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Data Science

Spring 2019 - Section 002 - Larry Dignan

Data Science

MIS 0855.002 ■ SPRING 2019 ■ LARRY DIGNAN
  • Announcements
  • Schedule
  • About
    • Course Objectives & Policies
    • Evaluation & Grading
    • Getting Tableau Desktop
    • Readings
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Readings

For links that take you to the Temple library, search for the article title using Summon.

MODULE 1

Session 1.2: January 14

  • Data Science and Prediction (Dhar)  (This link will take you to the library page.  Type in the title and view the PDF for the article)
  • Three Science Words We Should Stop Using (Allain)

Session 2.1: January 28

  • I’m Beating the NSA to the Punch by Spying on Myself (Stein)
  • What the NSA Wants to Know About Your Phone Calls (Di Justo)
  • The Ashley Madison Hack Is Only the Beginning (Aguilar)

Session 2.2:

  • What the Fox Knows (Silver)
  • Open Data (Wikipedia)
  • In Search of America’s Best Burrito (Silver)

Session 3.1: Feb. 4

  • Bubble Trouble: Is Web Personalization Turning Us into Solipsistic Twits? (Weisberg)
  • The Hidden Biases in Big Data (Crawford)
  • In Data We Trust (Hayes)

MODULE 2

Session 4.1: Feb. 11

  • Chapter 2: Good Graphics? Handbook of Data Visualization (Unwin–pages 57-77)

Session 4.2:

  • Stephen Few on Data Visualization: 8 Core Principles (Hoven)
  • Watch out, Terrorists: Big Data is on the Case (Acohido)

Session 5.1: February 18

  • Telling a Story with Data (Davenport)
  • Visualizing a Day in the Life of a New York City Cab (Matlin)

Session 5.2:

  • Chapter 1: The Science of Infographics. Cool Infographics (Krum)
  • Chapter 6: Designing Infographics. Cool Infographics (Krum)

MODULE 3

Session 7.1: March 11

  • Data’s Credibility Problem (Redman)
  • Damn Excel! How the ‘Most Important Software Application of All Time’ is Ruining the World (Gandel)

Session 7.2:

  • Stupid Data Corruption Tricks (Taber)
  • Top Ten Ways to Clean Your Data (Microsoft)

Session 8.1: March 18

  • Performance Indicator (Wikipedia)
  • The Tyranny of Success: Nonprofits and Metrics (Schambra)

Session 8.2:

  • Wearable Tech is Plugging Into Health Insurance (Olson)
  • Tracking Health One Step at a Time (Bialik)

Session 9.1: March 25

  • How Data Integration Works (Strickland)
  • The GOP Arms Itself for the Next “War” in the Analytics Arms Race (Gallagher)

Session 9.2:

  • Best Practices for Designing Views and Dashboards (Tableau)
  • The One Skill You Really Need for Data Analysis (Farmer)

MODULE 4

Session 11.1: April 8

  • Knowing Just Enough about Relational Databases (Rosenblum and Dorsey)
  • How To Explain Hadoop to Non-Geeks (Bertolucci)

Session 11.2:

  • How to Structure Source Data for Excel Pivot Tables & Unpivot (Acampora)  NOTE: Use firefox or Chrome to open.

Session 12.1: April 15

  • Unstructured Data in a Big Data Environment (Hurwitz et al.)
  • Techniques and Applications for Sentiment Analysis (Feldman)

Session 12.2:

  • Don’t Worry, Facebook Still Has No Clue How You Feel (Wohlsen)

Session 13.1: April 22

  • What Analytics Can Teach Us About the Beautiful Game (Paine)
  • Big Data Analytics: Descriptive vs. Predictive vs. Prescriptive (Bertolucci)

Session 13.2:

  • They’re Watching You at Work (Peck)

Primary Sidebar

Recent Announcements

  • Final Exam Reminder
  • Study Guide for Exam 3 (Final Exam)
  • In class exercise 13.2 driver download
  • In-Class Exercise 13.2: Simple Predictive Analytics
  • Primer on descriptive, prescriptive, predictive analytics

For student help:

Larry Dignan

Phone: 267.614.6467

Office hours: 5:00-5:30pm and 8 to 8:30pm, Mondays, main campus; By appt via Zoom, Skype, FaceTime etc.

ITA: Nhi Nguyen

Great Data Sites

  • FiveThirtyEight
  • Guardian Data Blog
  • Flowing Data
  • Financial Times Data Blog
  • Socrata Open Data
  • Pew Research Data

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