Data Science for All: A University-Wide Course in Data Literacy

Abstract

Infusing data literacy into a curriculum is an unrealized opportunity for higher education to truly make an impact on the current generation as they prepare to move into the workforce. This chapter describes the design and structure of a new, unique undergraduate elective course introduced into the curriculum of a large, public University in the Northeastern United States. The design of the course is designed to inspire an “evidence-based” mindset, encouraging students to identify and use data relevant to them in their field of study and the larger world around them. The chapter includes the course goals mapped to specific learning objectives, examples of exercises and assignments, a reading list, and a course syllabus. Instructors and institutions interested in bringing data science concepts to a broad audience can use this course as a foundation to build their own curriculum in this area.

Appendix: Abbreviated Course Syllabus for Data Science

20.1.1 Course Description

We are all drowning in data, and so is your future employer. Data pours in from sources as diverse as social media, customer loyalty programs, weather stations, smartphones, and credit card purchases. How can you make sense of it all? Those that can turn raw data into insight will be tomorrow’s decision-makers; those that can solve problems and communicate using data will be tomorrow’s leaders. This course will teach you how to harness the power of data by mastering the ways it is stored, organized, and analyzed to enable better decisions. You will get hands-on experience by solving problems using a variety of powerful, computer-based data tools virtually every organization uses. You will also learn to make more impactful and persuasive presentations by learning the key principles of presenting data visually.

20.1.2 Course Objectives

• Describe how advances in technology enable the field of data science

• Locate sources of data relevant to their field of study

• Identify and correct problems with data sets to facilitate analysis

• Combine data sets from different sources

• Assess the quality of a data source

• Convey meaningful insights from a data analysis through visualizations

• Analyze a data set using pivot tables

• Determine meaning in textual data using text mining

• Identify when advanced analytics techniques are appropriate

• Predict events that will occur together using association mining

20.1.3 Assignments

#	Assignment description
1	Create a data analysis plan (individual) Develop a plan for data analysis by forming hypothesis and finding data sets that will allow you to test those hypotheses. The scenario: Once students graduate, it’s time for them to go get a job. But is staying in the area the best choice? Evaluate our city as a place to live, work, and play compared to the rest of the United States
2	Analyze a data set using tableau (individual) Use Tableau to analyze and reveal various relationships within a data set. Use the data set from the Environmental Protection Agency regarding fuel economy 2015 model year cars. Answer a series of questions by creating the most visually effective charts and graphs using the guidelines discussed in class
3	Cleaning a data set (individual) Correct the errors in a data set for the fictitious company “Vandelay Industries.” The sales group is suspicious that there might be errors in the data for January. Work with a new data set of 3296 orders with 5192 line items from January 2014
4	Group data analysis (group term project) In groups, perform an original analysis on a data set of your choosing. The data set can come from any source as long as it is something you have not already worked on for this course. Possible sources of data include: open data from Data.gov, data sets from the Pew Research Center, sports statistics, a data set from your current employer, or an original survey conducted by your group Your analysis should clearly demonstrate the tools and techniques you’ve been exposed to in this course. This can take any form you’d like (i.e., comparison of averages across categories, mapping geographic data, sentiment analysis, developing and visualizing KPIs) Your group will present your work in class through a five-min presentation, with 2 min for questions

20.1.4 Schedule and Reading List (Current Configuration Is for Two 80-min Sessions per Week)

Week/session	Topic/key questions	Readings
Module 1: Data in our daily lives
1.1	Introduction  • Course introduction/syllabus  • What is the difference between data, information, and knowledge?  • What makes “big data” big?
1.2	Science and data science  • What is data science?  • What is the difference between a theory and a hypothesis?  • What are the dangers of data analysis without a hypotheses?	Dhar, V. (2013). Data Science and Prediction. Communications of the ACM. Vol. 56, No. 12. pp. 64–73 Allain, R. (2013). Three Science Words We Should Stop Using. Wired.com. March 27
2.1	A brief introduction to data  • What are the forms data can take?  • Where does data come from?  • What is metadata? A data dictionary?	Stein, G. (2013). I’m Beating the NSA to the Punch by Spying on Myself. Fastcolabs.com. June 12 Di Justto, P. (2013). What the N.S.A. Wants to Know about Your Phone Calls. The New Yorker. June 7
2.2	Identifying Sources of Data  • What kinds of data are available in different disciplines (arts, sciences, medicine, business, government, etc.)?  • What kinds of problems and issues can data insight address?	Silver, N. (2014). What the Fox Knows. FiveThirtyEight.com. March 17 Open Data. Wikipedia Silver, N. (2014). In Search of America’s Best Burrito. FiveThirtyEight.com. June 5
3.1	Learning to (Mis)trust Data  • How do you spot reliable sources of data?  • How do you assess data quality?  • What is the “Filter Bubble?”	Weisberg, J. (2011). Bubble Trouble: Is Web Personalization Turning Us Into Solipsistic Twits? Slate.com Crawford, K. (2013). The Hidden Biases in Big Data. Harvard Business Review Blog Network. April 1 Hayes, B. (2013). In Data We Trust. Business Over Broadway. November 4
3.2	Guest speaker
Module 2: Telling stories with data
4.1	Viewing data  • What are different ways of viewing data?  • When do you need to visualize data?  • What are the basic techniques of data visualization?	Unwin, A. (2008). Chapter II.2: Good Graphics? Handbook of Data Visualization. Chen, Hardle, and Unwin (Eds.). pp. 57–78
4.2	Introduction to Tableau  • What is Tableau? What can you do with it?  • How is it different from Microsoft Excel?	Hoven, N. (n.d.). Stephen Few on Data Visualization: 8 Core Principles. Tableau Software Acohido, B. (2013). Watch Out, Terrorists: Big Data is on the Case. USAToday.com. July 29
5.1	Communicating using data  • What are the principles of communicating data?  • How do you communicate complex ideas using data?  • How do you construct visualizations that complement a report? That stand on their own?	Davenport, T. (2013). Telling a Story with Data. Deloitte University Press Matlin, C. (2014). Visualizaing a Day in the Life of a New York City Cab. FiveThirtyEight.com. July 17
5.2	Storytelling with infographics  • How are infographics different from other types of visualizations?  • How do infographic tools differ from other data tools we’ve used so far?	Krum, R. (2014). Cool infographics: Effective Communication with Data Visualization. (Chapter 1: The Science of Infographics) Krum, R. (2014). Cool infographics: Effective Communication with Data Visualization. (Chapter 6: Designing Infographics)
6.1/6.2	Exam review/EXAM 1
Module 3: Working with data in the real world
7.1	Dirty Data  • How does data get dirty?  • What are the consequences (i.e., ethical, financial) of dirty data?  • How do you clean it?	Redman, T. (2013). Data’s Credibility Problem. Harvard Business Review. Vol. 91, No. 12. pp. 84–88 Gandel, S. (2013). Damn Excel! How the ‘Most Important Software Application of All Time’ Is Ruining the World. Fortune.com. April 17
7.2	Data cleansing  • How do you identify data problems?  • How do you correct data problems?  • When is fixing the data not worth it?	Taber, D. (2010). Stupid Data Corruption Tricks: Take our CRM Quiz. CIO.com. November 2 Top Ten Ways to Clean Your Data. Microsoft
8.1	Choosing relevant data  • How do you identify Key Performance Indicators (KPIs)?  • How do you identify the right measure for the selected problem?	Performance Indicator. Wikipedia Schambra, W. (2013). The Tyranny of Success: Nonprofits and Metrics. NonprofitQuarterly.com. December 30
8.2	Evaluating key performance indicators  • How do you categorize and visualize KPIs according to a threshold?  • How do you use Tableau to evaluate KPIs? How would you use Excel?	Olson, P. (2014). Wearable Tech is Plugging into Health Insurance. Forbes.com. June 19 Bialik, C. (2014). Tracking Health One Step (and Clap, and Wave, and Fist Pump) at a Time. FiveThirtyEight.com. March 17
9.1	Connecting diverse data  • How do you identify data sets that can be combined?  • How do you combine data sets?  • How do you resolve conflicts?	Strickland, J. (n.d.). How Data Integration Works. howstuffworks.com Gallagher, S. (2014). The GOP Arms Itself for the Next “War” in the Analytics Arms Race. arstechnica.com. February 7
9.2	Creating interactive dashboards  • How does a dashboard differ from an Infographic? A chart?  • How do dashboards facilitate decision-making?	Best Practices for Designing Views and Dashboards. Tableau Software Farmer, D. (2014). The One Skill You Really Need for Data Analysis
10.1/10.2	Exam Review/EXAM 2
Module 4: Analyzing data
11.1	Storing and retrieving data  • What is a database? How are spreadsheets just a type of database?  • How are technology advances changing how we think about storing data?  • What are the core technologies of big data analytics?	Rosenblum, M. and Dorsey, P. (n.d.). Knowing Just Enough about Relational Databases. Dummies.com Bertolucci, J. (2013). How to Explain Hadoop to Non-Geeks. InformationWeek.com. November 19
11.2	Using Tableau to aggregate data  • What can you learn from aggregation?  • How does thinking of data dimensionally help solve problems?	Acampora, J. (2013). How to Structure Source Data for Excel Pivot Tables & Unpivot. July 18
12.1	Beyond numbers  • What is the difference between structured and unstructured data?  • What can you learn from text data that you can’t from numeric data?  • What are the tools for text analysis?	Hurwitz, J., Nugent, A., Halper, F., and Kaufman, M. (n.d.). Unstructured Data in a Big Data Environment. Dummies.com Feldman, R. (2013). Techniques and Applications for Sentiment Analysis. Communications of the ACM. Vol. 56, No. 4. pp. 82–89
12.2	Twitter sentiment analysis using Excel and Google Drive  • What are the steps in performing a sentiment analysis?  • What are the challenges in deriving meaningful information from text?	Wohlsen, M. (2014). Don’t Worry, Facebook Still Has No Clue How You Feel. Wired.com. July 2
13.1	Predicting the future  • What is predictive analytics? What problems does it address?  • What kinds of analysis can be done?  • What kinds of data are needed for an analysis?	Paine, N. (2014). What Analytics Can Teach Us About the Beautiful Game. June 12 Bertolucci, J. (2013). Big Data Analytics: Descriptive vs. Predictive vs. Prescriptive. InformationWeek.com. December 31
13.2	Predictive analytics using Tableau  • Perform a forecasting analysis  • Perform a simple association analysis	Peck, D. (2013). They’re Watching You at Work. TheAtlantic.com. November 20
13.1/13.2	Group presentations/FINAL EXAM review