MRK712 - Applied Data Mining and Modelling

Outline information
Semester
Schools offering this subject
Last revision date 2020-12-03 08:39:11.758
Last review date 2020-12-03 08:39:23.49

Subject Title
Applied Data Mining and Modelling

Subject Description
This course will introduce students to recent developments in advanced analytics techniques. Students will learn to use transformations, decision trees, neural networks, and support vector machines to build high performance models. They will also learn bias-variance trade-off and apply it during the model building process. Ensemble methods of bagging and boosting will also be covered to equip students with well rounded knowledge of the latest developments in Machine Learning.
 
This course focuses on actual problem solving skills. SAS Enterprise Miner and Open Source Software R will be used to demonstrate concepts, and students will use these tools in their assignments.  SAS Enterprise Miner is available in the computer labs and to students with a Windows laptop.  R is open source and students can install them on their own machines.

Learning Outcomes
Upon successful completion of this subject the student will be able to:

1.   Formulate situation-appropriate analysis framework for business problems
2.   Solve business problems using analytical techniques of transformation, decision tree, neural network, support vector machine, cross-validation, bagging, and boosting
3.   Build advanced models using SAS Enterprise Miner and R
4.   Interpret analysis results to verify correctness of bias-variance trade-off
5.   Assess performance of different algorithms and provide thoughtful commentary on their characteristics
6.   Prepare technical analysis journal and justify interpretation
7.   Summarize business insights in the form of a professional business presentation

Essential Employability Skills

    •  Communicate clearly, concisely and correctly in the written, spoken and visual form that fulfils the purpose and meets the needs of the audience.

    •  Respond to written, spoken, or visual messages in a manner that ensures effective communication.

    •  Execute mathematical operations accurately.

    •  Apply a systematic approach to solve problems.

    •  Use a variety of thinking skills to anticipate and solve problems.

    •  Locate, select, organize, and document information using appropriate technology and information systems.

    •  Analyze, evaluate, and apply relevant information from a variety of sources.

    •  Manage the use of time and other resources to complete projects.

    •  Take responsibility for one's own actions, decisions, and consequences.

Academic Integrity
Seneca upholds a learning community that values academic integrity, honesty, fairness, trust, respect, responsibility and courage. These values enhance Seneca's commitment to deliver high-quality education and teaching excellence, while supporting a positive learning environment. Ensure that you are aware of Seneca's Academic Integrity Policy which can be found at: http://www.senecapolytechnic.ca/about/policies/academic-integrity-policy.html Review section 2 of the policy for details regarding approaches to supporting integrity. Section 2.3 and Appendix B of the policy describe various sanctions that can be applied, if there is suspected academic misconduct (e.g., contract cheating, cheating, falsification, impersonation or plagiarism).

Please visit the Academic Integrity website http://open2.senecac.on.ca/sites/academic-integrity/for-students to understand and learn more about how to prepare and submit work so that it supports academic integrity, and to avoid academic misconduct.

Discrimination/Harassment
All students and employees have the right to study and work in an environment that is free from discrimination and/or harassment. Language or activities that defeat this objective violate the College Policy on Discrimination/Harassment and shall not be tolerated. Information and assistance are available from the Student Conduct Office at student.conduct@senecapolytechnic.ca.

Accommodation for Students with Disabilities
The College will provide reasonable accommodation to students with disabilities in order to promote academic success. If you require accommodation, contact the Counselling and Accessibility Services Office at ext. 22900 to initiate the process for documenting, assessing and implementing your individual accommodation needs.

Camera Use and Recordings - Synchronous (Live) Classes
Synchronous (live) classes may be delivered in person, in a Flexible Learning space, or online through a Seneca web conferencing platform such as MS Teams or Zoom. Flexible Learning spaces are equipped with cameras, microphones, monitors and speakers that capture and stream instructor and student interactions, providing an in-person experience for students choosing to study online.

Students joining a live class online may be required to have a working camera in order to participate, or for certain activities (e.g. group work, assessments), and high-speed broadband access (e.g. Cable, DSL) is highly recommended. In the event students encounter circumstances that impact their ability to join the platform with their camera on, they should reach out to the professor to discuss. Live classes may be recorded and made available to students to support access to course content and promote student learning and success.

By attending live classes, students are consenting to the collection and use of their personal information for the purposes of administering the class and associated coursework. To learn more about Seneca's privacy practices, visit Privacy Notice.