SB/MSBA5220 3.00F Course Description

Schulich Course Information

SB/MSBA5220 3.00F: DATA MANAGEMENT AND PROGRAMMING II
Winter 2014
M 14:30 - 17:30
Room SSB W255

Term: W2
Exam: A3

COURSE DIRECTOR:	Stephen Keelan	ADMINISTRATIVE ASSISTANT:	Paula Gowdie Rose
OFFICE:	S337D SSB	OFFICE:
OFFICE TELEPHONE:	416.736.2100 ext. 66471	TELEPHONE:
OFFICE HOURS:	By appointment	EMAIL:
EMAIL:	skeelan@schulich.yorku.ca

Schulich School of Business
York University

Course Outline

MSBA 5220 3.00 F: Data Management and Programming II
Mondays, 2:30-5:30pm
W255 SSB
Winter 2014

Instructor

Stephen Keelan
S337 Seymour Schulich Building
skeelan@schulich.yorku.ca
Office hours: TBA
Administrative Support: Paula Gowdie Rose, S337 SSB, 416-736-5074

Brief Description

The Data Management and Programming II course examines advanced techniques for manipulating data. The course emphasises the SAS environment. Major areas for discussion include controlling input and output, summarizing data, data transformations, and debugging.

Prerequisite(s)

MSBA 5120 1.50

Course objectives

This course starts where Data Management and Programming I left off. It teaches higher-level techniques for manipulating data and introduces debugging. Like its predecessor, the course emphasizes practice over theory and allows students to experiment with each of the key data manipulation procedures. Upon completion of the course, students will posses the ability to manipulate large data sets. The course also will follow the new developments in the data management world, and will integrate new techniques and programming solutions as they become available. The course is also a requirement for the SAS Business Analyst certification.

Organization of the Course

Pedagogy

The course takes place in the computer lab. Each session of the course focuses on a chapter of the SAS Data Management and Mining manual. Students are expected to complete required readings prior to the lecture and come prepared to follow along at their workstations. Part of each class session is devoted to completing in-class exercises.

Textbook

Students use the SAS Data Management and Mining manual as their textbook for the course.

Evaluation of Student Performance

The course grading scheme for Master’s level courses at Schulich uses a 9-value grade-point system. The possible course letter grades for a course (and the corresponding grade points) awarded for each grade are:

A+ 9 grade points

A 8

A- 7

B+ 6

B 5

B- 4

C+ 3

C 2

C- 1

F 0

Students are reminded that they must maintain a cumulative GPA of at least 4.2 to remain in good standing and continue in the program, and a minimum of 4.4 to qualify for their degree. Schulich grading guidelines mandate a section grade point average [‘GPA’] of between 4.7 and 6.1 for core courses and a section GPA of between 5.2 and 6.2 for electives.

Where instructors use numerical or percentage grades, Schulich grading policy does not require a preset translation of percentages into specific letter grades. In this class, final letter grades will be determined by the following process:

The final grade for the course will be based on the following items weighted as indicated:

	Max. % of Total
Assignments – 3 @ 10%	30%
Midterm Exam	30%
Final Exam	40%
Maximum Total Mark	100%

Assignments (30%) Students complete three assignments over the duration of the course. The assignments generally require students to manipulate data into a form best suited for making a particular business decision, using the toolkit learned in the course. Students must submit assignments at the beginning of class, in the form of computer printouts. Each assignment is worth 10%.

Midterm Test (30%) The midterm test will cover material from the first half of the course and all of the material from the predecessor course. It will take place in lab and will consist of a series of assignments to be completed in the three-hour period.

Final Exam (40%) The material for the final exam incorporates all the techniques discussed in the course. It includes problem-solving questions and short-answer questions. The three-hour exam will take place at a time and place to be announced.

Late Delivery: The students will lose 5% of their assignment grade for every day the assignment is delayed.

Academic Honesty

Academic honesty is fundamental to the integrity of university education and degree programs. The Schulich School will investigate and will act to enforce academic honesty policies where apparent violations occur. Students should familiarize themselves with York University’s policy on academic honesty. It is printed in full in your student handbook and can also viewed on-line on the Schulich website, clicking through as indicated:

Schulich website ‘Programs’ -> ‘Master’s Degree’ -> ‘MBA' -> ‘Academic Honesty’

While academic dishonesty can take many forms, there are several forms of which students should be highly aware because they are the ones that are most likely to occur in the context of a specific course.

[1]

Plagiarism.

Plagiarism is the presentation of information, ideas, or analysis generated by other people as being your own. It includes direct quotations as well a substantive paraphrases where the course of that information or idea is not clearly identified to the reader. Students should be careful to present their written work in a way that makes it completely clear in each and every cases where a quotation, a paraphrase, or an analysis is based on the work of other people. (This includes information from all sources, including websites.)

[2]

Cheating.

Cheating is an attempt to gain an unfair advantage in an evaluation. Examples of such violations include (but are not limited to) consulting prohibited materials during an examination or copying from another student.

[3]

Failure to follow limitations on collaborative work

with other students in preparing academic assignments. Each class differs in the mix of assignments and group-versus-individual preparation that is allowed. The instructor will make clear the extent of collaboration among students that is acceptable among students on various pieces of assigned work. Students should abide by those limitations and, if they are unsure about whether a certain level or form of collaboration would be acceptable, to clarify that question with the instructor in advance.

[4]

Aiding and abetting

. A student is guilty of violating academic honesty expectations if he/she acts in a way that enables another student to engage in academic dishonesty. If a student knows (or should reasonably expect) that an action would enable another student to cheat or plagiarize, that student’s action constitutes an academic honesty violation. Illustrative examples include making your exam paper easily visible to others in the same exam or providing your own working or finished documents for an ‘individual assignment’ to another student (even if that other student said that he/she just wanted to ‘get an idea of how to approach the assignment’ or ‘to check whether they had done theirs correctly’).

[5]

Use of academic work in more than one course.

Generally, academic work done for every course is ‘new’ work, done for that course only. If a student wishes to use some or all of the academic work done for an assigned task in one course in another course, the student must get

explicit, prior

permission from both instructors so that they agree that the scope and nature of the overlapping use of that work is such that it can fairly be counted toward both courses.

Schedule of Topics and Readings

The following includes a list of lecture topics, material to be read, reviewed and/or prepared for the various class sessions, as well as when assignments are due and quizzes are scheduled. If any changes in this schedule become necessary, notifications will be posted in the course website, and where such changes need to be announced between class sessions, an email will be sent to students’ Lotus Notes email accounts, notifying them of the change.

Date	Week	Topic/Assigned Work Due	Readings & Resources
January 6	1	Controlling Input and Output · Outputting multiple observations · Writing to multiple SAS data sets · Selecting variables and observations	Chapters I from the course kit
January 13	2	Summarizing Data · Creating an accumulating total variables · Accumulating totals for a group of data Assignment I is handed out	Chapter II from the course reader
January 20	3	Reading Raw Data Files · Reading raw data files with formatted input · Controlling when a record loads · Additional techniques for list input	Chapter III from the course reader
January 27	4	Data Transformations · Manipulating character values · Manipulating numeric values · Converting variable types Assignment 1 due	Chapter IV from the course reader
February 3	5	Debugging Techniques · Using the PUTLOG statement · Using the DEBUG option	Chapter V from the course reader
February 10	6	Processing Data Iteratively · DO loop processing · SAS array processing · Using SAS arrays Assignment 2 due	Chapter VI from the course reader
February 17	7	No Class – Family Day
February 24	8	Midterm Exam
March 3	9	Restructuring a Data Set · Rotating with the DATA step · Using the TRANSPOSE procedure	Chapter VII from the course reader
March 10	10	Other SAS Languages · General introduction to other languages · Using the SQL procedure · The SAS macro language Assignment 3 handed out	Chapter VIII from the course reader
March 17	11	Other SAS Languages
March 24	12	Other SAS Languages
March 31	13	Creating Graphics Using SAS/GRAPH · General introduction to SAS/GRAPH · Creating bar and pie charts · Creating plots · Enhancing your output Assignment 3 due	Chapter IX from the course reader

Please check last year's version of the course if this course's outline is not (yet) available.
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Last revised: 12/16/2013