Summer 2021 - CMPT 459 D100

Special Topics in Database Systems (3)

Data Mining

Class Number: 4568

Delivery Method: In Person

Overview

  • Course Times + Location:

    Tu 2:30 PM – 4:20 PM
    REMOTE LEARNING, Burnaby

    Fr 2:30 PM – 3:20 PM
    REMOTE LEARNING, Burnaby

  • Prerequisites:

    CMPT 354 with a minimum grade of C-.

Description

CALENDAR DESCRIPTION:

Current topics in database and information systems depending on faculty and student interest.

COURSE DETAILS:

What should you do when you are facing (a huge amount of) data from applications? How can you become a data scientist? This course introduces an important research and development frontier: knowledge discovery in databases (KDD), also known as data mining (DM). This course emphasizes data-driven thinking and focuses on big data, fundamental methods and killer applications. Students taking this course are expected to (1) have taken an entry-level college statistics course; (2) have taken an algorithm course; and (3) be able to program fluently in C/C++/C#, Java, Python or R. After taking the course, a student should understand the essential principles of data science, learn a handful of data mining techniques and have a good command of data mining tools.

COURSE-LEVEL EDUCATIONAL GOALS:

Topics

  • Introduction
  • Business intelligence, data warehousing, data lakes, and enterprise data infrastructure
  • Finding useful patterns
  • Predictive analytics
  • Clustering analysis
  • Anomaly and fault detection
  • Working with data as a data scientist

Grading

NOTES:

Grading scheme will be announced in the first week of the class. Evaluation will be based on several individual assignments and exams.

Students must attain an overall passing grade on the weighted average of exams in the course in order to obtain a clear pass (C- or better).

Materials

REQUIRED READING:

  • Data Mining: Concepts and Techniques (3rd Ed), Jiawei Han, Micheline Kamber, and Jian Pei, Morgan Kaufmann Publishers, 2011,

ISBN: 9780123814791

Registrar Notes:

ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS

SFU’s Academic Integrity web site http://www.sfu.ca/students/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating.  Check out the site for more information and videos that help explain the issues in plain English.

Each student is responsible for his or her conduct as it affects the University community.  Academic dishonesty, in whatever form, is ultimately destructive of the values of the University. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the University. http://www.sfu.ca/policies/gazette/student/s10-01.html

TEACHING AT SFU IN SUMMER 2021

Teaching at SFU in summer 2021 will be conducted primarily through remote methods, but we will continue to have in-person experiential activities for a selection of courses.  Such course components will be clearly identified at registration, as will course components that will be “live” (synchronous) vs. at your own pace (asynchronous). Enrollment acknowledges that remote study may entail different modes of learning, interaction with your instructor, and ways of getting feedback on your work than may be the case for in-person classes. To ensure you can access all course materials, we recommend you have access to a computer with a microphone and camera, and the internet. In some cases your instructor may use Zoom or other means requiring a camera and microphone to invigilate exams. If proctoring software will be used, this will be confirmed in the first week of class.

Students with hidden or visible disabilities who believe they may need class or exam accommodations, including in the current context of remote learning, are encouraged to register with the SFU Centre for Accessible Learning (caladmin@sfu.ca or 778-782-3112).