COMS 4407 A Week 1 Introduction Critical DATA

Week 1 - Agenda • Introductions • Course Outline • Assessment • Readings •

Course Objectives • Distinguish big data from small data, and recognize different types of

13 Weeks / 36 Hours Week 1 (Sept. 8) Introduction Week 2 (Sept. 15)

13 Weeks / 36 Hours Study Break – Oct. 24 – 28 Week 8

Assessment 1, 2 & 4 you must do, you chose between 3&5 or do

Readings • best to read these before class, seminars are just much better when

Wikipedia https: //dashboard. wikiedu. org/courses/Journalism_and_Communication, _Carleton_University/COMS 4407_Critical_Data_Studies_(Fall_ Dr. Tracey P. Lauriault, COMS 4407 A

Definitions Dr. Tracey P. Lauriault, COMS 4407 A 2016, Carleton University https: //doi. org/10.

In-Class Database Exercise • NYTimes Data. Dive Surprising New Evidence Shows Bias in Police

#1 Data Description Assignment (Due @ noon Sept. 15) (10 %) 3 pages: •

Credit Score Trans. Union Equifax Consumer Disclosure Request Form: Canadian Credit File Request Form:

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COMS 4407 A 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 4407 A in the subject line Office: 4110 b River Building Office Hours: Mondays 2: 30 to 5: 30, Thursdays 9: 30 -11: 30. Bookmarks: http: //del. icio. us/tlauriau Dr. Tracey P. Lauriault, COMS 4407 A 2016, Carleton University https: //doi. org/10. 22215/tplauriault. courses. 2016. coms 4407

Week 1 - Agenda • Introductions • Course Outline • Assessment • Readings • Wikipedia • Definitions • In-Class Group Database Activity • Assignment 1 Dr. Tracey P. Lauriault, COMS 4407 A 2016, Carleton University https: //doi. org/10. 22215/tplauriault. courses. 2016. coms 4407

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. Dr. Tracey P. Lauriault, COMS 4407 A 2016, Carleton University https: //doi. org/10. 22215/tplauriault. courses. 2016. coms 4407

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: Tim Beynon, GIS system administrator and developer at Ottawa Police Service. Dr. Tracey P. Lauriault, COMS 4407 A 2016, Carleton University https: //doi. org/10. 22215/tplauriault. courses. 2016. coms 4407

13 Weeks / 36 Hours Study Break – Oct. 24 – 28 Week 8 (Nov. 3) Data Politics, Activism and Cultures • Guest Speaker: Dr. Bjenk Ellefsen, Center for Special Business Projects, Statistics Canada Week 9 (Nov. 10) Data Brokers and Credit Scoring • Guest Speaker: Bob Lytle, Currently with rel 8 ed. to Analytics in Niagara, formerly CIO of Trans. Union Canada and a strong Open Data advocate. (He also knows a little something about sports analytics). Week 10 11 12 13 (Nov. 17) The Rationale for Big Data (Nov. 24) The End of Science? (Dec. 1) Ethical, political, social and legal concerns (Dec. 8) Review Exams December 9 – 22 Dr. Tracey P. Lauriault, COMS 4407 A 2016, Carleton University https: //doi. org/10. 22215/tplauriault. courses. 2016. coms 4407

Assessment 1, 2 & 4 you must do, you chose between 3&5 or do both and I pick the highest mark. Dr. Tracey P. Lauriault, COMS 4407 A 2016, Carleton University https: //doi. org/10. 22215/tplauriault. courses. 2016. coms 4407

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! Dr. Tracey P. Lauriault, COMS 4407 A 2016, Carleton University https: //doi. org/10. 22215/tplauriault. courses. 2016. coms 4407

Wikipedia https: //dashboard. wikiedu. org/courses/Journalism_and_Communication, _Carleton_University/COMS 4407_Critical_Data_Studies_(Fall_ Dr. Tracey P. Lauriault, COMS 4407 A 2016, Carleton University https: //doi. org/10. 22215/tplauriault. courses. 2016. coms 4407

Definitions Dr. Tracey P. Lauriault, COMS 4407 A 2016, Carleton University https: //doi. org/10. 22215/tplauriault. courses. 2016. coms 4407

In-Class Database Exercise • NYTimes Data. Dive Surprising New Evidence Shows Bias in Police Use of Force but Not in Shootings • http: //www. nytimes. com/2016/07/12/upshot/surprising-newevidence-shows-bias-in-police-use-of-force-but-not-inshootings. html? _r=1&module=Arrows. Nav&content. Collection=T he%20 Upshot&action=keypress&region=Fixed. Left&pgtype=arti cle • Fatal Encounters: • http: //www. fatalencounters. org/people-search/ • Washington Post Police Shootings: • https: //www. washingtonpost. com/graphics/national/policeshootings/ • 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-theu. s/2013/crime-in-the-u. s. -2013/offenses-known-to-lawenforcement/expandedhomicide/expanded_homicide_data_table_14_justifiable_homic ide_by_weapon_law_enforcement_2009 -2013. xls • The Counted: • http: //www. theguardian. com/us-news/nginteractive/2015/jun/01/the-counted-police-killings-us-database Dr. Tracey P. Lauriault, COMS 4407 A 2016, Carleton University https: //doi. org/10. 22215/tplauriault. courses. 2016. coms 4407

#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. Dr. Tracey P. Lauriault, COMS 4407 A 2016, Carleton University https: //doi. org/10. 22215/tplauriault. courses. 2016. coms 4407

Credit Score Trans. Union Equifax Consumer Disclosure Request Form: Canadian Credit File Request Form: https: //www. transunion. ca/product/consu https: //helpmer-disclosure#Mail en. equifax. ca/app/answers/detail/a https: //secure_id/300/no. Intercept/1 ocs. transunion. ca/secureocs/authenticatio n-step-1. html Dr. Tracey P. Lauriault, COMS 4407 A 2016, Carleton University https: //doi. org/10. 22215/tplauriault. courses. 2016. coms 4407