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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.