Research Areas
Analytics & Statistics

Analytics is the process of using data relationships and computer models to drive business value, improve decision-making, and understand human relationships.

Analytics and statistics research tackles technologies and methodologies in the area of data science. We focus on the design of algorithms and analysis in statistical modeling, data mining and predictive analytics.

Recent Research

Recent research in analytics and statistics includes:

  • Algorithms and analysis of training predictive models on very large data sets
  • Statistical methods for the design and analysis of computer simulation experiments
  • Text mining for legal analytics
  • Mining large industrial databases for discovering root causes of poor quality
  • Development and implementation of machine learning algorithms in map reduce
  • Mining patient data in the healthcare space for disease risk modeling, design of more powerful clinical trials, etc
  • Predictive modeling of consumer credit data for strategic risk management

The "Data Age"

If the "Information Age" began in the 1990s with the rise of technology, then we’ve now officially entered the “Data Age.”

Companies like Google, Facebook, IBM, Teradata, Oracle, and SAS have the capacity to gather a lifetime’s worth of data about their customers and the customers’ behavior. All of their data is just a massive pile of numbers until a skilled analyst turns those numbers into meaningful and useful information for making intelligent business decisions.

Today, companies are searching for experts in analytics with backgrounds in both business and technology who understand the importance of the latest data and information-age trends.

Data Analysis

Our research area in analytics and statistics focuses on more than simple data analysis.

We use three lenses to view data:

  • Prescriptive analytics to focus on trends using simulation and optimization
  • Predictive analytics to use statistical tools to predict the future
  • Descriptive analytics to enable smart decisions based on data

Student Coursework

Student coursework in analytics and statistics focuses on the following areas:

  • Optimization: integer programming, nonlinear programming, local search, genetic algorithms, simulated annealing, and metaheuristics
  • Statistical modeling and analysis
  • Machine learning
  • Design of physical and simulation experiments
  • Predictive analytics: non-parametric regression and classification, time series, and quality control methods
  • Big data: with emphasis on Hadoop, unstructured data concepts (key-value), MapReduce technology, and analytics for big data
  • Data mining: clustering (k-means, partitioning), association rules, factor analysis, scale development, survival analysis, principal components analysis, and dimension reduction


Faculty members in analytics and statistics include: