The course will be lecture-based and will also offer some hands-on tutorials. The project component will be flexible and will involve data collection, manipulation, and analysis. For further details on the course content, please refer to its outline (pdf). This course is offered by the School of Computer Science at the Carleton University.

Seminars are held every Thursday from 11:35 AM to 2:25 PM in PA 133 (Olga's class) and PA 115 (Elio's class).

Announcements

• We will be using Slack for course communication, news and reminders. Please join DATA 5000 channel by September 6.
• Project teams must be formed and emailed to instructor no later than September 12.
• Welcome to DATA 5000! Lectures start on September 6th.

The course covers topics relevant to data science: working with data, exploratory data analysis, data mining, machine learning. The concepts are illustrated using the R language. Students also receive introduction to IBM Watson Analytics and IBM SPSS Modeler. Students will be evaluated by their course projects.

Thursday September 6, 2018 - Lecture 1: What is Data Science?/ Introduction to R (in PA 133).

Thursday September 13, 2018 - Lecture 2: Working with Data (in PA 133).

Thursday September 20, 2018 - Lecture 3: Visualization and Exploration.

Thursday September 27, 2018 - Lecture 4: Data Mining and Machine Learning I.

Thursday October 4, 2018 - Lecture 5: Machine Learning II.

Thursday October 11, 2018 - IBM SPSS Modeler Tutorial by Dennis Buttera.

Thursday October 18, 2018 - Guest lecture: Tracey Lauriault (in PA 133)

Thursday October 25, 2018 - NO CLASS (Reading Week).

Thursday November 1, 2018 - Tableau Tutorial (in PA 133).

Thursday November 8, 2018 - IBM Cognos Analytics with AI Tutorial by Dennis Buttera (in PA 133).

Thursday November 15, 2018 - Guest Lecture: Maria Pospelova from Interset (in PA 133).

Evaluation

Paper presentation

Each group needs to choose a conference publication on the topic of Data Science to present in class (15 minute talk). Paper selection due September 20, 2018. A 8-12 page conference proceeding (e.g., IEEE International Conference on Data Science, SIGKDD/KDD Conference, etc.) will be approved by the instructor. Presentations will be scheduled throughout the course between 11:35-14:25.

Project proposal

The project forms an integral part of this course. The project is to be completed in group of two students.

You have two options: you can choose to mine and analyze one of the provided datasets or come up with an idea of your own that relates to the course material. In either case, the project topic will require my approval (via the project proposal).

Before you undertake your project you will need to submit a proposal for approval. The proposal should be short (max 2 page PDF in ACM format). The proposal should include a problem statement, the motivation for the project, and set of objectives you aim to accomplish. I will read these and provide comments. This will be due on September 27 by 11:59 PM via email.

Presentation outline

You would need to submit your project presentation outline describing the structure of your slides and preliminary content (in PDF format). This will be due on November 8 by 11:59 PM via email.

Poster presentation

Each group will have the opportunity to present their project's posters during the poster presentation day on November 22 (in HP 5345). The independent jury will evaluate posters and select winners.

Project presentation

Each group will have the opportunity to present their project in class on November 29 and December 6 . This presentation should take the form of a 20 minute (hard maximum) conference-style talk and describe the motivation for your work, what you did, and what you found. If a demo is the best way to describe what you did, feel free to include one in the middle of the talk. Please allocate 3-5 minute time for questions after the project has been presented.

The proposed structure of your presentation:

1. Introduction (describe the problem and motivation)
2. Research questions
3. Methodology: data collection, data cleanup, data mining, data analysis (statistics, machine learning), etc.
4. Results (achieved, preliminary, or anticipated)
5. Implications (why does this study matter? how can your findings be used?)
6. Conclusion (summary, main contributions)

Project report

The required length of the written report varies from project to project (8-10 pages, double column format); all reports must be formatted according to the ACM format and submitted as a PDF. This report will constitute 50% of the course grade. This will be due on December 13 by 11:59 PM via email.

Datasets

• Interset Projects (TBA)
• GitHub repository via GHTorrent
• MSR Mining Challenge datasets (various datasets for different years)
• Tera-PROMISE repository
• Open Data @ Government of Canada
• Machine learning data set repo
• IAPR
• Datamob
• KDnuggets:
• in R

Resources

The following books are suggested but not required:

• "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.

The following books are good references for data mining and machine learning algorithms:

• "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.

The following are good references for R (just to name a few):

• "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.

Contact

The best way to get in touch with instructor is via email: olga.baysal[at]carleton.ca or elio.velazquez[at]cmail.carleton.ca. However, for any public course related communication we will be using Slack DATA 5000 channel. For private messages, please email instructor directly or send a private message on Slack.

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.