Probability & Statistics courses

5

In the final course of the statistical modeling for data science program, learners will study a broad set of more advanced statistical modeling tools. Such tools will include generalized linear models (GLMs), which will provide...

4.4

Define a composite hypothesis and the level of significance for a test with a composite null hypothesis. Define a test statistic, level of significance, and the rejection region for a hypothesis test. Give the form...

4.54

Welcome to the Advanced Linear Models for Data Science Class 2: Statistical Linear Models. This class is an introduction to least squares from a linear algebraic and mathematical perspective. Before beginning the class make sure...

4.6

Design and analyze experiments where some of the factors are randomDesign and analyze experiments where there are nested factors or hard-to-change factorsAnalyze experiments with covariatesDesign and analyze experiments with nonnormal response distri...

4.37

Calculate descriptive statistics and create graphical representations using R softwareSolve problems and make decisions using probability distributionsExplore the basics of sampling and sampling distributions with respect to statistical inferenceClas...

In this course, you will learn to perform state-of-the art predictive analytics using networked data in R. The aim of network analytics is to predict to which class a network node belongs, such as churner...

Simulations are a class of computational algorithms that use the relatively simple idea of random sampling to solve increasingly complex problems. Although they have been around for ages, they have gained in popularity recently due...

4.39

This course focuses on one of the most important tools in your data analysis arsenal: regression analysis. Using either SAS or Python, you will begin with linear regression and then learn how to adapt when...

One of the primary goals of any scientist is to find patterns in data and build models to describe, predict, and extract insight from those patterns. The most fundamental of these patterns is a linear...

Statistics is the science that deals with the collection, analysis, and interpretation of data. Having a solid foundation in statistics will help you effectively work with your data to test hypotheses and uncover insights that...

In this course you'll learn how to work with and visualize network data. You'll use the igraph package to create networks from edgelists and adjacency matrices. You'll also learn how to plot networks and their...

Probability is the study of making predictions about random phenomena. In this course, you'll learn about the concepts of random variables, distributions, and conditioning, using the example of coin flips. You'll also gain intuition for...

Scientists seek to answer questions using rigorous methods and careful observations. These observations—collected from the likes of field notes, surveys, and experiments—form the backbone of a statistical investigation and are called data. Statisti...

People make choices everyday. They choose products like orange juice or a car, decide who to vote for, and choose how to get to work. Marketers, retailers, product designers, political scientists, transportation planners, sociologists, and...

You rely on predictions every day: you might check the weather app before choosing your outfit or peek at the traffic before starting your commute. Perhaps you are responsible for setting your organization’s strategy in...

4.59

Welcome to Practical Time Series Analysis! Many of us are "accidental" data analysts. We trained in the sciences, business, or engineering and then found ourselves confronted with data for which we have no formal analytic...

Understand how to use, select, and interpret process control charts to identify special causes of variationCreate and interpret control charts for normal and non-normal distributionsCreate and interpret control charts for discrete dataAnalyze the cap...

In this course you'll take your skills with simple linear regression to the next level. By learning multiple and logistic regression techniques you will gain the skills to model and predict both numeric and categorical...

Bayesian data analysis is an approach to statistical modeling and machine learning that is becoming more and more popular. It provides a uniform framework to build problem specific models that can be used for both...

4.59

The capstone project will be an analysis using R that answers a specific scientific/business question provided by the course team. A large and complex dataset will be provided to learners and the analysis will require...