Techniques of Data Analysis

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 Brigthspace.

Schedule: Sep 07 – Dec 07
    ■ Mondays, 08:30-10:00, MRT 221
    ■ Wednesdays, 13:00-14:30, MRT 221
    ■ No class on Oct 24 and Oct 26

Zoom link

Projects Deadlines: 23-Sep, 07-Oct, 28-Oct, 18-Nov, 09-Dec, 16-Dec
    ■ Upload completed projects as PDFs to Slack
    ■ 12 pages maximum, no exceptions

Course Notes: (you will need a gmail address to request access to the course notes on RStudio Connect)
    ■ Data Understanding, Data Analysis, and Data Science (DUDADS)
    ■ The Practice of Data Visualization (PDV)

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:
    ■ 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]

Slide Decks:
    ■ Data Fundamentals
    ■ Introduction to Programming
    ■ Programming in R (and Python)
    ■ Data Processing
    ■ Storytelling with Data

Course Notes Chapters:
    ■ Programming Primer (DUDADS)
    ■ Non-Technical Aspects of Data Work (DUDADS)
    ■ Data Science Basics (DUDADS)
    ■ Data Preparation (DUDADS)
    ■ Visualization and Storytelling (PDV)

Slide Deck: Data Exploration and Data Visualization

Course Notes Chapters:
    ■ Data Visualization and Data Exploration (DUDADS)
    ■ Overview (PDV)
    ■ Basics of Data Visualization (PDV)
    ■ Essentials of Visual Design (PDV)
    ■ Practical Aspects of Data Visualization (PDV)

Project 1 (due date: 07-Oct)

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

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:
    ■ Bayesian Data Analysis (DUDADS)

Project 2 (due date: 28-Oct)

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

Slide Decks:
    ■ Basics of Queueing Theory
    ■ CATSA and Queueing Systems

Course Notes Chapter:
    ■ Queueing Systems (DUDADS)

Project 3 (due date: 18-Nov)

Slide Deck: Anomaly Detection and Outlier Analysis

Course Notes Chapter:
    ■ Anomaly Detection and Outlier Analysis (DUDADS)

Project 4 (due date: 09-Dec)

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:
    ■ Feature Selection and Data 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