Analytics

Study mode:On campus Study type:Full-time Languages: English
Local:$ 50.4 k / Year(s) Foreign:$ 50.4 k / Year(s) Deadline: Jan 15, 2025
24 place StudyQA ranking:3930 Duration:2 years

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The Master of Science in Analytics program at Northwestern University’s McCormick School of Engineering teaches students the skills that drive business success in today’s hyper-competitive, data-driven world.

Drawing on the expertise of renowned professors and lecturers from major corporations, we combine mathematical and statistical studies with instruction in advanced information technology and data management. Students are immersed in a comprehensive and applied curriculum as they learn to:

  • Identify patterns and trends
  • Derive optimized recommendations evaluated through simulations
  • Interpret and gain insight from vast quantities of structured and unstructured data
  • Communicate their findings in practical and useful terms that help businesses succeed

Supplemented by an internship and industry-supplied projects, graduates will be exceptionally well equipped to harness and communicate the full value of data to the organizations they serve.

Many programs focus on just one aspect of analytics, producing graduates who are trained primarily in either modeling or data mining. The MSiA program provides students with a rigorous course of study in all three areas of analytics: predictive, prescriptive, and descriptive. Coursework in statistics, modeling, operations research, quantitative analysis, decision analysis, databases, and data management form the core of the curriculum. Each course builds upon previous courses and is complemented by case studies, team projects, and guest speakers from industry, ensuring an academic experience that’s grounded in business reality. This comprehensive curriculum provides a broader perspective and teaches the skills necessary to effectively solve business challenges. 

Students leave the program with the ability to lead via our three-pronged approach:

  • Business: Students integrate strong business acumen, communication skills, and management ability into their work.
  • IT: Students train in Java, Python, and R, preparing them for high-tech roles.
  • Data science: Students explore all three areas of data analysis—predictive (forecasting), descriptive (business intelligence and data mining), and prescriptive (optimization and simulation), using industry-leading tools such as SAS, SQL, Hadoop, Pig, Hive, Mahout, Tableau, D3.

Courses

  • ENTREP 495: NUVENTION ANALYTICS
  • MEM 415: COMPUTER SIMULATION FOR RISK AND OPERATIONS ANALYSIS
  • MSIA 400: ANALYTICS FOR COMPETITIVE ADVANTAGE
  • MSIA 401: PREDICTIVE ANALYTICS I
  • MSIA 410: ANALYTICAL CONSULTING PROJECT LEADERSHIP
  • MSIA 411: DATA VISUALIZATION
  • MSIA 412: LEADERSHIP FOR ANALYTICAL ORGANIZATIONS AND FUNCTIONS
  • MSIA 413: DATABASES RETRIEVAL
  • MSIA 420: PREDICTIVE ANALYTICS II
  • MSIA 421: DATA MINING
  • MSIA 422: INTRO TO JAVA & PYTHON PROGRAMMING
  • MSIA 430: BIG DATA AND BUSINESS INTELLIGENCE
  • MSIA 431: ANALYTICS FOR BIG DATA
  • MSIA 440: OPTIMIZATION AND HEURISTICS
  • MSIA 489: INDUSTRY PRACTICUM
  • MSIA 490-20: TEXT ANALYTICS
  • MSIA 490-21: PREDICTIVE MODELS FOR CREDIT RISK MANAGEMENT
  • MSIA 490-23: HEALTH ANALYTICS AND DECISION MAKING
  • MSIA 490-27: SOCIAL NETWORKS ANALYSIS
  • MSIA 490-29: DEEP LEARNING
  • MSIA 499: CAPSTONE DESIGN

Requirements

  • A minimum of 3.0 Grade Point Average on a 4.0 scale in undergraduate work
  • Transcripts from all undergraduate and graduate education attempted
  • $75 application fee payable in US funds
  • Three letters of recommendation that attest to the candidate's work performance and management potential
  • TOEFL or IELTS scores for international applicants who have not had English as their primary language of instruction
  • Unofficial GMAT or GRE scores
  • Candidate Video (optional)

Scholarships

The Master of Science in Analytics offers a limited number of fellowships each year covering 50 percent of the tuition expenses. They are awarded during the application process to select students and are based on merit. No additional applications are required for consideration. 

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