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COMS4407A Week 1: Introduction Critical DATA Studies

COMS4407A Week 1: Introduction Critical DATA Studies. Class Schedule: Thursdays, 14:35 - 17:25 Location: River Building 3224 Instructor: Dr. Tracey P. Lauriault E-mail: Tracey.Lauriault@Carleton.ca include COMS 4407A in the subject line Office: 4110b River Building

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COMS4407A Week 1: Introduction Critical DATA Studies

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  1. COMS4407AWeek 1: IntroductionCritical DATA Studies Class Schedule: Thursdays, 14:35 - 17:25 Location: River Building 3224 Instructor:Dr. Tracey P. Lauriault E-mail:Tracey.Lauriault@Carleton.ca include COMS 4407A in the subject line Office: 4110b River Building Office Hours: Mondays 2:30 to 5:30, Thursdays 9:30-11:30. Bookmarks: http://del.icio.us/tlauriau

  2. Week 1 - Agenda • Introductions • Course Outline • Assessment • Readings • Wikipedia • Definitions • In-Class Group Database Activity • Assignment 1

  3. Course Objectives • Distinguish big data from small data, and recognize different types of data; • conceptualize data as part of socio-technological and political processes, as a form of discourse and as media; • identify data politics and critically read data policies; and • think about data-based knowledge, the construction of facts and the framing of the truth.

  4. 13 Weeks / 36 Hours Week 1 (Sept.8) Introduction Week 2 (Sept. 15) Conceptualizing data? Week 3 (Sept. 22) Open Data & Indicators • Guest Speaker: Robert Giggey, Program Manager, Content Design & Development Service Ottawa, City of Ottawa. Week 4 (Sept. 29) Open Government, Public Policy and Citizen Engagement • Guest Lecturer: Dr. Mary Francoli Week 5 (Oct. 6) The Characteristics of Big Data Week 6 (Oct. 13) The Enablers of Big Data Week 7 (Oct. 20) Data Science & Data Analytics • Guest Speaker:TimBeynon, GIS system administrator and developer at Ottawa Police Service.

  5. 13 Weeks / 36 Hours Study Break – Oct. 24 – 28 Week 8 (Nov. 3) Data Politics, Activism and Cultures • Guest Speaker: Dr. BjenkEllefsen, Center for Special Business Projects, Statistics Canada Week 9 (Nov. 10) Data Brokers and Credit Scoring • Guest Speaker: Bob Lytle, Currently with rel8ed.to Analytics in Niagara, formerly CIO of TransUnion Canada and a strong Open Data advocate. (He also knows a little something about sports analytics). Week 10 (Nov. 17) The Rationale for Big Data Week 11 (Nov. 24) The End of Science? Week 12 (Dec. 1) Ethical, political, social and legal concerns Week 13 (Dec. 8) Review Exams December 9 – 22

  6. Assessment 1, 2 & 4 you must do, you chose between 3 & 5 or do both and I pick the highest mark.

  7. Readings • Readings • best to read these before class, seminars are just much better when you do. • The papers beyond the book are in ARES. • In-Class discussion • datasets, portals, indicators, reports or resources - do not need to be read before class. You will however want to have copies of these on your electronic devices as we will do in-class exercises that relate to these. • Being familiar with them is a good thing though!

  8. Wikipedia https://dashboard.wikiedu.org/courses/Journalism_and_Communication,_Carleton_University/COMS4407_Critical_Data_Studies_(Fall_2016)?enroll=hcplvzda

  9. Definitions

  10. In-Class Database Exercise • Fatal Encounters: • http://www.fatalencounters.org/people-search/ • Washington Post Police Shootings: • https://www.washingtonpost.com/graphics/national/police-shootings/ • Mapping Police Violence: • http://mappingpoliceviolence.org/ • Killed by Police: • http://killedbypolice.net/ • FBI Justifiable Homicide: • https://www.fbi.gov/about-us/cjis/ucr/crime-in-the-u.s/2013/crime-in-the-u.s.-2013/offenses-known-to-law-enforcement/expanded-homicide/expanded_homicide_data_table_14_justifiable_homicide_by_weapon_law_enforcement_2009-2013.xls • The Counted: • http://www.theguardian.com/us-news/ng-interactive/2015/jun/01/the-counted-police-killings-us-database • NYTimesDataDive Surprising New Evidence Shows Bias in Police Use of Force but Not in Shootings • http://www.nytimes.com/2016/07/12/upshot/surprising-new-evidence-shows-bias-in-police-use-of-force-but-not-in-shootings.html?_r=1&module=ArrowsNav&contentCollection=The%20Upshot&action=keypress&region=FixedLeft&pgtype=article

  11. #1 Data Description Assignment(Due @ noon Sept. 15) (10 %) 3 pages: • Look for a CANADIAN dataset related to police shootings, homicides, crime, or gun ownership, etc. • Consider the in-class dataset exercise & describe the dataset. • Write a brief assessment of this dataset. • Some suggestions: • Are there any potential biases? • What are the methodological strengths and limitations of this dataset? • What is not being measured? • Could these data be used to inform public policy? • If you were to use them would you include any cautionary notes? • Do you trust these data? • Find a news article that refers to these data & consider whether or not the article accurately reported the issue. This is descriptive precise writing, this is not an essay, think of it as a data-based annotated bibliography.

  12. Credit Score

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