
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Self Paced
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13 hours
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English
Differential Equations Part II Series Solutions
This introductory courses on (Ordinary) Differential Equations are mainly for the people, who need differential equations mostly for the practical use in their own fields. So we try to provide basic terminologies, concepts, and methods of solving various types of differential equations as well as a rudimentary but indispensable knowledge of the underlying theory and some related applications.
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Course by
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Self Paced
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14 hours
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English
Image and Video Processing: From Mars to Hollywood with a Stop at the Hospital
In this course, you will learn the science behind how digital images and video are made, altered, stored, and used. We will look at the vast world of digital imaging, from how computers and digital cameras form images to how digital special effects are used in Hollywood movies to how the Mars Rover was able to send photographs across millions of miles of space.
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Course by
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Self Paced
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21 hours
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English
Differential Equations Part I Basic Theory
This introductory courses on (Ordinary) Differential Equations are mainly for the people, who need differential equations mostly for the practical use in their own fields. So we try to provide basic terminologies, concepts, and methods of solving various types of differential equations as well as a rudimentary but indispensable knowledge of the underlying theory and some related applications.
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Course by
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Self Paced
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15 hours
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English
Games without Chance: Combinatorial Game Theory
This course will cover the mathematical theory and analysis of simple games without chance moves.
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Course by
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Self Paced
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14 hours
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English
Probability Theory: Foundation for Data Science
Understand the foundations of probability and its relationship to statistics and data science. We’ll learn what it means to calculate a probability, independent and dependent outcomes, and conditional events. We’ll study discrete and continuous random variables and see how this fits with data collection. We’ll end the course with Gaussian (normal) random variables and the Central Limit Theorem and understand its fundamental importance for all of statistics and data science.
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Course by
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Self Paced
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48 hours
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English