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.

Classes are held every Thursday from 11:35 AM to 2:25 PM. Please check the Carleton Central for room location. There are 3 classrooms for the 3 sections. The first class and the tutorials/guest lectures will be in the SC103. We look forward to seeing you on Thursday January 9 at 11:35 AM in SC103.

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

Teaching Assistant:
Adnan Khan, AdnanKhan5@cmail.carleton.ca
Office Hours: 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 will be evaluated by their course projects. There will be tutorials and guest speakers.

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: 10% (due January 23 26)
  • Paper selection : 0% (due January 30 )
  • Paper presentation: 10% ( February 13 27 )
  • Progress report: 10% (due March 4)
  • Class participation and discussions: 5%
  • Project presentation: 15% ( March 27 and April 3)
  • Project report: 50% (due April 10)

Project Proposal

The project forms an integral part of this course. The project is to be completed in group of two students. Each group would have one technical expert (a student from Computer Science, Systems and Computer Engineering, Information Technology, Physics, Chemistry), and one domain expert (e.g., from Communication, Geography, Biology, History, Psychology, Economics, Business, Health Sciences, Cognitive Science, Public Policy and Administration, International Affairs). Domain experts may contribute to finding the right problem, justifying why it is important to study it, extracting the value and implications of the work. Technical experts do the heavy lifting of building models. The main goal for students is to learn how to work on a multidisciplinary team, i.e., for domain experts, it is about learning technical terminology, while for technical experts, how to fruitfully work with domain experts.

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 23 26 by 11:59 PM.

Paper Presentations

Each group needs to choose a conference or journal paper related to Data Science and present it. Paper selection is due January 30. The paper needs to be approved by the instructor. Papers will be presented on February 13 27.

Progress Report

Each group should submit a progress report (3000 characters max) by March 4. A progress report typically includes the following sections:
  1. Introduction: A summary of the project and its goals.
  2. Work completed: A list of tasks that have been completed.
  3. Work in progress: A list of tasks that are currently in progress.
  4. Work to be started: A list of tasks that have not yet started.
  5. Conclusion: An appraisal of the project's progress and if applicable, issues or concerns about the project.

Project Presentation

Each group will have the opportunity to present their project in class on either March 27 or April 3. Slides must be submitted by March 26 regardless of the date of your presentation. This presentation should be in the form of a conference-style talk and describe the motivation for your work, what you have done and what you have found so far. 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, 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 10.

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.