Human Factors in Data Science
The Human Factors in Data Science certificate program provides students with the knowledge and skills to engage in the design of artificial intelligence (AI) systems, based on data science (DS) and machine learning (ML) applications, providing a critical understanding of the role that humans play through the data processing pipeline.
As engineering solutions turn more and more toward automation and the use of AI, the need for practitioners who understand the critical role of human decision making in automated system design is greater than ever. This program will be of value to students interested in engineering, data science and computer science with a desire to build human-AI systems.
The program draws on courses in human factors engineering and data science. Students will be prepared to engage in AI system development, guiding the design of human data collection, feature extraction and system implementation efforts.
Program of Study
- ENP-167 Human Factors of Data Science
This course focuses on the human factors of data processing pipelines. This includes data collection techniques, data cleaning and manipulation techniques, interactions between data collection and model assumptions, and concepts for interpretation of model accuracy with respect to human performance. This course applies to a range of topics including healthcare, transportation, military operations, consumer-facing systems, and safety systems.
- CS-205 Data Science in Python
Fundamentals of python programming for data analysis. Common python data structures and algorithms. Design of python programs. Coding standards and practices. Use and creation of software libraries. Examples drawn from data preparation and transformation, statistical data analysis, machine learning, deep learning, and deep data science including recommendation systems and trend analysis. Labs utilizing iPython and the Jupyter data analysis workflow framework.
Choose One Foundation Course:
- CS-135 Introduction To Machine Learning And Data Mining
An overview of methods whereby computers can learn from data or experience and make decisions accordingly. Topics include supervised learning, unsupervised learning, reinforcement learning, and knowledge extraction from large databases with applications to science, engineering, and medicine.
- CS-119 Big Data
Principles, practices, and tools for analyzing and interpreting large data sets. Distributed data stores and maintaining data consistency. Query languages for data analysis, including SparQL. Scalable indexing strategies for data search, including SOLR. Map/Reduce and other parallel programming paradigms for data reduction and analysis. Supercomputing, high-performance storage, and strategies for assuring data locality and movement. Principles illustrated by applying common data analysis algorithms to large data sets. Prerequisites: COMP 15 and MATH 70, or graduate standing.
Choose One Elective Course:
- ENP-161 Human Factors in Product and System Design
- ENP-162 Human Factors in Human Machine Systems
- ENP-163 Analytic Methods in Human Factors
- ENP-164 Behavioral Statistics for Engineering