Course outline:

Lectures:

Instructors:

• email: olga.baysal@carleton.ca
• office: HP 5125D
• office hours: by appointment or via Slack
• website: http://olgabaysal.com/teaching/winter16/data5000.html

• email: boyanbejanov@cmail.carleton.ca
• office: N/A
• office hours: by appointment or via Slack
• website: http://scs.carleton.ca/~boyanbejanov/data5000

Marking:

• 10% Project proposal
• 10% Presentation outline
• 30% Presentation in class
• 50% Project paper

Resources:

• Doing Data Science: Straight Talk From the Frontline,
  by Cathy O'Neil and Rachel Schutt, O'Reilly Media, 2013.
• Data Mining and Business Analytics with R,
  by Johannes Ledolter, Wiley, 2013.
• Data Science for Business: what you need to know about data mining and data-analytic thinking,
  by Foster Provost and Tom Fawcett, O'Reilly Media, 2013.

• An Introduction to Statistical Learning: with Applications in R,
  by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani, Springer, 2013.
• The Elements of Statistical Learning: Data Mining, Inference, and Prediction,
  by Trevor Hastie, Robert Tibshirani and Jerome Friedman, Springer, 2011.

• Cookbook for R by Winston Chang.
• The R Inferno by Patrick Burns.
• Quick-R.
• Software for Data Analysis Programming with R,
  by John Chambers, Springer, 2008.

Datasets for projects:

• Bayesian Network-Based Log Generation (PDF)
• Behavioral Models for the Detection of Unusual Asset Access (PDF)
• Behavioral Models and Exploratory Data Analysis (PDF)

• Bank Note Circulation Dataset (PDF)

• GitHub repository via GHTorrent
• MSR Mining Challenge datasets
• Tera-PROMISE repository
• Open Data @ Government of Canada
• Machine learning data set repo
• IAPR
• Datamob
• KDnuggets
• in R

Lecture slides:

• Introduction to Data Science
• Introduction to R

• Working With Data

• Visualization and Exploration
• Managing Data
• custdata.tsv

• Data Minig and Machine Learning
• Extensions of Linear Regression

• Data Minig and Machine Learning Part 2. Data: pts_2clusters.csv, MarketBasket.csv
• Introduction to NoSQL

University Policies:

Academic Integrity

Academic Integrity is everyone’s business because academic dishonesty affects the quality of every Carleton degree. Each year students are caught in violation of academic integrity and found guilty of plagiarism and cheating. In many instances they could have avoided failing an assignment or a course simply by learning the proper rules of citation. See the academic integrity for more information.

Academic Accommodations for Students with Disabilities

The Paul Menton Centre for Students with Disabilities (PMC) provides services to students with Learning Disabilities (LD), psychiatric/mental health disabilities, Attention Deficit Hyperactivity Disorder (ADHD), Autism Spectrum Disorders (ASD), chronic medical conditions, and impairments in mobility, hearing, and vision. If you have a disability requiring academic accommodations in this course, please contact PMC at 613-520-6608 or pmc@carleton.ca for a formal evaluation. If you are already registered with the PMC, contact your PMC coordinator to send me your Letter of Accommodation at the beginning of the term, and no later than two weeks before the first in-class scheduled test or exam requiring accommodation (if applicable). After requesting accommodation from PMC, meet with me to ensure accommodation arrangements are made. Please consult the PMC website for the deadline to request accommodations for the formally-scheduled exam (if applicable).

Religious Obligation

Write to the instructor with any requests for academic accommodation during the first two weeks of class, or as soon as possible after the need for accommodation is known to exist. For more details visit the Equity Services website.