Download BA 575 Data Exploration and Visualization

BA 575 Data Exploration and Visualization
Course Syllabus
BA 575 Course Content
In this course we concentrate on the initial, exploratory phases of Business Analytic data analysis.
We explore different types of data and the types of analysis they allow; aggregating and
disaggregating data and issues of validity with both selecting and collecting data. We also start
exploring one or more datasets relating to our future Integrated Business Analytics Project (BA
577).
 We explore and practice aggregating, disaggregating, organizing, screening and clean-up of
data.
 We discuss the types of information residing in data sets and what sorts of questions these
data sets can and cannot answer.
 We produce visualizations and exploratory statistics, identifying possible trends and
patterns.
 For our hands-on work we will use R; an open source implementation of the S programming
language and library for statistical computing and data visualization.
 We analyze and assess other people’s methods and findings (cases) in their efforts to
further uncover and analyze those trends and patterns.
 We review and reflect on (seemingly) contrarian approaches and points of view; e.g.,
automated (big) data mining vs. focused, non-statistical ethnographic/ethnological
approaches, data-driven vs. theory-driven perspectives or the blessing vs. curse of
dimensionality.
 For planning future analysis projects, we will discuss research design, operationalization
and data quality assurance and control (QA/QC) issues so that the data will indeed support
future analysis.

Finally, we will conduct an initial exploration of one or more data sets associated with our
future Integrated Business Analytics Project (BA 577), report on our findings and share our
experiences with the class.
Prerequisites: BA 573
Course Credit: 3 credits, course meets once per week for three hours of lecture.
BA 575 Learning Outcomes
Upon completion of this course you should be able to:
 Associate specific Business Analytics questions with the types of data and analysis methods
best suitable to answer those questions.
 Associate different types of data with families of analysis techniques best used to explore
them.
 Assess data set as to their suitability in answering specific Business Analytic questions.
 Provide advice concerning an initial set of exploratory statistics and visualizations which
may help explore the data for patterns and trends.
 Use a toolkit such as R to quickly compute/generate those statistics and visualizations.
 Recognize the benefits and limitations of; i.e., critically assess, de facto Business Analytic
activities, designs and visualizations.
 Specify a plan for data collection, data cleanup, data aggregation and/or disaggregation to
answer Business Analytic questions.
Course Delivery Methods/Pedagogy



Lectures.
R lab sessions including self-guided exercises and three (3) assignments (homework).
NOTE: the labs introduce all the concepts you need to successfully complete your R
assignments. They have been carefully designed to fit those assignments. Hence, you do
yourself a BIG favor studying them.
Student teams conduct an initial exploration of one or more datasets which they hope to use
for their Integrated Business Analytics Project (BA 577) and present the results to the class.
Exams: We have a Midterm exam but no final exam
Text:


Shmueli, G., Patel, N.R., Bruce, P.C. (2010) Data Mining for Business Intelligence: Concepts,
Techniques, and Applications in Microsoft Office Excel with XLMiner. Wiley. (Note: this text
is shared with BA 573 Data Analytics for Competitive Advantage)
TBA: one or more texts to accompany the R labs and exercises; e.g.,
o Coghlan, A. (2013) Little Book of R For Multivariate Analysis (freely available on
line -- https://media.readthedocs.org/pdf/little-book-of-r-for-multivariateanalysis/latest/little-book-of-r-for-multivariate-analysis.pdf) or
o
Chang, W. (2013) R Graphics Cookbook. O’Reilly Media Inc.
or
o
Mittal, H.V. (2011) R Graphs Cookbook. Packt Publishing, Birmingham, UK.
or

o Teetor, P. (2001) R Cookbook. O’Reilly Media Inc.
TBA: 16 (short) articles or papers (academic, practitioners, business news media, etc.; two
per week) which are used as discussion cases; two cases per week for weeks 2-10.
Class Schedule
Theory/Lectures
R Labs
Week 1
Introduction; The role of data
exploration and data visualization
Intro to R
R language
R data types and data
imports
R packages
Week 2
Validity (construct, internal, external,
statistical, conclusion)
Operationalization & Research
Designs
Shmueli, Patel & Bruce, Ch. 3: Data
visualization
Cases 1-2
Univariate: frequencies,
histograms, line/bar
graphs, starplots, etc.
Bivariate: crosstabs,
scatterplots.
Multivariate:
scatterplot3d, multiline
graphs, etc.
Week 3
Operationalization & Research
Designs Cont.’d
Shmueli, Patel & Bruce, Ch. 12:
Discriminant analysis
Cases 3-4
Standardizing variables
Summary statistics
Correlation &
regressions
Discriminant analysis
Week 4
Dimensionality: a curse or a blessing?
Shmueli, Patel & Bruce, Ch. 4:
Dimension reduction
Shmueli, Patel & Bruce, Ch. 14:
Cluster Analysis
Cases 5-6
Clustering variables
(PCA, FA, MDS)
Clustering objects
(cluster analysis)
Week 5
Special topic: Web Systems Usage
Analytics & Timeseries
Shmueli, Patel & Bruce, Ch. 15
Cases 7-8
Exploring a Webserver
log with R
Timeseries Analysis
Week 6
Midterm exam
Cases 9-10
Week 7
Guest lecture: Information
Visualization (InfoVis) trends and
patterns
Cases 11-12
Students work on
exploring their project
data
Week 8
Cases 13-14
Students work on
Project
Students discuss their
plans for analysis of
their own data with the
class
exploring their project
data
Week 9
Trees and (social) network analysis
Reading TBD
Cases 15-16
Tree & Network
analysis and
visualization
Week 10
Students update class
on project progress
Students present on
findings
Grading
Homework assignments
30%
Midterm exam
35%
Project work (presentation & report)
30%
Peer evaluation grade
Note 1: To receive any peer evaluation credit, you must score a least
60/100.
5%
Note 2: To receive peer evaluation credit, you must yourself turn in the
evaluation of your peers.
The following number-to-letter grade scale will be used for calculating the final letter grade:
Letter Grade
Number Grade (0-4)
Number grade (0-100)
A
4.0
93.00 ≤ grade ≤ 100.00
A-
3.7
90.00 ≤ grade < 93.00
B+
3.3
87.00 ≤ grade < 90.00
B
3.0
83.00 ≤ grade < 87.00
B-
2.7
80.00 ≤ grade < 83.00
C+
2.3
77.00 ≤ grade 80.00
C
2.0
73.00 ≤ grade < 77.00
C-
1.7
70.00 ≤ grade < 73.00
D+
1.3
67.00 ≤ grade < 70.00
D
1.0
63.00 ≤ grade < 67.00
D-
.7
60.00 ≤ grade < 63.00
F
0
grade < 60.00
!!! Deadlines, exam dates, submission dates and presentation dates stated in this syllabus are firm and
will not be altered to accommodate the schedules of individual students !!!
OSU 'No Show Drop' rule
Note that for this course the OSU 'No Show Drop' rule will be strictly enforced. This rule: Academic
regulations AR 9§b reads as follows:
"If it is anticipated that the demand for enrollment in a given course will exceed the maximum number
that can be accommodated, the department offering the course may designate it in the Schedule of
Classes with the code "NSHD" (no-show-drop). A student who is registered for such a course who attends
no meetings of the course during the first five school days of the term will be dropped from the course by
the instructor, unless the student has obtained prior permission for absence. If such action is taken, the
instructor will send written notice through the department to the Registrar's Office, which in turn will
notify the student that the course has been dropped from his or her schedule. Students should not
assume they have been dropped unless they receive notification from the Registrar's Office. No fee will
be charged."
Academic Honesty
You are expected to uphold the OSU standard of conduct for students relating to academic honesty.
Academic dishonesty is defined as an intentional act of deception in which a student seeks to claim
credit for the work or effort of another person or uses unauthorized materials or fabricated
information in any academic work
You assume full responsibility for the content and integrity of the academic work they submit. The
guiding principle of academic integrity is that a student's submitted work, examinations, reports,
and projects must be that student's own work for individual assignments, and the group's own
work for group assignments/projects. You are guilty of academic dishonesty if you:

Use or obtain unauthorized materials or assistance in any academic work; i.e., cheating.


Falsify or invent any information regarded as cheating by the instructor; i.e., fabrication.
Give unauthorized assistance to other students; i.e., assisting in dishonesty.


Represent the work of others as their own; i.e., plagiarism.
Modify, without instructor approval, an examination, paper, record or report for the
purpose of obtaining additional credit; i.e., tampering.
The penalty for academic dishonesty is severe. Any student guilty of academic dishonesty may be
subject to receive a failing grade for the exam, assignment, quiz, or class participation exercise as
deemed appropriate by the instructor. In addition, the penalty could also imply that the student
receive a failing grade for the course and be reported to the University officials at the College of
Business, and the officials at the Office of Student Affairs.
For details on the OSU policies on academic honesty, refer to
http://oregonstate.edu/studentconduct/http:/%252Foregonstate.edu/studentconduct/code/index.php
Students with Disabilities
Oregon State University is committed to providing equal opportunity to higher education for
academically qualified students without regard to a disability. Accommodations are collaborative
efforts between students, faculty and Disability Access Services. Students with disabilities are
encouraged to contact Disability Access Services to learn more about their rights and responsibilities.
Students with accommodations approved through Disability Access Services are responsible for
contacting the faculty member in charge of the course prior to or during the first week of the term
to discuss accommodations. Students who believe they are eligible for accommodations but who
have not yet obtained approval through DAS should contact DAS immediately at 737-4098.