Outline

The planning for the current semester.

with Professor Johan Latulippe
office hours: Wednesday 11:30 to 14:30

Course Description


An examination of various methods for getting, exploring, and modeling large data sets. The student will gain insight into the data science toolboxes and their application in finance, economics, marketing, human resources, management, sales and more. Topics include a wide range of analytical procedures and models prevalent in data science.

Learning Outcomes


  • Through problem solving, find the right approach to extract meaning from raw data
  • Find, collect, and clean data
  • Organize, structure, and rearrange data for analysis
  • Identify relevant assumptions needed for modeling
  • Determine the correct tools and models necessary for data analysis
  • Summarize and visualize the data to bring to light the implications of the data
  • Present your findings and recommend implementable actions

Course Organization


The course will consist of short lectures broken into short segments interwined with in-class exercises. There will be two major data analysis projects done in groups that will be peer-graded with instructor quality control. Course grades will be determined by the data analyses, peer reviews, and bonus points for participating in the course. The students will be closely supervised by the instructor throughout the semester.

References


  • Venables, William N and Smith, David M, An introduction to R; Network Theory Limited, 2009.

  • Get introduced to R (for free) with Try R.

Required Software


This course requires the software R.

  • R from cran is a free command-line based language.

  • RStudio is a free integrated development environment (IDE) for R.

Both of these programs are free and cross-platform (Windows, Mac, Linux). Install R first, then RStudio. Make sure to use the latest version of each.

Method of Evaluation


Participation  

At the discretion of the instructor, extra marks will be awarded to students who participate in class discussion. 

English Standards  

Assignments must be free of spelling, punctuation and grammatical errors. Assignments containing such errors will be penalized (i.e. mark deductions).

Accommodation  

Students with documented disabilities requiring academic and/or exam accommodation should contact Disability Services in Building 200.

Grading Scale


Evaluation Percentage
Problem sets 50
Draft 10
Project 40
  • Problem sets Random and unpredictable, due 7 days after posting.
  • Draft 1 November 2017
  • Project 4 December 2017

Course Schedule

We will cover as much of what follows as possible, but topics may be added or deleted depending upon time since we have to accommodate assessments. The tentative lecture and reading schedule for the seven weeks is as follows:

Please Note


  • Students should acquaint themselves with the University’s “Student Conduct Policy”.

  • Students should feel free to ask questions during lectures.

  • Students are responsible for all materials taught in class and readings assigned.

Academic Misconduct


Academic misconduct includes, but is not limited to, giving and receiving information during any test or exam, using unauthorized sources of information during any test; plagiarizing; fabrication, cheating, and, misrepresenting the work of another person as your own, facilitation of academic misconduct, and under certain conditions, non-attendance.

Plagiarism will not be tolerated. You must reference your work and acknowledge sources with in-text citations and a complete list of references. This includes direct and indirect quotes, diagrams, charts, figures, pictures and written material.

For group projects, the responsibility for academic integrity, which can result in academic misconduct and its resulting penalties, rests with each person in the group and sanctions would be borne by each member.

No electronic dictionaries, cell phones or other electronic devices will be allowed in exams/tests/quizzes. Only the following approved calculators may be used in exams/tests/quizzes. Texas Instrument BAII Plus, BAII, BA35 Sharp EL-733A Hewlett Packard 10B No other materials will be allowed on the desktop apart from a pen/pencil unless specifically approved by the faculty member.

Grading Scheme


Letter
Grade
Percentage GPA
A+ 90-100 4.33
A 85-89 4.00
A- 80-84 3.67
B+ 76-79 3.33
B 72-75 3.00
B- 68-71 2.67
C+ 64-67 2.33
C 60-63 2.00
C- 55-59 1.67
F Below 50 0