Program Overview

Program Overview

The Master of Data Analytics – Big Data Systems Option is a graduate program designed to equip students with the skills and knowledge necessary for analyzing big data to solve business problems. The program focuses on building expertise in data analytics, including the design and implementation of large-scale databases, data mining, and predictive analytics.

Program Details

• The program consists of 30 credits, with a cost of $1,078 per credit.
• The program is offered entirely online, allowing students to complete their coursework at their own pace.
• The application deadline for the summer semester is March 15, with classes starting on May 18.

Admission Requirements

• Applicants must hold a baccalaureate degree from a regionally accredited U.S. institution or a tertiary degree deemed comparable to a four-year bachelor's degree from a regionally accredited U.S. institution.
• A postsecondary (undergraduate) junior/senior (last two years) GPA of 3.0 or above on a 4.0 scale is required.
• GRE/GMAT scores are not required and will not be reviewed.
• English proficiency is required for international applicants, with minimum test scores and exceptions found in the English Proficiency section on the Fox Graduate School's "Requirements for Graduate Admission" page.

Program Structure

• The program includes 9 credits of core courses, 9 credits of prescribed courses for the Big Data Systems Option, and 9 credits of electives chosen in consultation with the program adviser.
• A 3-credit culminating capstone experience is required to complete the program.

Core Courses

• DAAN 545: Data Mining (3 credits)
• IE 575: Foundations of Predictive Analytics (3 credits)
• STAT 500: Applied Statistics (3 credits)

Big Data Systems Option Prescribed Courses

• DAAN 822: Data Collection and Cleaning (3 credits)
• DAAN 825: Large-Scale Database and Warehouse (3 credits)
• DAAN 826: Large-Scale Databases for Real-Time Analytics (3 credits)

Electives

• A-I 570: Deep Learning (3 credits)
• A-I 572: Reinforcement Learning (3 credits)
• A-I 574: Natural Language Processing (3 credits)
• A-I 596: Individual Studies (3 credits)
• A-I 801: Foundations of Artificial Intelligence (3 credits)
• A-I 804: Ethics of Artificial Intelligence (3 credits)
• A-I 879: Machine Vision (3 credits)
• BA 831: Foundations in Finance (3 credits)
• BADM 834: Portfolio Management (3 credits)
• BUSAD 525: Quantitative Methods in Finance (3 credits)
• DAAN 846: Network and Predictive Analytics for Socio-Technical Systems (3 credits)
• DAAN 862: Analytics Programming in Python (3 credits)
• DAAN 871: Data Visualization (3 credits)
• DAAN 881: Data-Driven Decision-Making (3 credits)
• DAAN 897: Enterprise Analytics Strategies (3 credits)
• INSC 521: Database Design Concepts (3 credits)
• STAT 501: Regression Methods (3 credits)
• STAT 510: Applied Time Series Analysis (3 credits)
• SWENG 805: Software Project Management (3 credits)

Culminating Experience

• DAAN 888: Design and Implementation of Analytics Systems (3 credits)

Career Opportunities

• The program prepares students for careers in data science, including data scientist, data analyst, data architect, data engineer, and data officer.
• Employment outlook for occupational fields related to this degree includes computer and information systems managers, data scientists, computer systems analysts, database architects, and database administrators.

Faculty

• The program is delivered through a strong partnership between three academic departments from across the University, offering students the opportunity to benefit from the expertise and unique perspectives of faculty with diverse backgrounds.
• Faculty members include:
  • Adrian S. Barb
  • Youakim Badr
  • Mohamad Darayi
  • Ashkan Negahban
  • Colin Neill
  • Robin G. Qiu
  • Dusan Ramljak
  • Raghvinder S. Sangwan
  • Hajime Shimao
  • Satish Srinivasan
  • Chengfei Wang

Research Areas

• The program covers a range of research areas, including data mining, knowledge discovery in databases, big data, machine learning, and software engineering.
• Faculty members have research interests in areas such as smart service computing, IoT, information security, data/business analytics, and bioinformatics.