Data Science

Explore Data Science fundamentals and advanced topics for career growth.

Data Science Courses

Analysis of Survey Data from Complex Sample Designs
This course will teach you how to estimate descriptive quantities and sampling variances from complex surveys, and also how to fit linear and logistic regression models to complex sample survey data.
Customer Analytics in R
Throughout this course, you’ll engage in a complete customer analytics project using the R programming language, covering every stage from initiation to completion.
Forecasting Analytics
Enroll in this course to learn the process of selecting the right time series model, fitting it accurately, conducting diagnostics, and utilizing it effectively for forecasting purposes.
Generalized Linear Models
This course will explain the theory of generalized linear models (GLM), outline the algorithms used for GLM estimation, and explain how to determine which algorithm to use for a given data analysis.
Integer and Nonlinear Programming and Network Flow
This course will teach you a number of advanced topics in optimization: how to formulate and solve network flow problems; how to model and solve optimization problems; how to deal with multiple objectives in optimization problems, and techniques for handling optimization problems.
Interactive Data Visualization with Tableau
Enroll in this course to learn the process of selecting the right time series model, fitting it accurately, conducting diagnostics, and utilizing it effectively for forecasting purposes.
Introduction to Data Literacy
Learn how to understand and work with data using Excel.
Introduction to Network Analysis
This course will teach you a mix of quantitative and qualitative methods for describing, measuring, and analyzing social networks.
Mixed and Hierarchical Linear Models
This course covers fundamental principles of linear and non-linear mixed effects models, hierarchical linear models, estimation algorithms, and practical data analysis examples.
Multivariate Statistics
This course will teach you key multivariate procedures such as multivariate analysis of variance (MANOVA), principal components, factor analysis, and classification.
NLP and Deep Learning
In this course you will learn about deep neural networks, and how to use them in processing text with Python (Natural Language Processing or NLP).
Optimization with Linear Programming
Learn to apply mathematical models in managerial decision-making, including formulating linear programming models to meet multiple constraints.
Persuasion Analytics and Targeting
This course will teach you how to apply predictive modeling methods to identify persuadable individuals and to target voters in political campaigns.
Predictive Analytics – Project Capstone
Practical application of predictive modeling in predictive analytics course.
Predictive Analytics 1 – Machine Learning Tools
This course online outlines the fundamental concept of predictive modeling: classification and prediction.
Predictive Analytics 2 – Neural Nets and Regression
As a continuation of Predictive Analytics 1, this course introduces to the basic concepts in predictive analytics to visualize and explore predictive modeling.
Predictive Analytics 3 – Dimension Reduction, Clustering, and Association Rules
This course will teach you key unsupervised learning techniques of association rules – principal components analysis, and clustering – and will include an integration of supervised and unsupervised learning techniques.
Predictive Analytics for Healthcare
This course provides an overview of the foundational predictive modeling concept: classification and prediction, particularly focusing on its healthcare applications.
Predictive Analytics 1 – Machine Learning Tools
This online course introduces the basic paradigm of predictive modeling: classification and prediction.
Predictive Analytics 2 – Neural Nets and Regression
As a continuation of Predictive Analytics 1, this course introduces to the basic concepts in predictive analytics to visualize and explore predictive modeling.
Predictive Analytics 3 – Dimension Reduction, Clustering, and Association Rules

This course will teach you key unsupervised learning techniques of association rules – principal components analysis.

Responsible Data Science
Public and corporate concern about bias and other unintended harmful effects resulting from data science models has resulted in greater attention to the ethical practice of data science. This course,
Risk Simulation and Queuing
This online course covers three important modeling techniques. Students will learn how to (1) construct and implement simulations to model the uncertainty in decision input variables (e.g. price, demand, etc.)
SQL – Introduction to Database Queries
This course will teach you how to extract data from a relational database using SQL and merge data into a single file in R so that you can perform statistical operations.
Survival Analysis
This course will teach you the various methods used for modeling and evaluating survival data or time-to event data.

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