Probability & Statistics courses

4.75

Conduct a factorial experiment in blocks and construct and analyze a fractional factorial designApply the factorial concept to experiments with several factorsUse the analysis of variance for factorial designsUse the 2^k system of factorial designs

Have you taken DataCamp's Introduction to Network Analysis in Python course and are yearning to learn more sophisticated techniques to analyze your networks, whether they be social, transportation, or biological? Then this is the course...

4.67

By the end of this course, you will be able to: Approach complex industrial and business research problems and address them through a rigorous, statistically sound experimental strategyUse modern software to effectively plan experimentsAnalyze the...

In this course, you will learn the foundations of A/B testing, including hypothesis testing, experimental design, and confounding variables. You will also be exposed to a couple more advanced topics, sequential analysis and multivariate testing....

How do we get from data to answers? Exploratory data analysis is a process for exploring datasets, answering questions, and visualizing results. This course presents the tools you need to clean and validate data, to...

The most successful companies today are the ones that know their customers so well that they can anticipate their needs. Customer analytics and in particular A/B Testing are crucial parts of leveraging quantitative know-how to...

In this course, you will become an expert in fitting ARIMA models to time series data using R. First, you will explore the nature of time series data using the tools in the R stats...

Accurately predicting demand for products allows a company to stay ahead of the market. By knowing what things shape demand, you can drive behaviors around your products better. This course unlocks the process of predicting...

There has never been a better time to use R for spatial analysis! The sf package has made working with vector data in R a breeze and the raster package provides a set of powerful...

When your dataset is represented as a table or a database, it's difficult to observe much about it beyond its size and the types of variables it contains. In this course, you'll learn how to...

4.09

Identify characteristics of “good” estimators and be able to compare competing estimators. Construct sound estimators using the techniques of maximum likelihood and method of moments estimation. Construct and interpret confidence intervals for one an...

4.24

Understand the process of drawing conclusions about populations or scientific truths from dataDescribe variability, distributions, limits, and confidence intervalsUse p-values, confidence intervals, and permutation testsMake informed data analysis de...

This course for practicing and aspiring data scientists and statisticians. It is the fourth of a four-course sequence introducing the fundamentals of Bayesian statistics. It builds on the course Bayesian Statistics: From Concept to Data...

4.86

This course aims to help you to draw better statistical inferences from empirical research. First, we will discuss how to correctly interpret p-values, effect sizes, confidence intervals, Bayes Factors, and likelihood ratios, and how these...

From online social networks such as Facebook and Twitter to transportation networks such as bike sharing systems, networks are everywhere—and knowing how to analyze them will open up a new world of possibilities for you...

Data is all around us and can help us to understand many things. Making a pretty graph is great, but how can we tell the difference between a few outliers on a graph and a...

4.35

Learn fundamental concepts in data analysis and statistical inference, focusing on one and two independent samples.

You've taken a survey (or 1000) before, right? Have you ever wondered what goes into designing a survey and how survey responses are turned into actionable insights? Of course you have! In Analyzing Survey Data...

This course is designed to get you up to speed with the most important and powerful methodologies in statistics.

4.44

Use regression analysis, least squares and inferenceUnderstand ANOVA and ANCOVA model casesInvestigate analysis of residuals and variabilityDescribe novel uses of regression models such as scatterplot smoothing