Course Descriptions

DATA 800 — Introduction to Applied Analytics Statistics

Course Contents:
  • Examining distributions, descriptive statistics including measures of central tendency and variations, and advanced visualizations of data.
  • Examining relationships, correlations, measures of associations amongst categorical data, non-parametric tests.
  • Survey and surveillance methods.
  • Probability and Sampling distributions (Poisson, Binomial and Normal).
  • Introduction to statistical inference, central limit theorem, distribution of means and proportions.
  • Inference on one and two population parameters including means and proportions.
  • Experimental designs and analysis of variance.
  • Simple linear regression and multiple regression.

DATA 801 — Foundations of Data Analytics

Course Contents:
  • Exploratory Data Analysis
  • Multiple Linear Regression and diagnostics
  • Multiple Linear Regression and diagnostics
  • Intro to design of experiments (ANOVA)
  • Intro to Time Series
  • Analysis of contingency tables and non-parametric techniques
  • Data sleuths/abuse of statistical tools and techniques
  • Statistics Assessments
  • Written and computer application

DATA 802 — Analytical Tools and Foundations

Course Contents:
  • Programming and Data
  • Intro to Data cleaning and restructuring
  • SAS Programming
  • SPSS
  • SQL Programming
  • R Programming
  • JMP
  • Advanced Excel
  • Visualization (Excel, Tableau, SAS VA, GIS, Piktochart)

DATA 803 — Introduction to Analytics Applications

Course Contents:
  • Data Mining
  • Text Mining
  • Multivariate and Logistic regression
  • Multivariate Techniques
  • Customer analytics and Segmentation
  • Multivariate Techniques
  • Web Analytics
  • Simulation

DATA 900 — Data Architecture

Course Contents:

The following are the major topic areas to be covered:

  • Data Cleaning and management
  • Data mining
  • Data Storage
  • Data Warehousing
  • Data Security and privacy
  • Machine Learning
  • Machine Learning
  • Big Data
  • SAS programming including loops, arrays, macros, ODS.

DATA 901 — Analytics Applications I

Course Contents:

The following are the major topic areas to be covered:

  • R Visualization
  • SAS Visual analytics
  • Tableau and Piktochart
  • SAS Enterprise Miner
  • SAS Text Miner
  • Sentiment Analysis
  • Customer analytics and segmentation
  • Financial analytics
  • Optimization
  • Risk analytics

DATA 911 — Analytics Practicum I

Course Contents:

The following are the major topic areas to be covered in the practicum via exercises, case studies or through the project:

  • Project Overview
  • Project Management
  • Data Privacy and Security
  • Legal Issues
  • Data Visualization
  • Geospatial Data
  • Teamwork and Conflict Resolution
  • Leadership/Followership
  • Consulting Skills
  • Problem-Solving
  • Communication Skills
  • Technical Writing

DATA 902 — Analytics Methods

Course Contents:

The following are the major topic areas to be covered:

  • Predictive modeling using multivariate regression, Logistic Regression, Decision Trees, Random Forest, Neural networks
  • Segmentation techniques like Cluster analysis and factor analysis
  • Multivariate techniques like Discriminant Analysis, Canonical correlations
  • Non Parametric techniques
  • Imputation and Outlier Analysis
  • Design of Experiments

DATA 903 — Analytics Applications II

Course Contents:

The following are the major topic areas to be covered:

  • Web Analytics
  • Simulation Models
  • Survey weighting / Psychometry
  • Survival analysis
  • Propensity score matching
  • Time series and forecasting

DATA 912 — Analytics Practicum II

 

DATA 896 — Self-Designed Analytics Lab I

Course Contents

The following are the major topic areas to be covered:

  • Write advanced programs in SAS, SQL and / or R

  • Explore data and patterns in SAS Enterprise Miner, SAS VA and JMP

  • Understand intermediate statistical methods and theory, using a variety of statistical and analytic techniques as example

  • Present findings, limitations, and future questions in a clear and comprehensive manner

  • Have a working knowledge of popular visualization software

DATA 897 — Self-Designed Analytics Lab II

Course Contents

This is the second of the two self-designed thesis courses offered under the master’s degree in data analytics.  The following are the major topic areas to be covered:

  • Learning and implementing statistical tools and techniques like linear and nonlinear modeling
    • regressions 
    • discriminant analysis 
    • neural networks
    • structured equation modeling,
  •  Utilizing multivariate techniques like factoring analysis, canonical correlations, manova
  • Understanding segmentation techniques like decision trees, random forests, and cluster analysis. 
  • A working knowledge and implementation of modern visualization tools like Tableau, SAS-VA, Piktochart, and JMP

DATA 950 — Population Health Analytics

Course Contents

The following are the major topic areas to be covered:

  • Analytics to measure needs, outcomes, causes, and program results  
  • Using information to mobilize action   
  • Analytics tools for
    • managing high cost/high need patients 
    • individualizing care 
    • community health outcomes 
    • health management sustainability
  •  Develop competencies to bridge a transition from the patient level to the community level, from intra- to inter- organizational management, and from a focus on high-costs to high-health

Cluster Course Requirements

MS in Analytics Program -
In addition to the above courses, two Cluster course electives are requried to allow students to specialize in an area of expertise. Courses will vary depending on Cluster area of focus and semester. Course areas include: 

Decision Sciences: ADMN 926: Information Systems across the Enterprise,  ADMN 940: Operations Management, ADMN 926:Information Systems and Enterprise Integration 
Economics: ECON 976: Microeconomics I, ECON 909: Environmental Evaluation
Finance: ADMN 930: Financial Management Investments, ADMN 830: Investments Analysis
Accounting: ADMN 919: Management Accounting and ACFI 890: Accounting Information Systems
Management:  ADMN 912: Organizational Behavior , ADMN 982: Strategic Management
Marketing: ADMN 926: Marketing Management, ADMN 898: Advertising and Integrated Marketing Communication
Health: Health Analytics, HMP 900: Introduction to the Health Services Industry
Self-Design: DATA 896: Self-Designed Analytics Thesis, DATA 897: Data Analytics Lab *

*Student works with an advisor to create a cluster of one's personal area of interest. Approval required.

 

Graduate Certificate in Data Science Courses

Data 800 Introduction to Applied Analytic Statistics

In this class, students will learn the foundations of probability and inferential statistics: upon completion, students will have an understanding of and be able to use Python explore descriptive statistics, probability distributions, margins of error, p-values, confidence intervals and more. All learning objectives are achieved through active application of these techniques to real world datasets.

DATA 820 Programming for Data Science

In this class, students will build their foundational toolbox in data science: upon completion, students will be able to use the computer from the command line; practice version control with Git & GitHub; gain a mastery of programming in Python; data wrangling with Python and perform an exploratory data analysis (EDA) in Python. All learning objectives are achieved through active application of these techniques to real world datasets. 

Co-requisite: DATA 800

DATA 821 Data Architecture

In this class, students will learn the foundations of databases and large datasets: upon completion, students will be able to explore out-of-ram datasets through traditional SQL databases and NoSQL databases. Students will also be introduced to distributed computing. All learning objectives are achieved through active application of these techniques to real world datasets.

Prerequisite: DATA 800; DATA 820

DATA 822 Data Mining & Predictive Modeling

In this class, students will learn the foundations of practical machine learning: upon completion, students will be able to understand and apply supervised and unsupervised learning in Python to build predictive models on real world datasets. Techniques covered include (but not limited to): preprocessing, dimensionality reduction, clustering, feature engineering and model evaluation. Models covered include: generalized linear models, tree-based models, bayesian models, support vector machines, and neural networks. All learning objectives are achieved through active application of these techniques to real world datasets.

Prerequisite: DATA 800; DATA 820

Co-Requisite: DATA 821