Spring 2019 - STAT 240 D100

Introduction to Data Science (3)

Class Number: 3431

Delivery Method: In Person

Overview

  • Course Times + Location:

    Mo 12:30 PM – 2:20 PM
    EDB 7618, Burnaby

  • Exam Times + Location:

    Apr 15, 2019
    12:00 PM – 3:00 PM
    Location: TBA

  • Prerequisites:

    Any STAT course (except STAT 100) or BUEC 232, and one of CMPT 102, CMPT 120, CMPT 125, CMPT 128, CMPT 129, CMPT 130, or permission of the instructor.

Description

CALENDAR DESCRIPTION:

Introduction to modern tools and methods for data acquisition, management, and visualization capable of scaling to Big Data. Quantitative.

COURSE DETAILS:

This course will use the statistical software R to acquire and clean data for answering data science questions. This class will focus on using SQL to query big data sets, handling text data from a webpage api, and scraping data from webpages.

Grading

  • Weekly Lab Assignments 20%
  • Projects 40%
  • Final Exam 40%

NOTES:

Above grading is subject to change.

Materials

REQUIRED READING:

Automated Data Collection with R: A Practical Guide to Web Scraping and Text Mining. Authors: Simon Mumzert, Christian Rubba, Peter Meissner, Dominic Nyhuis. Publisher: Wiley
ISBN: 978-1-118-83481-7

Department Undergraduate Notes:

Students with Disabilites:
Students requiring accommodations as a result of disability must contact the Centre for Accessible Learning 778-782-3112 or csdo@sfu.ca


Tutor Requests:
Students looking for a Tutor should visit http://www.stat.sfu.ca/teaching/need-a-tutor-.html. We accept no responsibility for the consequences of any actions taken related to tutors.

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