General Information

Welcome to Data5000! 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).

Seminars are held every Thursday from 11:35 AM to 2:25 PM. Please check the Carleton Central for room location. We look forward to seeing you on Thursday January 11 at 11:35 AM.

Instructors Majid Komeili Elio Velazquez Mahmud Hasan
Email majid.komeili@carleton.ca Elio.Velazquez@carleton.ca mahmudhasan@cunet.carleton.ca
Office hours by appointment by appointment by appointment

Content Overview

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 hands-on tutorials (e.g., Tableau, IBM Cognos Analytics, Microsoft Azure).

Tentative Schedule

It is important to note that this schedule is evolving and will change based on how the class is progressing.

Course Information

Evaluation

Project Proposal

The project forms an integral part of this course. The project is to be completed in group of two-three 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 the instructor's approval.

Before you undertake your project you will need to submit a proposal for approval. The proposal should be short (max 2 page PDF). You may use the ACM format. The proposal should include a problem statement, the motivation for the project, a description of the data your will be working on, and a set of objectives you aim to accomplish. This will be due on January 25 by 11:59 PM via Email.

Paper Presentations

Each group needs to choose a conference or journal paper related to Data Science and present it in class (15-minute talk). Paper selection is due February 1. The paper needs to be approved by the instructor. Papers will be presented on February 15.

Poster Draft

You would need to submit your poster draft including the structure of your poster and content (in PDF format). Instuctors will review posters and offer feedback. This will be due on March 12 by 11:59 PM via email.

Poser Submission

You would need to submit your poster to the Data Day by March 19. Note that the Data Day committee will not consider late submissions. Further instructions will be communicated closer to the Data Day.

Poster Presentation

Each group will have the opportunity to present their project's poster during the Data Day poster fair. Data Day is held usually in late March. The exact date will be announced.

Project Presentation

Each group will have the opportunity to present their poster in class on April 4. 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.

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 or IEEE format and submitted as a PDF. This will be due on April 11 by 11:59 PM via email.

Datasets

Resources

The following books are suggested but not required: The following books are good references for data mining and machine learning algorithms: The following are good references for R (just to name a few):

Contact

The best way to get in touch with instructors is via email. However, for any public course related communication we will be using Discord. For private messages, please email your instructor directly or send a private message on Discord.

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.