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Masters' Big Data Analysis

INSY 5379
Closed
University of Texas at Arlington
University of Texas at Arlington
Arlington, Texas, United States
SN
Sridhar Nerur
Professor
2
Timeline
  • August 27, 2017
    Experience start
  • September 11, 2017
    Project Scope Meeting
  • October 16, 2017
    Mid-Term Progress Review
  • December 5, 2017
    Experience end
General
  • 5 learners; individual projects
  • 50 hours per learner
  • Dates set by experience
  • Learners self-assign
Preferred companies
  • 1 projects wanted
  • Anywhere
  • Academic experience
  • Start-ups, non-profit, for-profit, universities, or any entity that is seeking a data analytic solution to a business problem.
  • Any industry
Categories
Skills
data analysis data science machine learning data analytics business analytics
Project timeline
  • August 27, 2017
    Experience start
  • September 11, 2017
    Project Scope Meeting
  • October 16, 2017
    Mid-Term Progress Review
  • December 5, 2017
    Experience end
Overview
Learner goals and capabilities

Master's-level student-consultants will develop a tailored strategy for your organization using data analytics and machine learning algorithms, designed to address a challenge or opportunity specific to your needs.

Expected outcomes and deliverables

Phase 1 – Project Plan: Students will meet with organization representative(s) to devise the project scope and prepare detailed plan for completion of the project.

Phase 2 – Analysis & Strategy: Students will work on deliverables outlined in the project plan. Teams will periodically communicate with organization representative(s) as needed to complete project tasks.

Phase 3 - Outcome Report and Presentation: Students will submit detailed report on the outcomes of the project. Teams will also present this in the last class. Organization representative(s) are invited to attend these presentations. Report and presentation will include:

  • Clear statement of objectives
  • Alternatives considered to fulfill business objectives
  • Evaluation of alternatives
  • What was the best solution
  • Limitations and future directions
  • Conclusions
Project Examples

Starting this August, master's-level Business Analytics students will spend time analyzing applicable data sets which will help you make informed decisions, key to the success of your business.

Based on information on your organization provides and the goals that you've shared with the students, they will provide you with an analysis and recommendations that will help you gain insights into new opportunities or address a specific challenge your organization is facing.

The students are trained in a variety of technical and business skills, and have studied topics including market basket analysis, text analytics, machine learning, deep learning, Spark Analytics, and recommender systems.

Examples of previous projects include the following:

  • Sentiment analysis of social media content (positive, negative or neutral)
  • Stock Market prediction based on time-series modelling
  • Text analysis of product/business reviews and how they impact performance
  • Predicting house prices based on historical data
  • Building a recommender system for movies
  • Predicting customer churn and recommending solutions to avoid this
  • Text analysis of blogs to identify common trends

Projects are not limited to the above areas. The students have experience studying datasets to deal with a variety of problems, such as image classification, predicting the quality of wine, detecting credit card fraud, etc.

Additional company criteria

Companies must answer the following questions to submit a match request to this experience:

Upon completion of the project, the organization should provide feedback on the performance of the students/groups.

Be available for questions from students.

Provide any necessary data (NDAs can be signed)