Program Overview

Master of Science - Data Analytics

About

The M.S. in Data Analytics equips students with technical skills that are used by professional data scientists for data analysis, visualization, predictions, and discoveries. This Master degree at UNLV utilizes the strengths of six colleges for computer programming, database technology, machine learning, management, and statistical techniques for data analysis in specific disciplines. The program covers applications in many industries and specialties such as health care, hospitality, social sciences, engineering, business, and government.

Accreditation

For information regarding accreditation at UNLV, please refer to Academic Program Accreditations.

Learning Outcomes

• Demonstrate a basic level of technical knowledge related to programming, database management, and machine learning.
• Demonstrate a mastery of in-depth knowledge of fundamental and basic statistical principles.
• Formulate, update, and communicate results obtained from the data.

Requirements

Plan Description

The program is designed to appeal to and accept a wide variety of students from a wide variety academic disciplines and the goal is that students will be able to acquire this degree and enter careers in their area of specialization with mid-level data analytical capabilities. From there, they will be able to grow their careers as their experience and application of these skills in their field increases.

This program will utilize the strengths of the College of Engineering's Computer Science Department, which can provide the technical expertise of dealing with database technology, programming, and machine learning. The Lee Business School's Department of Information Systems faculty will provide the expertise in managerial aspects of data, governance, and the application of data analytics in an organizational environment to solve problems. The program will rely on the other participating colleges to provide statistical techniques training that are required for data analysis in specific disciplines.

Plan Admission Requirements

• Completed a regionally accredited Bachelor's degree.
• Minimal mathematics background is required (equivalent to MATH 127 or 128).
• Official transcript of all university-level education from accredited institutions. Unofficial transcripts can be accepted at the time of application, however, official transcripts must be submitted upon acceptance.
• International students must follow the English proficiency requirements.
• Two letters of recommendation concerning the potential for success in the graduate program.
• Statement of purpose explaining interest in the program.
• Minimum GPA and further requirements for all domestic and international applicants can be found at the Graduate College Admission and Registration Requirements page.

Plan Requirements

Total Credits Required: 36

Subplan Requirements

Subplan 1 Requirements: Data Analytics

Total Credits Required: 36

Course Requirements

Required Courses – Credits: 18

• DA 621 - Programming For Data Analytics I
• DA 622 - Programming for Data Analytics II
• DA 651 - Managing Big Data and Web Databases
• MIS 761 - Business Analytics Methods and Tools
• MIS 769 - Big Data Analytics for Business
• MIS 776 - Business Analytics

Statistical Courses – Credits: 6

• EAB 770 - Applied Statistical Methods for Categorical Data
• EAB 783 - Multivariate Methods for the Health Sciences
• HOA 730 - Statistical Analysis for Hospitality
• STA 691 - Statistics for Scientists I
• STA 692 - Statistics for Scientists II

Elective Courses – Credits: 9

Complete 9 credits of advisor approved courses.

Culminating Experience – Credits: 3

• DA 790 - Data Analytics Capstone

Subplan 2 Requirements: Data Analytics - (Exclusively Fully Online)

Total Credits Required: 36

Course Requirements

Required Courses – Credits: 27

• CS 637 - Building Applications in the Cloud
• CS 642 - Cloud Computing
• CS 769 - Advanced Data Base Management
• DA 621 - Programming For Data Analytics I
• DA 622 - Programming for Data Analytics II
• DA 651 - Managing Big Data and Web Databases
• MIS 761 - Business Analytics Methods and Tools
• MIS 769 - Big Data Analytics for Business
• MIS 776 - Business Analytics

Statistical Courses – Credits: 6

• EAB 770 - Applied Statistical Methods for Categorical Data
• EAB 783 - Multivariate Methods for the Health Sciences
• STA 691 - Statistics for Scientists I
• STA 692 - Statistics for Scientists II

Culminating Experience – Credits: 3

• DA 790 - Data Analytics Capstone

Degree Requirements

• This Program is available In-Person, Blended (courses available in person and/or online), or Fully Online.
• Students must complete 36 credits of approved coursework.
• Students must obtain a 3.0 GPA in order to graduate.
• A student can have no more than one grade less than B-.

Plan Graduation Requirements

• The student must successfully complete a culminating project.
• Students may apply for graduation up to two semesters prior to completing their degree requirements.

Documents/Downloads

• Plans of Study
• Syllabi
• Degree Worksheets
• Graduate Handbooks

Contacts

Graduate Coordinator

• Kazem Taghva
• Data Analytics Chair, Professor

Department of Computer Science

• The Department of Computer Science is nationally and internationally recognized for research in theoretical and experimental computer science.

Howard R. Hughes College of Engineering

• The College of Engineering provides students a well-rounded foundation in several engineering disciplines for a successful career in engineering and computer science.