Machine Learning and Data Engineering

The MLDE MS program equips students to harness the power of Data Engineering to improve the efficiency and effectiveness of Machine Learning, Data Science, and AI when tackling important, real-world engineering challenges. Combining cutting-edge concepts, modern software tools, and hands-on applications, the program prepares the students to develop innovative, data-driven solutions with meaningful impact across engineering domains and applications of societal relevance.

The Machine Learning and Data Engineering (MLDE), M.S. program complements the mainstream M.S. programs in Data Science and Artificial Intelligence (AI) by providing unique, foundational expertise at the intersection of Machine Learning, Data Engineering, and engineering applications. It focuses on the challenges and opportunities of working with diverse data types, including analog data, high-speed streaming data, and heterogeneous data collected from various sensors. Students will have opportunities to explore, in both breadth and depth, a unique synergy of: (i) how Data Engineering can improve the efficiency and effectiveness of Machine Learning, Data Science, and AI techniques can be applied to address specific engineering challenges across a variety of domains; and (ii) how Machine Learning techniques can improve data organization and retrieval. The MLDE program prepares students not only in the principles and practice of Machine Learning and Data Engineering techniques and tools, but also in their effective application to real-world engineering problems, where data-driven insights and intelligent decision-making are essential. By connecting students with different engineering domains and their unique data, modeling, and decision-making needs, the program offers a distinctive educational experience that bridges computational innovation and practical engineering applications — one that is unique among the Regents Universities in Iowa


Degrees Offered: M.S.

 

Goce Trajcevski

gocet25@iastate.edu
515-294-2176
347 Durham Hall

Learning Goals

Enable engineers to:

  • Apply data-driven methods to problems in engineering domains
  • Make better use of data being collected by widely prevalent sensors
  • Apply (deep) machine learning techniques to heterogeneous data in engineering domains
  • Better address research challenges in harnessing the data revolution
  • Achieve wide range of societal impacts

Provide training in:

  • Data Methods – collection, managing (storing/retrieving), analysis, and interpretation of data
  • Data Acquisition, Analysis, and Knowledge generation and management in Engineering Domains
  • Software and Statistical Methods for manipulating data and information extraction.
  • Experience with applying data-driven methods to engineering domains

Admission Requirements

Graduate College Requirements:

  • 4 year Bachelor’s degree (or equivalent)
  • Academic Records/Transcripts
  • Minimum 3.0 GPA (Program may alter requirement.)
  • Proof of English Proficiency.

Program Specific Requirements:

  • Resume
  • Letters of recommendation

International Requirements:

  • Financial Statement:  Application
  • English Proficiency Minimum:
    TOEFL Paper (PBT) 600
    TOEFL Internet (iBT) 100
    IELTS 7.5
    PTE 68
    Duolingo (approved through spring 2025) 115

Application Requirements

Application Deadlines:

Will start accepting applications February 1, 2027 for Fall 2027 term.

International students should apply at least three months before the start of term they are applying.

Application Details:

This program is open to domestic and international students.

Application Instructions