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 06 – Dec 06
    ■ Mondays, 08:30-09:50, STE J0106
    ■ Wednesdays, 13:00-14:30, STE J0106

Zoom link (if needed)

Projects Deadlines: 22-Sep, 06-Oct, 27-Oct, 17-Nov, 08-Dec, 15-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:
Schedule

Project Datasets:
    ■ polls_us_election_2016
    ■ ab_data | mimic3d
    ■ BASA_AUC_2028_912 | dat_F_sub | data_P_sub | years20262030
    ■ flights1_2019_1

Sample Reports: (may not apply to course)
    ■ Classification Project, [data]
    ■ Short write-up [B; although I like how it is written in terms of the “Multiple I’s”, there are too many missing technical details, it is too short, it includes no code, and it doesn’t really do what was required in the project]
    ■ Long write-up [A-; I like the “Multiple I’s” approach on this one, but it is a tad too long and doesn’t entirely do what was asked for in the project statement]

0. Storytelling with Data (06-Sep to 15-Sep) 1. Data Visualization (18-Sep to 06-Oct)
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: 06-Oct)

Supplementary Materials:
    ■ DAL Podcast Episodes: 17 episodes (9:07:38)

2. Bayesian Data Analysis (09-Oct to 27-Oct) 3. Queueing Systems (30-Oct to 17-Nov)

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: 27-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: 17-Nov)
4. Anomaly Detection and Outlier Analysis (20-Nov to 08-Dec) Supplementary Material
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: 30-Jul)

Slide Deck: Feature Selection and Dimension Reduction (DUDADS)

Video Lectures:
    ■ Part 1 (01:13:30)
    ■ Part 2 (00:44:01)
    ■ Part 3 (00:30:15)
    ■ Part 4 (00:28:45)

Course Notes Chapter:
    ■ DUDADS, ch. 19: Introduction to Machine Learning
    ■ DUDADS, ch. 20: Regression and Value Estimation
    ■ DUDADS, ch. 21: Classification and Supervised Learning
    ■ DUDADS, ch. 22: Clustering
    ■ DUDADS, ch. 23: Feature Selection and Dimension Reduction

Supplementary Datasets:
    ■ Canada2011
    ■ Canada2011_CMA
    ■ Algae_Blooms
    ■ 2016collisionsfinal.csv
    ■ HR_2016_Census_simple
    ■ GlobalCitiesPBI
    ■ ab_data
    ■ mimic3d
    ■ DraftData
    ■ BASA_AUC_2028_912
    ■ dat_F_sub
    ■ data_P_sub
    ■ years20262030
    ■ Distracted Driving Fatalities
    ■ flights1_2019_1
    ■ Flights Read Me