Data Science in Chemical Engineering

The Data Science program addresses a training gap that currently exists between chemical engineering and emerging data science applications. Chemical engineering is a data rich discipline exhibiting many opportunities to exploit data science and modern machine learning methods to automate, optimize, and expedite many industrial processes. However, successful data science applications typically require both computer science expertise and application domain expertise. In the case of the interface between chemical engineering and data science, this interaction plays out at all levels of data generation and data modeling. To realize this potential, it is critical to train chemical engineers in the tools of data science, including understanding aspects of data collection and curation, contemporary machine learning strategies, and the pitfalls of various algorithms.

The courses available in this program are designed to expose chemical engineers to these aspects of data science within the context of chemical engineering applications. These offerings include training in the underlying mathematics and statistics that underpin machine learning algorithms, courses associated with process optimization and automation, and courses associated with applied machine learning and programming. In this way, students in this program are exposed to all aspects of the data science within the context of chemical engineering. When coupled with the management courses that are common to the Chemical Engineering Professional M.S. program, this training prepares students to enter, and quickly manage, the organizations that rely on data-driven decision making and data utilization for a competitive advantage within the chemical industry.

Professional Master’s Program Degree Map

MSChE – Data Science - 12-month Track

  • Students must earn a “C” or better in all graduate-level coursework
  • Students must complete at least 15 credits of coursework with a CHE prefix
  • Students must have a cumulative GPA of 2.7 or higher to graduate

Degree Requirements

30 credits required for graduation:

  • 6 Credits of Core ChE Course:
    • (3) CHE 69700 – Statistical Methods in Chemical Engineering OR CHE 53000 – Engineering Math
    • (3) CHE 54000 – Transport Phenomena
  • 9 Credits of Concentration Elective Courses
  • 9 Credits of Management/Business Courses from the following:
    • (3) CHE 59700 – Engineering Applications in Marketing Mgmt
    • (3) CHE 59700 – Financial Analysis & Management of Projects
    • (3) MGMT 65000 – Strategic Management I
    • (3) MGMT 66000 – Intro to Operations Management
  • 6 Credits of ChE Capstone Project

Degree Map Example

Fall Semester (12 credits)

___ (3) CHE 69700 – Statistical Methods in Chemical Engineering

___ (3) CHE 59700 – Engineering Applications in Marketing Mgmt

___ (3) Concentration Elective Course (chosen from the list below)

___ (3) Concentration Elective Course (chosen from the list below)

Spring Semester (12 credits)

___ (3) CHE 54000 – Transport Phenomena

___ (3) CHE 59700 – Financial Analysis & Management of Projects

___ (3) MGMT 65000 – Strategic Management I OR (3) MGMT 66000 – Intro to Operations Management

___ (3) Concentration Elective Course (chosen from the list below)

Summer Semester (6 Credits)

___ (6) CHE 59700 – Prof. MS Capstone Projec

Potential Concentration Elective Courses:

(CS courses are subject to availability)

___ (3) BIOL 56310 – Protein Bioinformatics
___ (3) CHE 46300 – Applications of ChE Principles
___ (3) CHE 55000 – Optimization in ChE
___ (3) CHE 55400 – Smart Mfg. in Process Industries
___ (3) CHE 55500 – Computer Integrated Process Ops
___ (3) CHE 55700 – Intelligent Systems in Process Engr
___ (3) CHE 59700 – Data Science in Chemical Engr
___ (3) CHE 59700 – Industrial Chem Technology
___ (3) CHE 59700 – Process Synthesis
___ (3) CHE 59700 – Process Safety
___ (3) CHE 63300 – Probabilistic Methods in ChE
___ (3) CHE 65600 – Advanced Process Control
___ (3) CHE 69700 – Adv Modeling for Catalysis Studies
___ (3) CS 50100 – Computing for Science and Engr
___ (3) CS 57800 – Statistical Machine Learning
___ (1) CS 59000 – Foundations of Computer Science
___ (3) CS 59300 – Machine Learning Theory
___ (1) CS 50023 – Data Engineering I
___ (1) CS 50024 – Data Engineering II
___ (1) CS 50025 – Foundations of Decision Making
___ (1) CS 59000 – Numerical Computing for Data Sci
___ (3) ECE 50024 – Machine Learning
___ (3) ECE 56200 – Introduction to Data Management
___ (3) ECE 57000 – Artificial Intelligence
___ (3) ECE 59500 – Intro to Data Mining
___ (3) ECE 60000 – Random Variables and Signals
___ (3) ECE 62900 – Introduction to Neural Networks
___ (3) ECE 64200 – Information Theory and Source Coding

Professional Master’s Program Degree Map

MSChE – Data Science in Chemical Engineering – 16-month Track

  • Students must earn a “C” or better in all undergraduate and graduate-level coursework
  • Students must complete at least 15 credits of coursework with a CHE prefix
  • Students must have a cumulative GPA of 2.7 or higher to graduate

Degree Requirements

41 credits required for graduation

  • 11 credits of pre-requisite courses:
    • (4) CHE 20500 – Chemical Engineering Calculations
    • (3) CHE 30600 – Separations Processes
    • (4) CHE 34800 – Reaction Engineering
  • 6 Credits of Core ChE Course:
    • (3) CHE 53000 – Engineering Math
    • (3) CHE 54000 – Transport Phenomena
  • 9 Credits of Concentration Elective Courses
  • 9 Credits of Management/Business Courses from the following:
    • (3) CHE 59700 – Engineering Applications in Marketing Mgmt
    • (3) CHE 59700 – Financial Analysis & Management of Projects
    • (3) MGMT 65000 – Strategic Management I
    • (3) MGMT 66000 – Intro to Operations Management
  • 6 Credits of ChE Capstone Project

Degree Map Example

Fall Semester 1 (10 credits)

___ (4) CHE 20500 – Chemical Engineering Calculations

___ (3) CHE 53000 – Engineering Math

___ (3) Concentration Elective Course (from the list below)

Spring Semester 1 (13-16 credits)

___ (3) CHE 30600 – Separations Processes

___ (4) CHE 34800 – Reaction Engineering

___ (3) CHE 54000 – Transport Phenomena

___ (3) CHE 59700 – Financial Analysis & Management of Projects

___ (3) MGMT 65000 – Strategic Management I OR (3) MGMT 66000 – Intro to Operations Management

Summer Semester 1 (6 Credits)

___ (6) CHE 59700 – Prof. MS Capstone Project

Fall Semester 2 (9 Credits)

___ (3) CHE 53000 – Engineering Applications in Marketing Mgmt

___ (3) Concentration Elective Course (from the list below)

___ (3) Concentration Elective Course (from the list below)

Potential Concentration Elective Courses:

(CS courses are subject to availability)

___ (3) BIOL 56310 – Protein Bioinformatics
___ (3) CHE 46300 – Applications of ChE Principles
___ (3) CHE 55000 – Optimization in ChE
___ (3) CHE 55400 – Smart Mfg. in Process Industries
___ (3) CHE 55500 – Computer Integrated Process Ops
___ (3) CHE 55700 – Intelligent Systems in Process Engr
___ (3) CHE 59700 – Data Science in Chemical Engr
___ (3) CHE 59700 – Industrial Chem Technology
___ (3) CHE 59700 – Process Synthesis
___ (3) CHE 59700 – Process Safety
___ (3) CHE 63300 – Probabilistic Methods in ChE
___ (3) CHE 65600 – Advanced Process Control
___ (3) CHE 69700 – Adv Modeling for Catalysis Studies
___ (3) CS 50100 – Computing for Science and Engr
___ (3) CS 57800 – Statistical Machine Learning
___ (1) CS 59000 – Foundations of Computer Science
___ (3) CS 59300 – Machine Learning Theory
___ (1) CS 50023 – Data Engineering I
___ (1) CS 50024 – Data Engineering II
___ (1) CS 50025 – Foundations of Decision Making
___ (1) CS 59000 – Numerical Computing for Data Sci
___ (3) ECE 50024 – Machine Learning
___ (3) ECE 56200 – Introduction to Data Management
___ (3) ECE 57000 – Artificial Intelligence
___ (3) ECE 59500 – Intro to Data Mining
___ (3) ECE 60000 – Random Variables and Signals
___ (3) ECE 62900 – Introduction to Neural Networks
___ (3) ECE 64200 – Information Theory and Source Coding

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