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DATA 211 - Data Science and Visualization
Spring 2021, Section 01

search actionsID #Subj#SecTitleDatesDaysTimeCrdsStatusInstructorDelivery MethodLoc
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000608 DATA 211 01 Data Science and Visualization
01/14 - 04/29
Th
6:00pm - 9:20pm
4.0 Open Wei, Wei
Remote Teaching No In-Person Location: z MnSCU Metropolitan State University


Meeting Details
DatesDaysTimeBuilding/RoomInstructor
1/14/2021 - 4/29/2021 Th 6:00pm - 9:20pm n/a Wei, Wei

Notes
  • Note: Students whose prerequisites are not identified by the system should contact the Math and Statistics Department for an override at MATH@metrostate.edu.
  • Synchronous Completely Online; required meetings.
  • Note: This is a completely online course, not an independent study. Course has required synchronous online meetings that require intermediate computer/Internet skills, webcam and microphone and may require remotely proctored exams. For online learning and course access information go to www.metrostate.edu/solr.
  • Converted Due to COVID-19

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

Seat Availability
Status: Open Size: 24 Enrolled: 19 Seats Remaining: 5

Prerequisites (Courses and Tests)
[STAT 201 - Statistics I]
Add/Drop/Withdraw
Full refund is available until January 15, 2021, 11:59PM CST.
Adding course is closed. Dropping course is closed.
The last day to withdraw from this course is April 12, 2021.

Tuition and Fees (Approximate)

Tuition and Fees (approximate):

Tuition -resident: $965.56
Tuition -nonresident: $1,970.00
Approximate Course Fees: $148.44

Course Level
Undergraduate

Description
An introduction to methods and techniques commonly used in data science. This course will use object-oriented computer programming related to the processing, summarization and visualization of data, which will prepare students to use data in their field of study and to effectively communicate quantitative findings. Topics will include basics in computer programming, data visualization, data wrangling, data reshaping, ethical issues with the use of data, and data analysis using an object-oriented programming language. Students will complete a data science project.

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