Prof, ast
Business & Information Tech
301 W. 14th St
Rolla, MO 65409
Examines data science methodologies for scraping, manipulating, transforming, cleaning, visualizing, summarizing, and modeling large-scale data as well as supervised and unsupervised machine learning algorithms applied in various business analytics and data science scenarios. Python libraries such as Pandas, NumPy, Matplotlib, and Scikit-learn are utilized.
Time/Day: 02:00 PM - 02:50 PM MWF Prerequisites: One of Stat 3111, Stat 3113, Stat 3115, or Stat 3117; one of IS&T 1552, IS&T 1562, Comp Sci 1575; for Graduate Students: knowledge of calculus, statistics, and programming. Units: 3 Course Component(s): Lecture |
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For live class lectures, you will log in using the Zoom UM System link in Canvas. For accessing class recordings, you will need to contact your instructor directly.
Enrollment Information | ||||
Campus | Delivery Mode | Class Status | Class Nbr | Section |
Distance Education | Online | OPEN | 72975 | 102 |
Course Access Information | ||||
Learning Management System | Canvas |