Teaching Students About Realities of Data at Cornell College
Explore the innovative approach taken by Cornell College in teaching students about data through hands-on courses covering data cleaning, visualization, and big data concepts. The curriculum includes a blend of statistics and computer science, providing a comprehensive understanding of handling and analyzing data effectively.
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Messy Data: Teaching Students Early on About the Realities of Data
Cornell College Small liberal arts college (1100 students) Mathematics and Statistics Department with 4.5 tenure track lines Teach on the block plan
Statistics History at Cornell Intro stat Probability/Math Stat Stat 2 New Frontiers Epidemiology Dealing with Data: Data Manipulation, Data Visualization, and Big Data
Data Course Team taught with computer scientist Prerequisite either intro stat or CS 1 Focused on hands-on Morning was two hours of lecture Afternoon was two hours of computer lab
Data Course - Plan 1/3 of the course on each topic Data Cleaning Data Visualization Big Data Relevant computer science fundamentals addressed in a just-in- time fashion Use R as the software tool
Data Course - Reality 1/3 Data Cleaning 1/2 Data Visualization 1/6 Big Data
Daily Structure Morning 2 hours M-Thur: Lecture 1 hour stat 1 hour CS Fri : Student presentations Afternoon 2 hours Computer lab
Data Cleaning Simple issues Clearly wrong entries Potentially wrong entries Functions of a variable
Data Cleaning More complex issues Combining data sets Linking variable issues Making sure data sets are combined properly Different variable formatting in different data sets
Data Visualizations Look at published visualizations Discuss ways to improve published visualizations Specific visualizations created: Stream graphs Tree graphs Maps
Big Data Described big data Volume Velocity Variety Discussed computer science issues MapReduce Hadoop
Projects 3 Projects Chapter 2 of Data Science in R: A Case Studies Approach to Computational Reasoning and Problem Solving by Deborah Nolan and Duncan Temple Lang Twitter project Group project
Project 1 Introduce students to R 10 years of data from the Cherry Blossom Road Race in DC Lots of data cleaning Introduced some visualization issues with larger data sets Introduced the idea of smoothing
Project 1 Done in pairs Deliberately formed with one stat and one cs student In class work following the steps given for the men s data Written report due for women s data Includes both code and statistical report
Project 2 Download public tweets Filter for a query term Assign a sentiment score Aggregate tweets by state Produce geographic visualization of data
Project 2 Again done in cs/stat pairs Final report Required an extension of the basic lab Required both code and statistical report
Project 3 Term-long 4-person group project First week Individual brainstorming about topics Friday morning elevator pitches Second week Teams find data and refine goals Friday morning check-in report from all teams class feedback provided
Project 3 Third week Lab time devoted to project Finish data cleaning and do much of the analysis Friday morning check-in report from all teams class feedback provided
Project 3 Last 3 days of class Finishing touches on the analysis Create project website Final presentation to both class and other visitors
Lessons Learned Slower introduction to R Small individual assignments as we go More faculty input for statistical analysis of group projects
For more information Ann Cannon Department of Math and Stat Cornell College 600 First St SW Mt. Vernon, IA 52314 (319) 895-4461 acannon@cornellcollege.edu
