Course Details Datasets

Course Outline

Instructor: Patrick Boily, uOttawa, Data Action Lab & Idlewyld Analytics
       Linked In profile
       Follow @idlewyld_IACS on Twitter (#iqc4376) for Data Visualization content.

       Mondays, 08:30-10:00, Sep 14 – Dec 07
       Wednesdays, 13:00-14:30, Sep 09 – Dec 09
       No class on Oct 12, Oct 26, and Oct 28
       The session on Dec 09 takes place from 08:30-10:00

Zoom link
       Meeting ID: 784 099 4865
       Passcode: 7RXJ9J

Projects Deadlines: 30-Sep, 15-Oct, 31-Oct, 15-Nov, 5-Dec, 21-Dec.
       Completed projects should be uploaded as PDF documents to Brightspace
       15 pages maximum, no exceptions

Distracted Driving Fatalities
Flights Read Me
0. Data Analysis Universals 1. Data Visualization
Slide Deck: Data Analysis Universals

Video Lectures:
       Ethical Considerations (16:01)
       Guiding Principles (10:46)
       Asking the Right Questions (04:45)
       The Structure of Data (19:07)
       Quantitative Analysis Workflow (28:48)

Project (due date: 30-Sep)

Slide Deck: Data Exploration and Data Visualization

Video Lectures:
       14-Sep (01:16:00)
       16-Sep (01:13:35)
       21-Sep (01:11:55)

DAL Podcast Episodes:
       Episode 3: Minard’s March to Moscow (19:28)
       Episode 6: Storytelling with Data (26:02)
       Creating a Gapminder-Type Chart with ggplot2 (19:32)

Project (due date: 15-Oct)

Supplementary Materials:
       A ggplot2 Primer
       Tufte’s Fundamental Principles of Analytical Design
       Data Visualization and Representation
       Dashboards and Data Visualization, with Examples
       Simple Data Visualization in R
       Data Visualization with ggplot2
       More Data Visualization Stuff in R

2. Bayesian Data Analysis 3. Queueing Systems
Slide Deck: A Cursory Glance at Bayesian Analysis

Video Lectures:
       23-Sep (01:13:48)
       28-Sep (01:07:30)
       30-Sep (00:42:10)
       05-Oct (01:03:30)

Project (due date: 31-Oct)

Course Notes: A Soft Introduction to Bayesian Data Analysis

Supplementary Materials:
       R Code Archive (from Kruschke’ Doing Bayesian Analysis)
       Tutorial – Coin
       Tutorial – Dollar Bills
       Tutorial – Planes
       Tutorial – Salaries

Slide Decks:
       Basics of Queueing Theory
       CATSA and Queueing Systems

Video Lectures:
       07-Oct (00:46:05)
       14-Oct (00:46:16)
       19-Oct (01:07:08)

Project (due date: 15-Nov)

Course Notes: The Essentials of Queueing Systems Methods

4. Feature Selection and Data Reduction 5. Anomaly Detection and Outlier Analysis
Slide Deck: Feature Selection and Dimension Reduction

Video Lectures:
       21-Oct (01:13:30)
       02-Nov (00:44:01)
       04-Nov (00:30:15)
       09-Nov (00:28:45)

Project (due date: 5-Dec)

Course Notes: Feature Selection and Data Reduction (with Examples)

Supplementary Materials:
      R Code Archive (not commented)
      Python Notebooks Archive (install Anaconda to view)

Slide Deck: Anomaly Detection and Outlier Analysis

Video Lectures:
       11-Nov (00:42:36)
       16-Nov (00:36:32)
       18-Nov (00:52:31)
       23-Nov (00:34:35)
       25-Nov (00:20:46)
       30-Nov (00:29:49)
       02-Dec (00:31:32)
       05-Dec (00:29:16)

Project (due date: 21-Dec)

Course Notes: Anomaly Detection and Outlier Analysis

Supplementary Materials: R Code Archive for the Slides (not commented)