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Students
Tuition Fee
Start Date
Medium of studying
Duration
Program Facts
Program Details
Degree
Masters
Major
Data Analytics | Data Science | Software Engineering
Area of study
Information and Communication Technologies
Course Language
English
About Program

Program Overview


This MS program in data science, analytics, and engineering with a concentration in Bayesian machine learning equips students with advanced statistical and probabilistic machine learning techniques. Graduates are prepared for careers as statisticians and data scientists in various industries, including finance, healthcare, and energy. The program emphasizes Bayesian thinking and covers areas such as hierarchical modeling, time series analysis, and causal modeling.

Program Outline


Degree Overview:


Objectives:

  • Equip students with the ability to interpret complex data sets using Bayesian inference.
  • Understand the statistical and mathematical foundations of probabilistic machine learning.
  • Develop expertise in Bayesian learning algorithms and computational methods.
  • Apply these skills to solve real-world problems in various domains including engineering, physics, biology, social sciences, economics, and finance.

Outline:


Core Courses (9 credit hours):

  • STP 502: Theory of Statistics II: Inference (3 credit hours)
  • EEE 554: Probability and Random Processes (3 credit hours) or DSE 501: Statistics for Data Analysts (3 credit hours)
  • One course chosen from a list including:
  • CSE 572: Data Mining (3 credit hours)
  • CSE 575: Statistical Machine Learning (3 credit hours)
  • EEE 549: Statistical Machine Learning: From Theory to Practice (3 credit hours)
  • IEE 520: Statistical Learning for Data Mining (3 credit hours)
  • IFT 511: Analyzing Big Data (3 credit hours)
  • IFT 512: Advanced Big Data Analytics/AI (3 credit hours)
  • MAE 551: Applied Machine Learning for Mechanical Engineers (3 credit hours)
  • STP 550: Statistical Machine Learning (3 credit hours)

Concentration Courses (9 credit hours):

  • STP 505: Bayesian Statistics (3 credit hours)
  • STP 540: Computational Statistics (3 credit hours)
  • STP 551: Time Series Analysis (3 credit hours)

Electives (6 or 9 credit hours):

  • Students can choose elective courses from a list approved by the academic unit.

Culminating Experience (3 or 6 credit hours):

  • Students choose between taking the FSE 570 Data Science Capstone (3 credit hours) or completing a thesis (6 credit hours).
  • Courses taken for the required core or concentration cannot be used as electives.
  • Students should consult the academic unit for a list of approved electives and concentration requirements.

Careers:

They can find opportunities in several fields, including:

  • Financial markets
  • Central banks
  • Pharmaceutical industry
  • Semiconductor industry
  • Communications industry
  • Energy and power systems industries
  • Institutions like the National Institutes of Health, Centers for Disease Control and Prevention, and National Oceanic and Atmospheric Administration
  • The program covers areas such as Hierarchical modeling, time series analysis, ensemble modeling, spatial modeling, and causal modeling.
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Admission Requirements

Entry Requirements:


Domestic Students (including EU home students):

  • Applicants must hold a bachelor's or master's degree in computing, engineering, mathematics, statistics, operations research, information technology, or a related field from a regionally accredited institution.
  • Minimum cumulative GPA of 3.00 (scale is 4.00 = "A") in the last 60 hours of their first bachelor's degree program, or a minimum cumulative GPA of 3.00 (scale is 4.00 = "A") in an applicable master's degree program.

International Students (outside EU):

  • Applicants must meet the domestic requirements mentioned above.
  • Proof of English proficiency is required regardless of current residency, with a minimum score of 90 on the TOEFL iBT (taken in a testing center), 7 on the IELTS or 115 on the Duolingo English test.

Additional Requirements for All Applicants:

  • Relevant coursework or experience in the following areas:
  • Familiarity with Matlab, Python, SQL, R, or other relevant programming skills.
  • Undergraduate linear algebra (e.g., MAT 343 Applied Linear Algebra).
  • Undergraduate statistics or probability (e.g., STP 420 Introductory Applied Statistics, STP 421 Probability, EEE 350 Random Signal Analysis).

Additional Requirements for Applicants Without an Undergraduate Degree in a Relevant Field:

  • Evidence of at least one of the following certifications or equivalent experience:
  • AWS-certified cloud practitioner
  • Google data analytics certificate
  • Google IT support certificate

Additional Note:

  • Applicants without an undergraduate degree in computer science, computer engineering, software engineering, information technology, industrial engineering, operations research, statistics or a related computing field must show evidence of a three-credit course in linear algebra (equivalent to MAT 343 at ASU).

Language Proficiency Requirements:

  • Non-native English speakers must demonstrate proficiency through a minimum score of 90 on the TOEFL iBT (taken in a testing center), 7 on the IELTS or 115 on the Duolingo English test.
  • This requirement applies regardless of current residency.
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