Data Mining, Modeling, and Decision Making
(DMD) from Real-World Behavioral Data
Project Description
A major challenge is how to understand and gain valuable information from
data. The DMD will delve into huge, noisy, and complex real-world
behavioral data to produce innovative analysis. The DMD will creatively
find hidden values to improve the understanding of businesses to drive
actionable decisions.
This project will develop analytical solutions that address clients’ real
marketing problems. The data needs cleansing, merging, data-creation and
algorithm-implementation. In addition, we provide novel analytic insights in
relation to the client’s business domain and needs, algorithm
implementation, etc. For powerful solutions, we will explore advanced
analytics such as data mining/ machine learning, neural networks, SVM,
random forests, hierarchical Bayes, etc. Predictive modeling includes
variable selection, data imputation, collinearity diagnostics, factor analysis,
variable interaction analysis, etc.
Responsibilities of Undergraduate Researcher
1. Collaborates with teammates to solve business problems using data
science tools, packages, and visualization techniques.
2. Builds analytical models using statistical and data mining
methodologies.
3. Executes standard exploratory and ad hoc data analyses. Interprets and
presents results using tools such as PowerPoint or Tableau.
4. Applies cleansing, discretization, imputation, selection, generalization
etc. to create high quality features for the modeling process.
Desired Skills and Experience
1. Knowledge of at least one of the analytics languages / toolkits such as R,
Python with analytical extensions, SAS, SPSS or Matlab.
2. Knowledge of SQL and advanced data processing.
3. Effective written and verbal communication and presentation skills.
***New students will have data mining, statistics, and software
training.***
Outcomes
During the execution of the project, we expect to provide contributions on
the followings:
1. Attend ACES Symposium to give a poster and/or an oral presentation.
2. One doctoral thesis and at least two undergraduate papers will be
published related to this research.
3. Looking for possible external funding.
Open position
One undergraduate position is available in the project. Looking for someone
who desires to be a data scientist, team player, who is creative and eager to
learn and teach others.
Project Advisor
Dr. Aera Kim LeBoulluec
Lecturer
Department of Industrial and Manufacturing Systems Engineering
University of Texas at Arlington
500 West First Street, Arlington, TX 76019
Office: Woolf Hall 420 L
aeral@uta.edu