Fall 2020 - CMPT 353 D100

Computational Data Science (3)

Class Number: 6608

Delivery Method: In Person

Overview

  • Course Times + Location:

    Mo, Fr 4:30 PM – 5:20 PM
    REMOTE LEARNING, Burnaby

    We 4:30 PM – 5:20 PM
    REMOTE LEARNING, Burnaby

  • Prerequisites:

    CMPT 225 and (STAT 101, STAT 270, ENSC 280, or MSE 210).

Description

CALENDAR DESCRIPTION:

Basic concepts and programming tools for handling and processing data. Includes data acquisition, cleaning data sources, application of machine learning techniques and data analysis techniques, large-scale computation on a computing cluster.

COURSE DETAILS:

This course will be an introduction to the tools and techniques in data science. We will explore common challenges and solutions used in analysis of data. Online offering notes: you will need a computer with a webcam and reliable Internet access. The computer should be powerful enough to run a virtual machine: at least 8 GB memory, 20 GB disk, and a reasonably decent processor. There will be 3-4 quizzes during the semester which must be completed during the lecture time. Otherwise, lectures will be posted as a "watch party" where we can watch together (and ask questions in a forum), but they can also be viewed later.

Topics

  • Basics of data science: concepts, goals, motivation, expectations.
  • Introduction to selected data processing tools: Python with numpy and pandas.
  • Working with data. Cleaning data; extract, transform, load tasks; applying concepts from statistics.
  • Machine learning basics with existing implementations (such as scikit-learn).
  • Data analysis strategies: selecting techniques from statistics and machine learning.
  • Big data tools.
  • Data visualization and summarizing results.

Grading

  • Will include weekly exercises, quizzes (in lecture time), and a project. Details will be discussed in the first week of class. 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).

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 FALL 2020

Teaching at SFU in fall 2020 will be conducted primarily through remote methods. There will be in-person course components in a few exceptional cases where this is fundamental to the educational goals of the course. 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).