Data Science, Analytics and Engineering (Computing and Decision Analytics), MS

Program Overview

Arizona State University's MS in Data Science, Analytics and Engineering equips students with expertise in data science, computing, and industrial engineering. The program offers a comprehensive curriculum in probability, statistics, machine learning, data mining, and data engineering, preparing graduates for high-demand careers in data management, analytics, and engineering. Students can choose a concentration in Data Analysis, Optimization, Machine Learning, or Data Assurance and Security, and complete a capstone project or thesis to demonstrate their acquired knowledge and skills.

Program Outline

Degree Overview:

• Aims to equip students with data science expertise while enhancing their knowledge in computing and industrial engineering.
• Prepares graduates for high-demand careers by offering practical courses and real-world problem-solving collaboration opportunities.
• Focuses on probability, statistics, machine learning, data mining, data engineering, coupled with computing and industrial engineering-specific courses for a comprehensive and in-depth learning experience.

Outline:

• Program Structure:
• 30 credit hours and a thesis
• OR
• 30 credit hours with the required capstone course (FSE 570)
• Required Core (9 credit hours):
• STP 502 Theory of Statistics II: Inference (3)
• EEE 554 Probability and Random Processes (3)
• OR
• DSE 501 Statistics for Data Analysts (3)
• CSE 511 Data Processing at Scale (3)
• CSE 512 Distributed Database Systems (3)
• OR
• IFT 530 Advanced Database Management Systems (3)
• Choose one from the following (3):
• CSE 572 Data Mining (3)
• CSE 575 Statistical Machine Learning (3)
• EEE 549 Statistical Machine Learning: From Theory to Practice (3)
• IEE 520 Statistical Learning for Data Mining (3)
• IFT 511 Analyzing Big Data (3)
• IFT 512 Advanced Big Data Analytics/AI (3)
• MAE 551 Applied Machine Learning for Mechanical Engineers (3)
• STP 550 Statistical Machine Learning (3)
• Concentration (12 credit hours):
• Complete one course for each of the following areas (3 per area):
• Data Analysis
• Optimization
• Machine Learning
• Data Assurance and Security
• Electives (3 or 6 credit hours):
• Allows for personalization and specialization within the data science field.
• Culminating Experience (3 or 6 credit hours):
• Capstone project or thesis demonstrating acquired knowledge and skills.
• Can choose between:
• CSE, IEE or SER 599 Thesis (6)

Careers:

• Graduates are prepared for diverse career opportunities in various industries:
• Consulting
• Data and analytics management
• Data engineering
• Manufacturing
• The program equips graduates with the skills and knowledge to manage and analyze data, extract knowledge from large datasets, and make informed decisions.

Other:

• Completion of the program may qualify graduates for an Optional Practical Training (OPT) extension for up to 36 months.