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DATA 611 - Data Science and Analytics
Summer 2020, Section 01

search actionsID #Subj#SecTitleDatesDaysTimeCrdsStatusInstructorDelivery MethodLoc
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000552 DATA 611 01 Data Science and Analytics
06/15 - 06/26
M T W Th F
9:00am - 12:45pm
3.0 Cancelled Jacobson, David
Location: z MnSCU Metropolitan State University
Building/Room: LIBRARY 307


Meeting Details
DatesDaysTimeBuilding/RoomInstructor
6/15/2020 - 6/26/2020 M T W Th F 9:00am - 12:45pm LIBRARY 307 Jacobson, David

Notes
  • Cancel - Implementation of COVID-19. Prerequisites: Bachelor's degree in mathematics, mathematics education, statistics or related field.

Location Details
Offered through: Metropolitan State University.
Campus: Metropolitan State University. Location: z MnSCU Metropolitan State University.

Seat Availability
Status: Cancelled Size: 24 Enrolled: 0 Seats Remaining: 24

Restrictions
  • Permission is required
  • Requires students to be admitted.

Add/Drop/Withdraw
Full refund is available until June 16, 2020, 11:59PM CST.
The last day to add this course is 1 business day(s) after June 15, 2020. The last day to drop this course is 1 business day(s) after June 15, 2020.
The last day to withdraw from this course is June 24, 2020.

Tuition and Fees (Approximate)

Tuition and Fees (approximate):

Tuition -resident: $1,212.27
Tuition -nonresident: $2,424.57
Approximate Course Fees: $108.33

Course Level
Graduate

Description
The purpose of this course is to provide students with a sound conceptual understanding of the role that data science and analytics play in the decision-making process. The availability of massive amounts of data, improvements in analytic methodologies, and substantial increases in computing power have all come together to result in a dramatic upsurge in the use of data science and analytical methods. This course can be taken by students who have previously taken a course on basic statistical methods as well as students who have not had a prior course in statistics. Topics include models for summarizing, visualizing, and understanding historical data to assist in gaining insights for predicting possible future outcomes using descriptive, predictive and prescriptive data analytic techniques. Examples include applications in finance, human resources, marketing, health care, supply-chain, government and nonprofits, and sports.

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