Syllabus
MIS2502.002 – Data Analytics
Spring 2014
About the Instructor:
Frank Hoban (frank.hoban@temple.edu)
Office hours: before and after class by appointment
Class Location and Time:
Alter Hall 232 5:30 – 8:00 , Wednesday
On the web: http://community.mis.temple.edu/mis2502sec002s14/ (under construction)
Prerequisites:
Grade of C or better in MIS2101.
Course Description:
The course provides a foundation for designing database systems and analyzing business data to enhance firm competitiveness. Concepts introduced in this course aim to develop an understanding of the different types of business data, various analytical approaches, and application of these approaches to solve business problems. Students will have hands-on experience with current, cutting-edge tools such as MySQL and SAS Enterprise Miner.
Course Objectives:
• Articulate the key components of an organizations’ information infrastructure.
• Create data models based on business rules.
• Create a transactional database from a model using SQL.
• Create an analytical data store by extracting relevant data from a transactional database.
• Perform extract, transform, load (ETL) functions such as data sourcing, pre-processing, and cleansing.
• Discover trends in analytical data stores using the data mining techniques of clustering, segmentation, association, and decision trees.
• Present data visually for clear communication to a managerial audience.
Required Textbook:
There is no required textbook for this course.
Evaluation and Grading
Item Percentage
Exams (3) 70%
Assignments (9) 25%
Participation 5%
Scale
94 – 100 A 73 – 76 C
90 – 93 A- 70 – 72 C-
87 – 89 B+ 67 – 69 D+
83 – 86 B 63 – 66 D
80 – 82 B- 60 – 62 D-
77 – 79 C+ Below 60 F
Exams
There will be three exams during the semester. The date of the first exam is February 26, 2014 and the date of the second exam is April 9, 2014. The final exam is scheduled for Tuesday, May 14, 2014 from 5:30 – 8:00. Missed exams cannot be made up, regardless of the reason for absence.
Late Assignment Policy
An assignment is considered late if it is turned in after the beginning of class. No late homework assignments will be accepted without penalty. All assignments will be assessed a 10% penalty (subtracted from that assignment’s score) each day they are late.
No credit will be given for assignments turned in more than one week past the due date. However, you must submit all assignments, even if no credit is given. If you skip an assignment, an additional 10 points will be subtracted from your final grade in the course. For example, if you do not turn in an exercise, you will receive no credit for that assignment plus a 10 point penalty, reducing your maximum grade in the course to an 87.
Equipment failure is not an acceptable reason for turning in an assignment late.
Assignments
There will be ten assignments. They are to be done individually and should represent your own work. If you need help, you may consult with your instructor or the tutors.
# Assignment Due
1 ER Modeling Feb 5
2 SQL #1 – Getting Data out of the Database Feb 19
3 SQL #2 – Putting Data into the Database Mar 12
4 Working with Pivot Tables in Excel Mar 26
5 ETL Apr 9
6 SAS #1 – Introduction to working with SAS Apr 10
7 SAS #2 – Decision Trees Apr 18
8 SAS #4 – Association Rules Apr 23
9 SAS #3 – Clustering Apr 30
Classroom Etiquette
The environment you and your fellow students create in class directly impacts the value gained from the course. To that end, the following are my expectation of your conduct in this class:
• Arrive on time and stay until the end of class.
• Turn off cell phones, pagers and alarms while in class.
• Limit the use of electronic devices (e.g., laptop, tablet computer) to class-related usage such as taking notes. Restrict the use of an Internet connection (e.g., checking email, Internet browsing, sending instant messages) to before class, during class breaks, or after class.
• During class time speak to the entire class (or breakout group) and let each person “take their turn.”
• Be fully present and remain present for the entirety of each class meeting.
Participation
Participation will be evaluated in two ways. First, a question will be posted to the Community Site each week about some aspect of the material we have just covered. Leave an answer to the question as a comment. You can also respond to other students’ comments, as long as you also add your own insight to the discussion. You are expected to contribute something to each week’s discussion.
Second, involvement during class is also important. Being present in class to ask and answer questions is essential to the learning process. While you’re not expected to say something in every class meeting, simply showing up for class does not qualify as participation.
Plagiarism and Academic Dishonesty
Plagiarism and academic dishonesty can take many forms. The most obvious is copying from another student’s exam, but the following are also forms of this:
• Copying material directly, word-for-word, from a source (including the Internet)
• Using material from a source without a proper citation
• Turning in an assignment from a previous semester as if it were your own
• Having someone else complete your homework or project and submitting it as if it were your own
• Using material from another student’s assignment in your own assignment
If you use text, figures, and data in reports that were created by someone other than yourself, you must identify the source and clearly differentiate your work from the material that you are referencing. There are many different acceptable formats that you can use to cite the work of others (see some of the resources below). You must clearly show the reader what is your work and what is a reference to somebody else’s work.
Plagiarism and cheating are serious offenses. Penalties for such actions are given at my discretion, and can range from a failing grade for the individual assignment, to a failing grade for the entire course, to expulsion from the program.
Student and Faculty Academic Rights and Responsibilities
The University has adopted a policy on Student and Faculty Academic Rights and Responsibilities (Policy # 03.70.02) which can be accessed through the following link:
http://policies.temple.edu/getdoc.asp?policy_no=03.70.02
Schedule
(Keep in mind that all dates are tentative – check the Community site regularly for changes in the schedule!)
You are expected to review the assigned material for each class. Additional, supplementary material may be assigned throughout the course of the semester.
Day Topics Course Materials Assignments
Week 1
Jan 22 Course Introduction and Syllabus
The Things You Can Do with Data.
The Information Architecture of an Organization. PowerPoint: The Things You Can Do With Data
PowerPoint: Information Architecture
Data Modeling
Gathering requirements
Introducing The Entity-Relationship Diagram
In-class exercise: What is an entity? PowerPoint: Relational Data Modeling
Week 2
Jan 29 More on ERDs:
Relationships, cardinality, normalization
In-class exercise: Converting ERDs to schemas PowerPoint: Relational Data Modeling
In-class exercise:
Creating an Entity Relationship Diagram PowerPoint: Relational Data Modeling
Week 3
Feb 3 Drop Deadline
Feb 5 Getting data out of the database:
SQL SELECT, DISTINCT MIN, MAX, COUNT, and WHERE
Make sure you’ve done the MySQL tutorial and reviewed the MySQL PowerPoint deck.
In-class exercise: Pen-and-paper SQL exercise PowerPoint: SQL 1
Getting data out of the database:
Joining tables, SQL subselects, LIMIT Assignment 1 Due: ER Modeling
Week 4
Feb 12 In-class exercise: Working with SQL, part 1
Creating and updating the database
SQL CREATE, DROP, and ALTER
SQL INSERT, UPDATE, and DELETE PowerPoint: SQL 2
Week 5
Feb 19 In-class exercise: Working with SQL, part 2 Assignment 2 Due: SQL #1
Review for Exam 1
Week 6
Feb 26 Exam 1
Principles of Data Visualization
PowerPoint: Data Visualization
Week 7
Mar 5 SPRING BREAK
Week 8
Mar 12 In-class exercise: Data Visualization Assignment 3 Due: SQL #2
Turning transaction data into analytical data: Overview of the Dimensional Model
The structure of the Dimensional Model: The Star Schema PowerPoint: Dimensional Data Modeling
Week 9
Mar 19 Working with Dimensional Data:
Pivot Tables in Excel
In-class exercise: Pivot Tables in Excel
Getting data into the warehouse and cube:
The Extract, Transform, Load process
Data quality: Best practices, data cleansing, and integration PowerPoint: ETL
Week 10
Mar 25 Withdraw Deadline
Mar 26 In-class exercise: ETL
Assignment 4 Due: Pivot Tables in Excel
Introduction to Advanced Analytics and SAS Enterprise Miner
In-class exercise: Descriptive Statistics Review PowerPoint: Advanced Analytics – Introduction
Week 11
Apr 2 In-class exercise: Introduction to SAS Enterprise Miner/Preparing Data for Analysis Assignment 5 Due: Data Visualization
Review for Exam 2
Week 12
Apr 9 Exam 2, Decision Trees
Week 13
Apr 16 Decision Trees, Association Rules
Week 14
Apr 23 Clustering and Segmentation, Exam 2 Review
Week 15
Apr 30 Clustering and Segmentation, Final Exam Review
Week 16
Final