Innovation Through Data Analytics
Industrial and systems engineering offers a combination of classic statistics and modern data science and machine learning. Industrial engineers have a long tradition of employing statistical and data science methodologies to measure, analyze, interpret and optimize complex systems. They leverage data to understand system and human performance, improve efficiency, and make informed decisions.
This pathway is a strong fit for students who:
- Enjoy deciphering patterns, solving puzzles and making sense of large datasets.
- Are stimulated by the challenge of using statistical and data-driven computational methods to solve complex problems.
- Are passionate about turning data into meaningful stories that drive business decisions.
- Believe in the power of data to shape our world and future.
Example Occupations and Common Fields
- Data Scientist
- Applied Statistician
- Systems Analyst
- Machine Learning Engineer
- Machine Learning Operations (MLOps) Engineer
- Quality Engineer
- Reliability Engineer
- Business Consultant
- Supply Chain Analyst
- Engineering Manager
- Technology companies
- Consulting firms
Courses and Experiences
- IND E 427: Data analytics for Systems Engineering
- CSE 160: Data Programming or CSE 163: Intermediate Data Programming
- CSE 416: Introduction to Machine Learning
- STAT 416: Introduction to Machine Learning
- SOC 225: Data and Society or INFO 350 ***(NOTE: this class is not currently listed)*** or HCDE 410: Human Data Interaction
Learn more about our Data Science Degree Specialization.
Frequently Asked Questions
Do I need a graduate degree specializing in this area to be marketable to the field?
No, a graduate degree is not strictly necessary to be marketable in the fields of data science and machine learning. Many positions value practical experience, portfolio projects, and demonstrable skills alongside or even above formal education.
However, a graduate degree can provide deeper theoretical knowledge, specialized expertise, and can be beneficial for certain advanced or research-oriented roles. It's essential to align your educational choices with your career goals and the specific requirements of your target job positions.
What are some examples of real-world areas of application?
Opportunities in these fields are vast. They include:
- Manufacturing: Predictive maintenance of machinery, optimization of production processes, and quality control.
- Supply Chain Management: Forecasting demand, optimizing inventory, and route planning for logistics.
- Healthcare: Remote monitoring of patients' health, optimizing hospital operations, and improving patient care quality.
- Energy: Smart grid optimization, predicting equipment failures, and energy consumption forecasting.
- Transportation: Traffic pattern analysis, route optimization, and predictive maintenance of transportation fleets.
Does this pathway touch on global impact, equity and/or quality of life?
Yes, the pathway underscores the power of data to shape our world and future. By optimizing industrial systems and processes, data science and machine learning can lead to more sustainable and efficient operations, directly impacting global resources. Moreover, the ethical considerations in the advanced courses address issues of equity, including data privacy and potential biases. By driving informed decisions in sectors like healthcare, energy, and transportation, this field can significantly enhance the quality of life for individuals and communities worldwide.
From Classroom to Career: Alumni Spotlight
See how our remarkable alumni are using their ISE degrees in this field.
Data Visualization Engineer, Netflix