Carolina Salge: MWF 11:15 - 12:05pm (Sanford Hall 212)
Final Exam: Fri., Dec. 9, 12:00 - 3:00pm
This course provides the skills necessary to conceptualize, build, and implement systems utilizing business intelligence in organizations. Topics include big data, executive information systems, dashboards and scorecards, machine learning, text mining, and mapReduce. The course is divided into two sections: (1) descriptive and (2) predictive analytics.
The course syllabus is a general plan for the course; deviations announced to the class by me (the instructor) may be necessary.
Students completing this course will
There is no textbook for this course. Links to required readings are on the schedule. Videos are available on the Teradata University Network. The password is analytics (not case sensitive).
We will use MicroStrategy (manual) for descriptive analytics. We will then use Tableau (training), SAS Visual Analytics (documentation), IBM Watson Analytics (getting started), and BigML (video) for predictive analytics.
R is an open-source software environment for statistical computing and graphics, and RStudio is the interface to R. Download the latest versions for your operating system since you will be required to complete a number of online lessons to learn R at DataCamp. While the introductory lesson is free, you will be required to complete some courses that are not. You should expect to pay somewhere between $18-27 to complete these courses. This assignment is not a group project; all your work should be conducted individually and without consultation with any other students in the class.
Each one of you should:
Reminder:If you do not plan to continue studying R with DataCamp when you conclude the course requirements, please remember to cancel your subscription.
|DC1||Introduction to R||Write your first R code and discover vectors, matrices, data frames and lists||Approximately 4 hours|
|DC2||Intermediate R||Learn about conditional statements, loops and functions to power your R scripts||Approximately 6 hours|
|DC3||Data Visualization in R with ggvis||Use the grammar of graphics to create (interactive) graphics of your data||Approximately 4 hours|
|DC4||Reporting with R Markdown||Create dynamic html documents, presentations, and PDF reports in R||Approximately 3 hours|
Groups should contain three-four persons.
As a University of Georgia student, you have agreed to abide by the University's academic honesty policy, "A Culture of Honesty, " and the Student Honor Code. All academic work must meet the standards described in "A Culture of Honesty." Lack of knowledge of the academic honesty policy is not a reasonable explanation for a violation. Questions related to course assignments and the academic honesty policy should be directed to me.
In this class, you will work in teams. As a result, review a short report on team effectiveness and establish a team agreement (sample agreement). Give me a copy of your team agreement by Aug-26.
It occasionally happens in class and enterprise settings that someone in a group is not prepared to do his/her share. In the case of my classes, I recommend that the team give the freeloader one warning and then fire that person from the team. That person will then do group assignments individually or find another team to join. The team should notify me of the change in team composition immediately. I distribute a form to assess team participation at the end of the semester. If a major disparity in team contribution is reported, I will adjust team project grades.
Students are welcome to use laptops in class for note taking and completing class exercises, exclusively. If you plan to take notes, please advise and email a copy of the notes at the end of each class.
Attendance and participation are required for this course. Excessive unexcused absences (i.e., greater than 4) will result in a Drop or Withdrawal for Non-Attendance according to UGA policy.
See the class schedule for the due date. The due time is 11:59pm on the due date.
|Create a balanced scorecard for yourself. In particular, identify three to six major perspectives in your personal and professional life that need to be carefully monitored. Then pick one of the major perspectives (e.g., doing well physically) for more careful analysis and identify component parts (e.g., getting healthy) and specific metrics (e.g., dropping weight) that are relevant. It should be clear how all of the metrics are measured and calculated.|
|MicroStrategy Web MMT
|Access the web version of MicroStrategy (through the Teradata University Network) to learn how to use the software’s reporting and analysis capabilities. Begin by registering (use your full name as your ID). Complete all of the training modules and pass the test for each module with a score of 100%. Continue to take the tests until you score 100%.|
|MicroStrategy Mobile BI
|It has been suggested that the movement to mobile BI is as significant as the evolution of client/server computing to web-based applications.
It places BI in the hands of users wherever they are through a variety of smart devices.
The major BI vendors recognize this movement and have added mobility to their products; that is, mobile devices connect users to the vendors’ BI platforms to access reports, graphs, dashboards/scorecards, and specialized applications.
MicroStrategy is a leader in mobile BI.
The first step in this assignment is to watch a video about MicroStrategy mobile BI here.
While watching this video, answer the following questions:
|Assume you are a marketing manager in Superstore and you have a sense that there are profitability issues in your products.
You don’t know exactly how to define the problem nor what factors contribute to the issues.
But, you want to explore this situation by visualizing the data you’ve received from those kind folks in IT.
Import the Coffee Chain dataset and begin to explore by asking: |
|SAS Visual Analytics
|Complete the first three SAS Visual Analytics assignments under Course Contents on the SAS VA homepage.|
|Create a Twitter account with a valid phone number.
Go to Twitter App, login and create an application.
Use the information from your account in RStudio to set up access to Twitter's API connection.
Choose a topic of your interest and search for the most recent 10,000 tweets in English.
Extract the text from your tweets and clean them by removing punctuations, numbers, stop words, and white space.
In addition, transform the text to lower case and remove the keyword(s) included in your search.
Finally, create a word cloud to get a crude idea of what is recently being said about your chosen topic on Twitter.
Note. You will need to install and use four different packages for this assignment: twitteR, RCurl, tm, and wordcloud.
(Link to Dashboard)
|Create a shiny dashboard with information about a topic of your interest. However, make sure that you:
|Using Delta’s performance data for February 2013 do the following:
A presentation is required from each group on a business intelligence software, with a particular concentration on open-source products.
Some suggested softwares follow, and you can propose others by contacting me. You should submit your bid for a software via e-mail. When submitting a bid specify your team's name. Those who bid early present early.
|Apache Zeppelin (pdf)||Sept 26|
|TIBCO Spotfire (ppt)||Sept 16|
|SAP's Lumira (ppt)||Sept 9|
|DQ Analyzer (pdf)||Sept 28|
|Amazon QuickSight (ppt)||Sept 12|
|Amazon ML (ppt)||Sept 7|
|QlikView (ppt)||Sept 23|
|Information Builder's WebFOCUS|
|Logi Vision (ppt)||Sept 30|
|Microsoft PowerPivot (ppt)||Sept 21|
|Google BigQuery (ppt)||Sept 2|
|Google Prediction (ppt)||Aug 31|
|PredicSis (ppt)||Sept 19|
Select a project of your own choosing. This requires developing an appropriate application, (i.e., being able to explain or predict a phenomenon), preparing and analyzing the data, creating a model (if a predictive application), and telling about your analysis in a story (presentation to the class). The more challenging the project, the better the opportunity for a top grade. Although data sets are available, more challenging projects identify and collect their own data, such as off the Internet.
Follow the guidelines for the IBM Watson Analytics project.
You should submit your bid for a presentation date via e-mail. When submitting a bid specify your team's name and chosen application topic.
|Application Topic||Presentation Date||Order|
|Game of Thrones||Nov 30||1|
|March Madness||Nov 30||3|
|UGA Campus Transit||Nov 30||4|
|UGA Parking Services||Dec 2||4|
Part A. In the 2012 Presidential Election, Obama and the Democrats received considerable recognition for the use of analytics to understand the electorate and get out the vote. Use the 2012 POTUS Winner by County dataset in BigML to answer the following questions:
Part B. Churn is a major problem for telecommunications firms. It is not unusual for 20 percent of a company’s customers to not renew their contracts. Because of this, Telcos are using analytics to identify customers that are most likely to churn so they can intervene to try to influence these customers to stay, such as providing attractive offers to renew or the promise of better service through a new cell tower. Use the Telco churn dataset to develop a model to predict churn. Exclude any variables that are unlikely to be related to churn and provide the logic behind your thinking. Also check for any variables that need to be recategorized from numerical to categorical and discuss why.
Part C. Using one of the other datasets provided on the BigML site, create a model, and then develop questions that you answer using the Prediction feature. In designing your model, first build it using part of the data (say 80%) and then test it using the remaining data.
Follow the guidelines for the BigML project.
There are two components: (1) Completion of the Arch Ready Professionalism Certificate, which requires attending five events offered by the UGA Career Center or the Terry College of Business, and (2) attending two SMIS meetings.
If you have potential conflicts with meeting the professional development requirements or if you think that there are better development activities for your situation, meet with me to discuss the possibilities. This meeting must be at the start, and not the end, of the semester and is your responsibility to schedule.
There will be ten pop quizzes to test your preparation and understanding of the reading assignments. Quizzes will assess critical points in the readings and will never cover unimportant details. It is critical that you read each and every article prior to class time.
|State of the Art Presentation||7|
|Data Camp R||10|
|If you are unable to complete an exercise on time or take an exam at the specified time, please advise me as soon as possible so that alternative arrangements can be made.|
|Class||Day||Date||Readings / Videos||Assignment||Resubmission|
|3||Wednesday||8/17||Career in BI (ppt)|
|4||Friday||8/19||Case Study Session: The BI/Analytics Game (ppt)|
|5||Monday||8/22||History of BI - Part One (ppt)
*Read only pages 488-498
|6||Wednesday||8/24||History of BI - Part Two (ppt)
*Read only pages 499-506
|7||Friday||8/26||Executive Information Systems (ppt)|
|8||Monday||8/29||Dashboards and Scorecards (ppt)|
|9||Wednesday||8/31||The Balanced Scorecard (ppt)|
|10||Friday||9/2||Case Study Session: Search Term Analysis at Bloomingdales - Guest Speaker: Rick Watson|
|11||Monday||9/5||Labor Day Holiday||A1|
|12||Wednesday||9/7||Introduction to MicroStrategy (ppt)|
|13||Friday||9/9||Case Study Session: Sports Analytics (ppt) - Guest Speaker: Karim Jetha
Ross, T. F. 2015. Welcome to Smarter Basketball. The Atlantic.
MIT Technology Review. 2015. How Network Theory is Revealing Previously Unknown Patterns in Sports. Emerging Technology from the arXiv.
Blum, R. 2015. The Big Shift: Infields Spin In Response to Data Explosion. AP/New York Times.
|14||Monday||9/12||Mobile BI - Part One (ppt)||A2|
|15||Wednesday||9/14||Mobile BI - Part Two (ppt)|
|16||Friday||9/16||Case Study Session: Mobile BI (ppt)
Briggs, L. L. 2011. Apparel Company App Melds Fashion, Mobile BI. Business Intelligence Journal.
Watson, H. & Leonard, T. 2011. US Xpress: Where Trucks and BI Hit the Road. Business Intelligence Journal.
|17||Monday||9/19||Introduction to Tableau - Part One (zip file) (ppt)
*Read only page 25
Hardin, M., Horn, D., Perez, R., & Williams, L. 2013. Which Chart or Graph is Right For You? Tableau Whitepaper.
|18||Wednesday||9/21||Introduction to Tableau - Part Two (zip file) (ppt)|
|19||Friday||9/23||Work on Tableau Assignment|
|20||Monday||9/26||Selecting the Best BI Tool (ppt)||A4||A3|
|21||Wednesday||9/28||BI Project Approval (ppt)|
|22||Friday||9/30||Midterm Exam Review: What Have We Learned Thus Far? (ppt)|
|23||Monday||10/3||Midterm (Multiple Choice, True/False, & Fill in the Blanks) (sample)|
|24||Wednesday||10/5||Midterm (Short Answer Essay) (sample)||A4|
|25||Friday||10/7||Midterm (Software Problem Application) (data)|
|26||Monday||10/10||R and Tableau - Guest Speaker: Ben Daniel at HomeDepot (Skype Lecture) (Tableau/R Instructions) (zip file) (R example script)|
|27||Wednesday||10/12||Midterm Results & Introduction to SAS Visual Analytics|
|28||Friday||10/14||Data Warehouse - Guest Speaker: Yissel Cervantes at CTS (ppt)|
|29||Monday||10/17||Introduction to IBM Watson Analytics (ppt)
|30||Wednesday||10/19||Big Data (ppt)||A5|
|31||Friday||10/21||Work on Watson Analytics Project|
|32||Monday||10/24||Text Mining with R - Sentiment Analysis (ppt) (score.sentiment function)|
|33||Wednesday||10/26||Text Mining with R - Word Cloud (ppt) (data)||A5|
|35||Monday||10/31||Dashboards with R - The Basics (ppt)|
|36||Wednesday||11/2||Dashboards with R - More Basics (ppt)||A6|
|37||Friday||11/4||Work on Watson Analytics Project - BigML (ppt)|
|38||Monday||11/7||MapReduce - An Introduction (ppt)|
|39||Wednesday||11/9||MapReduce with R - Installation|
|40||Friday||11/11||MapReduce with R - Application (ppt)|
|41||Monday||11/14||Case Study Session: Social Bots (ppt)|
|42||Wednesday||11/16||Using R for Analytics at FirstData - Guest Speaker: Michael Anton||A7||A6|
|43||Friday||11/18||Analytics Experiences with Fiserv - Guest Speaker: Bob Trotter|
|47||Monday||11/28||IBM Watson Analytics Project Presentations|
|48||Wednesday||11/30||IBM Watson Analytics Project Presentations||A8|
|49||Friday||12/2||IBM Watson Analytics Project Presentations||BigML Project|
|50||Monday||12/5||Final Exam Review (data1) (data2) (data3)||IBM Watson Analytics Project, DC1, DC2, DC3, DC4 & Extra Credit DataCamp Modules||A1|
The form should be submitted by Dec 5.