Fall 2017 - CMPT 318 D100

Special Topics in Computing Science (3)

Computational Data Science

Class Number: 7074

Delivery Method: In Person

Overview

  • Course Times + Location:

    Sep 5 – Dec 4, 2017: Mon, 1:30–2:20 p.m.
    Burnaby

    Sep 5 – Dec 4, 2017: Wed, Fri, 1:30–2:20 p.m.
    Burnaby

  • Exam Times + Location:

    Dec 14, 2017
    Thu, 12:00–3:00 p.m.
    Burnaby

  • Prerequisites:

    CMPT 225.

Description

CALENDAR DESCRIPTION:

Special topics in computing science at the 300 level. Topics that are of current interest or are not covered in regular curriculum will be offered from time to time depending on availability of faculty and student interest.

COURSE DETAILS:

Offering title: "Computational Data Science".

Topics

  • Basics of data science: concepts, goals, motivation, expectations.
  • Introduction to (selected) data processing tools: Python with numpy and pandas; SQL basics.
  • 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.
  • Data visualization and summarizing results.
  • Big data tools.

Grading

NOTES:

Details to be announced in first week of class. Will include weekly exercises, a project, quizzes, 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).

Registrar Notes:

SFU’s Academic Integrity web site http://students.sfu.ca/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

ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS