Techniques of Data Analysis – Data Action Lab

Course Details	Datasets
Course Outline Course Logistics Instructor: Patrick Boily, uOttawa Communications with the Instructor must be conducted through Slack. The Slack invite link is available in the “Announcements” section on Brightspace. Schedule: Sep 04 – Dec 02 ■ Mondays, 10:00-11:20, STE G0103 ■ Wednesdays, 08:30-09:50, STE G0103 Zoom link (if needed) Projects Deadlines: 20-Sep, 04-Oct, 25-Oct, 15-Nov, 06-Dec, 13-Dec ■ Upload completed projects as PDFs to Slack ■ 10 pages maximum for reports, no exceptions Course Notes: ■ Data Understanding, Data Analysis, and Data Science (DUDADS) ■ The Practice of Data Visualization (PDV)	Detailed Schedule: Project Datasets: ■ polls_us_election_2016 ■ ab_data \| mimic3d ■ BASA_AUC_2028_912 \| dat_F_sub \| data_P_sub \| years20262030 ■ flights1_2019_1
0. Data Basics	1. Data Visualization
Video Lectures: (3:59:29) ■ 0.1 Data Fundamentals \| Part 1 (0:39:56) ■ 0.1 Data Fundamentals \| Part 2 (0:39:08) ■ 0.2 Basics of R \| Part 1 (0:25:50) ■ 0.2 Basics of R \| Part 2 (0:25:01) ■ 0.3 Tidyverse for Data Wrangling (0:37:44) ■ 0.4 Data Processing \| Part 1 (0:34:23) ■ 0.4 Data Processing \| Part 2 (0:37:27) Slide Decks: ■ Data Fundamentals ■ Introduction to Programming ■ Programming in R (and Python) ■ Data Processing ■ Storytelling with Data Course Notes Chapters: ■ DUDADS, ch. 1: Programming Primer ■ DUDADS, ch. 13: Non-Technical Aspects of Data Work (DUDADS) ■ DUDADS, ch. 14: Data Science Basics ■ DUDADS, ch. 15: Data Preparation ■ PDV, ch. 7: Stories and Storytelling ■ PDV, ch. 8: Effective Storytelling Visuals	Video Lectures: (4:28:04) ■ 1.1 Data Exploration & 1.2 Pre-Analysis Visualization (1:16:00) ■ 1.3 Post-Analysis Visualization & 1.4 Visualization Catalogue (1:13:35) ■ 1.5 Grammar of Graphics & 1.6 Introduction to Dashboards (1:11:55) ■ 1.7 Graphics with ggplot2 \| Part 1 (0:19:32) ■ 1.7 Graphics with ggplot2 \| Part 2 (0:27:02) Slide Deck: Data Exploration and Data Visualization Course Notes Chapters: ■ PDV, ch. 1: A Data Visualization Primer ■ PDV, ch. 2: Data Visualization and Exploration ■ PDV, ch. 4: The Mechanics of Visual Perception ■ PDV, ch. 5: Visual Design and Data Charts ■ PDV, ch. 6: Universal Design and Accessibility ■ PDV, ch. 9: Visualization Toolbox ■ PDV, ch. 10: Visualization Software ■ PDV, ch. 12: ggplot2 Visualizations in R Project 1 (due date: 04-Oct) Supplementary Materials: ■ DAL Podcast Episodes: 17 episodes (9:07:38)
2. Bayesian Data Analysis	3. Queueing Systems
Video Lectures: (4:06:59) ■ 2.1 Plausible Reasoning & 2.2 The Rules of Probability & 2.3 Bayes’ Theorem (1:13:48) ■ 2.3 Bayes’ Theorem (cont.) & 2.4 Examples & 2.5 Prior Distributions (1:07:30) ■ 2.5 Prior Distributions (cont.) (0:42:10) ■ 2.6 Naïve Bayes Classification & 2.7 MCMC and Numerical Methods (1:03:30) Slide Deck: A Cursory Glance at Bayesian Analysis Course Notes Chapter: ■ DUDADS, ch. 26: Bayesian Data Analysis Project 2 (due date: 25-Oct) Supplementary Materials: ■ R Code Archive (from Kruschke’ Doing Bayesian Analysis) ■ Tutorial – Coin (Excel) ■ Tutorial – Dollar Bills (Notebook) ■ Tutorial – Planes (Excel) ■ Tutorial – Salaries (Excel)	Video Lectures: (2:39:29) ■ 3.1 Introduction & 3.2 Terminology & 3.3 Notation (0:46:05) ■ 3.4 Little’s Queueing Formula & 3.5 The M/M/c Queueing Model (00:46:16) ■ 3.6 Example: Canadian Airports (01:07:08) Slide Decks: ■ Basics of Queueing Theory ■ CATSA and Queueing Systems Course Notes Chapter: ■ DUDADS, ch. 25: Queueing Models Project 3 (due date: 15-Nov)
4. Anomaly Detection and Outlier Analysis
Video Lectures: (5:51:03) ■ 4.1 Basic Notions and Overview (00:42:36) ■ 4.1 Basic Notions and Overview (cont.) (00:36:32) ■ 4.2 Quantitative Methods (00:52:31) ■ 4.2 Quantitative Methods (cont.) (00:34:35) ■ 4.3 Qualitative Methods (00:20:46) ■ 4.4 Anomalies in High-Dimensional Datasets (00:29:49) ■ 4.4 Anomalies in High-Dimensional Datasets (cont.) & 4.5 Outlier Ensembles (00:31:32) ■ 4.6 Anomalies in Text Datasets (00:29:16) Slide Deck: Anomaly Detection and Outlier Analysis Course Notes Chapter: ■ DUDADS, ch. 27: Anomaly Detection and Outlier Analysis Project 4 (due date: 06-Dec)