MIS 150 - Intro to Data Science - Spring 2016

Department of Mathematics and Information Technology

Dr. Angela Berardinelli

Important Dates for Spring 2016

Next quiz: First day survey (link on Moodle) due Wednesday, February 10 at 11:59PM

  • Wednesday, February 3: First day of class
  • Wednesday, February 10: Add/drop deadline
  • Friday, February 19: Homework Project 1 Due
  • Friday, March 4: Homework Project 2 Due
  • Friday, March 11: Homework Project 3 Due
  • Wednesday, March 16: Exam 1
  • Friday, March 18: Homework Project 4 Due
  • Monday, March 21 through Monday, March 28: Spring/Easter Break (no class)
  • Friday, April 8: Homework Project 5 Due
  • Friday, April 15: Homework Project 6 Due
  • Wednesday, April 20: Exam 2
  • Friday, April 22: Spring Break (no class)
  • Friday, April 29: Homework Project 7 Due
  • Friday, May 13: Homework Project 8 Due
  • Friday, May 13: Last day to drop

If you are in Section 1 (9:15AM), your final exam will be held Friday, May 20 (8AM-10AM).

If you are in Section 2 (10:30AM), your final exam will be held Wednesday, May 18 (10:30AM-12:30PM).

Course Description

An introduction to Microsoft Excel and Access in a data science context. The focus will be on machine learning techniques, particularly cluster analysis, Naïve Bayes, and ensemble methods. 3 credits.

Prerequisites: None.

In particular, this semester we will focus on the following topics:

  • Introduction to Excel
  • Introduction to databases, SQL, and Access
  • Implementing the following data science techniques using Excel:
    • Linear programming (don't let "programming" fool you, this is not "computer programming" but rather a mathematically-based optimization technique)
    • Naive Bayes classification method
    • Clustering methods
    • Recommendation systems and collaborative filtering


Required Textbook: Data Smart: Using Data Science to Transform Information into Insight, 1st Edition, by John W Foreman.

Optional Textbook: Programming Collective Intelligence: Building Smart Web 2.0 Applications, 1st Edition, by Toby Segaran. This book contains additional examples and explanations of the topics we will be discussing in class. However, its focus is through Python programming, not Microsoft Excel. So, while it may contain helpful information and additional insight, it is not required, and might be confusing/difficult to read if you've never been exposed to computer programming in the past.

Software: We will be using Microsoft Excel 2013 and Microsoft Access 2013 in class. This software is already available on the computers in the lab. (Note: Everything we do in Microsoft Excel can also be done in Excel for Mac and LibreOffice, if you do not use Microsoft products on your personal computer at home.)

Office Hours: The perfect opportunity to ask general questions about course material, specific questions about homework problems or in-class examples, questions about your grade, questions about majoring in math or information technology, etc. My office location and office hour schedule are on my home page. You can also e-mail me to set up an appointment outside of office hours if that suits your schedule better.

Grading Information

Your final grade in the course is the weighted average of three components: quizzes, homework projects, and exams.

If you have a weighted average of at least... You will earn a(n):
94% A
90% B+
84% B
78% C+
70% C
65% D+
60% D

Your quiz average counts for 10% of your final course grade. Quiz assignments may be out-of-class tasks (for example, the first day survey) or in-class quizzes. In-class graded quizzes will be announced in class at least one class period in advance.

Homework project instructions and data files are available on Moodle. Your average score for these 8 assignments counts for 40% of your final course grade.

Each in-class exam (of which there are two) counts for 15% of your final course grade. The final exam counts for 20% of your final course grade.