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Schulich School of Business
York University
Course Outline
MSBA 5120 1.50 A: Data Management and Programming I
Mondays, 2:30-5:30pm
S123 SSB
Fall 2013
Instructor
Stephen Keelan
S337 Seymour Schulich Building
[email protected]
Office hours: TBA
Administrative Support: Paula Gowdie Rose, S337 SSB, 416-736-5074
Brief Description
The Data Management and Programming I course introduces students to the key techniques
required for managing data. The course emphasizes the SAS environment but also incorporates
other tools, such as Excel. Major areas for discussion include reading and validating data,
manipulating and combining data sets, and displaying data in reports.
Prerequisite[s] / Co-requisite[s]
Corequisite: MSBA 5110 3.00
Course objectives
The effective practice of data analytics requires more than an understanding of statistical
techniques; it also requires the ability to manage and manipulate data. This course introduces
students to core data management techniques using SAS, a commercial analytics platform. The
course emphasizes practice over theory, and allows students to experiment with each of the key
data management procedures. Upon the completion of the course, students will have the ability
to manage large data sets. 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
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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.
Students complete a group project, due in the final session. The project provides students the
opportunity to manage and manipulate a data set, provided by the instructor, using the
techniques learned in class. Students prepare a short presentation for the final class session in
which they discuss their key findings.
Readings
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
A7
B+
6
B
5
B4
C+
3
C
2
C1
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:
Assignments: 3 x 10%
30%
Group Project
30%
Final Exam
40%
Total
100.00%
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
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submit assignments at the beginning of class, in the form of computer printouts. Each
assignment is worth 10%.
Group Project (30%) Students complete a group project as part of the course. The instructor
provides a data set and a problem statement, and each student group must decide on its own
how best to manipulate the data to solve the business problem. Students present their solution
and report outputs in a presentation in the final class. Since the problem has no one best
solution, effective communication and advocacy for the group’s own approach plays as
important a role in the group’s grade as the choice of manipulation techniques itself.
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 threehour exam will take place at a time and place to be announced. Please refer to the exam
schedule.
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-versusindividual preparation that is allowed. The instructor will make clear the extent of
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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 list of lecture topics and readings indicates the material to be read, reviewed
and/or prepared for the various class sessions. It will be assumed that the student have read
and thought about the issues explored in the readings and resources before coming to class.
Adequate preparation is absolutely necessary to benefit fully from class discussions and be able
to contribute to class discussions.
Week
1
Date
Topic
Assigned Readings &
Resources
September 9
Accessing SAS
Data Sets
Chapter I from the
course kit
Assigned Work
Due
Accessing SAS Data
Sets
 Examining descriptor
and data portions
 Accessing relational
databases
 Browsing SAS data
libraries in Windows
 Browsing SAS data
libraries in UNIX
2
September 16
Reading SAS
Chapter II from the
Assignment #1
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Week
Date
Topic
Assigned Readings &
Resources
Assigned Work
Due
Data Sets
course kit
handed out
Reading SAS Data
Sets
 Reading data in SAS
 Using SAS data as an
input
 Creating subsets of
observations and
variables
 Adding permanent
attributes
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September 23
Reading Excel
Worksheets
and
Manipulating
Data
Chapters III and IV
from the course reader
Assignment #1
due
Reading Excel
Worksheets
 Using Excel data as an
input
 Additional Excel
techniques
Assignment #2
handed out
Manipulating Data
 Creating variables
 Creating variables
conditionally
 Sub-setting
observations
4
September 30
Combining SAS
Data Sets
Chapter V from the
course reader
Assignment #2
due
Combining SAS Data
Sets
 General introduction
to combining SAS data
sets
 Appending a data set
 Concatenating data
sets
 Merging data sets
one-to-one
 Merging data sets
many-to-one
 Merging data sets with
5
Week
Date
Topic
Assigned Readings &
Resources
Assigned Work
Due
non-matches
5
October 7
Producing
Summary
Reports
Chapter VI from the
course reader
Assignment #3
handed out
Producing Summary
Reports
 Using the FREQ
procedure
 Using the MEANS
procedure
 Using the TABULATE
procedure
6
October 14
Thanksgiving – no class
7
October 21
Group
Presentations
Assignment #3
due
Final exam: Please refer to the exam schedule.
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