INET 4061
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Credits4
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Delivery MethodFully online
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Related Program
About This Course
Introduction to data science. Design strategies for business analytics: statistics for machine learning, core data mining models, data pipeline, visualization. Hands-on labs with data mining, statistics, and in-memory analytics software.
Sample course topics: Data science, statistics for machine learning, linear regression, classification, model features, ensemble models, textual and graph analysis, deep learning, and machine learning platform.
Sample textbooks: Data Mining: Examples and Case Studies by Yanchang Zhao, and Programming in Python by Wes McKinney
Instructors
PhD in organizational leadership, Saint Mary’s University of Minnesota; MBA and Master of Information Technology, University of Texas; Master of Health Informatics, University of Minnesota; BS in engineering, NED Engineering University of Pakistan
Dr. Saeed’s background is a blend of engineering, information technology, and education, with more than 15 years of related experience at Westinghouse, General Nanosystems, General Dynamics C4 Systems, Samsung, and others in managing and delivering various IT projects. Her experience includes leadership roles in application and web development, database design, IT infrastructure system modifications, process and product development, and operational improvements. She has worked as a program director of information technology and assistant adjunct professor of ITIS at the Saint Mary’s University of Minnesota, where she designed and managed three degree programs in areas such as information technology, business administration, and accounting.
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MS, business analytics and BISyE, industrial and systems engineering, University of Minnesota
Kyle Stahl is a senior data science supervisor at Cargill Inc, where he leads a team of data science specialists in solving complex machine learning and optimization problems across the agriculture industry. He has applied advanced analytics techniques to various industry groups within Cargill, including global supply chain, animal growth modeling, biochemistry, sales, agronomy, and production scheduling. Kyle works across the entire machine learning process, from defining the problem and collecting data to integrating mathematical models into software and helping people adapt their business processes to new technology.
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