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Case Studies: Network Analysis in R
Apply fundamental concepts in network analysis to large real-world datasets in 4 different case studies.
Building Response Models in R
Learn to build simple models of market response to increase the effectiveness of your marketing plans.
Introduction to Anomaly Detection in R
Learn statistical tests for identifying outliers and how to use sophisticated anomaly scoring algorithms.
Mixture Models in R
Learn mixture models: a convenient and formal statistical framework for probabilistic clustering and classification.
Predictive Analytics using Networked Data in R
Learn to predict labels of nodes in networks using network learning and by extracting descriptive features from the network
Choice Modeling for Marketing in R
Learn to analyze and model customer choice data in R.
Probability Puzzles in R
Learn strategies for answering probability questions in R by solving a variety of probability puzzles.
Forecasting Product Demand in R
Learn how to identify important drivers of demand, look at seasonal effects, and predict demand for a hierarchy of products from a real world example.
Multivariate Probability Distributions in R
Learn to analyze, plot, and model multivariate data.
Bayesian Modeling with RJAGS
In this course, youll learn how to implement more advanced Bayesian models using RJAGS.
Intermediate Network Analysis in Python
Analyze time series graphs, use bipartite graphs, and gain the skills to tackle advanced problems in network analytics.
Case Study: Analyzing City Time Series Data in R
Strengthen your knowledge of the topics covered in Manipulating Time Series in R using real case study data.
Case Studies in Statistical Thinking
Take vital steps towards mastery as you apply your statistical thinking skills to real-world data sets and extract actionable insights from them.
Bayesian Regression Modeling with rstanarm
Learn how to leverage Bayesian estimation methods to make better inferences about linear regression models.
Inference for Numerical Data in R
In this course youll learn techniques for performing statistical inference on numerical data.
Error and Uncertainty in Google Sheets
Learn to distinguish real differences from random noise, and explore psychological crutches we use that interfere with our rational decision making.
ChIP-seq with Bioconductor in R
Learn how to analyse and interpret ChIP-seq data with the help of Bioconductor using a human cancer dataset.
Inference for Categorical Data in R
In this course youll learn how to leverage statistical techniques for working with categorical data.
Differential Expression Analysis with limma in R
Learn to use the Bioconductor package limma for differential gene expression analysis.
Nonlinear Modeling with Generalized Additive Models (GAMs) in R
GAMs model relationships in data as nonlinear functions that are highly adaptable to different types of data science problems.
A/B Testing in R
Learn the basics of A/B testing in R, including how to design experiments, analyze data, predict outcomes, and present results through visualizations.
Discrete Event Simulation in Python
Discover the power of discrete-event simulation in optimizing your business processes. Learn to develop digital twins using Pythons SimPy package.
Analyzing Survey Data in Python
Learn how to analyze survey data with Python and discover when it is appropriate to apply statistical tools that are descriptive and inferential in nature.
Inference for Linear Regression in R
In this course youll learn how to perform inference using linear models.