MIS 686 Data Analytics and Visualization
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MIS 686 Data Analytics and Visualization
Jesse H. Jones School of Business
Texas Southern University
Instructor: Dr. Mayur S. Desai
Fall 2020
Course Syllabus (Tentative)
MIS 686 Data Analytics and Visualization
Time: MW 6:00P.M. – 7:30P.M.
Room No: JHJ 108 (Hybrid) – JHJ School of Business
Office: JHJ 320 – JHJ School of Business
Office Phone: (713) 313-7279, Mobile: (713) 553-9105 Fax: (713) 313-7722
Office Hours: TR 4-5:30 PM, MW 4-5:30 PM & F10AM-1PM Online, By Appointments
MIS 686 Data Analytics and Visualization
Help Desk: helpdesk@tsu.edu (713) 313-4357
Need Help with an Assignment in this course?
INTRODUCTION
MIS 686 DATA ANALYTICS AND VISUALIZATION (3)
This course provides an overview of gathering, cleaning, describing, integrating data, and, developing data models. Students will apply descriptive statistical tools to get a comprehensive understanding of the datasets and visualize those using data visualization tools. Students will be introduced to real-world datasets and will have hands-on experience in using them in building preliminary decision models. (Prerequisites: MGSC 624, MIS 672)
MIS 686 Data Analytics and Visualization .
COURSE APPROACH
MIS 686 takes a combination approach that includes textbook readings, faculty presentations, videos, tutorials, and projects. The course is delivered in a series of modules, which demonstrate the applicability of data analytics and visualization to several business disciplines while introducing various tools and techniques. The textbook readings will help to convey the main body of knowledge for this class and include a variety of terms, concepts, and issues related to data and business analytics. Practice exercises and homework assignments are used to provide specific business examples illustrating the content analyzed in the course readings.
COURSE DESCRIPTION
The use of Business Intelligence Tools to manage and optimize business performance. This course is intended to provide an integrative foundation of business intelligence at the operational, tactical, and strategic levels. It will focus on the basics of data warehousing, processing, and mining to identify, analyze, interpret and present results to support decision making and proactively influence and maximize business profitability and returning shareholder value. Students will learn how to utilize managerial decision-making tools to analyze complex business problems and to arrive at sound decisions.
GOALS
The objective of this course is to explore how data analytic tools can be used to monitor business performance and manage strategic capabilities that proactively influence and maximize business profitability. Towards the end of the semester, students will:
- Establish a common understanding of data analytics and the trends driving it.
- Understand how data and business analytics and data visualization are used to support BI.
Required Text Books and Materials
Jamsa, Kris, Introduction to Data Mining and Analytics with Machine Learning and Python, Jones & Bartlett Learning 2021, LLC, an Ascend Learning Company ISBN: 978-1-284-18090-9
MIS 686 Data Analytics and Visualization
Course Communication
All students will use email for communicating with the instructor. Contact via phone only if it is difficult to communicate certain aspects of the course via email. Every student is required to have an account on Blackboard (http://texsu.blackboard.com/). A separate assignment link will be provided to the students for submitting their assignments. If you do not have a user name or password to access blackboard you email Kenneth Collins at Kenneth.collins@tsu.edu.
Class Participation
You are strongly encouraged to participate in the class discussion. Remember – NO QUESTION is a STUPID question. Read the business section of a newspaper that talks about technology and new products and share the information with your classmates. Read the articles in current periodicals related to information technology and their impact on businesses and be prepared to discuss in the class.
Group Projects
The course includes group projects. We will discuss Group projects at various class times during the semester.
The group project will be based on the Business/Data Analytics software. The details will be discussed during the first class meeting. The purpose of the group project is to enable students to work as a team player. It also allows students to improve their communication and people skill in performing a specific task and solving project-related problems.
Individual team member’s grade may vary depending upon the team member’s participation in group project activities. At the end of the semester team members and group project evaluation will be provided to assess the team member and group performance. Each group will manage their group project meeting times to discuss and work on group projects and provide the status of their group projects when requested.
Individual Assignments
Individual assignments will include developing dashboards using analytics software such as Tableau, Excel, Rapid Miner and/or SAP analytics. The details will be discussed during the first-class meeting of the semester.
Quizzes / Pop Assignments
Every week students will be given a short assignment or a quiz. The assignment and quiz will be based on the topics we will discuss during the week or related to the projects.
MIS 686 Data Analytics and Visualization
Exams
The Mid-Term exam will cover all material covered in the class up to the class before the mid-term exam. The format of the mid-term and final exams will be mostly short essay questions and multiple-choice questions. The Final Exam will be comprehensive; however, majority of the exam will be over chapters covered after the mid-term exam and the weekly topics.
Grading
Exams will cover the material in the text, which should have been learned from assigned exercises in the PC lab, or extra insights from in-class demonstrations. Unexcused absences will result in zeros on exams. Exams missed for a valid reason are handled by taking a make-up exam. Make-up exams will address the assigned material in more depth to adjust for the student having had additional preparation time.
Grades are assigned using the following scale
90-93(A-), 94-97(A), 98-100(A+) 80-83(B-), 84-87(B), 88-89(B+)
70-73(C-), 74-77(C), 78-79(C+) 60-63(D-), 64-67(D), 68-69(D+) below 60 = F
The distribution of the exam scores is designed very carefully. Grade distribution is such that students will have ample of opportunities and multiple ways to demonstrate their ability to perform. Students will be able to monitor their performance on a regular basis. A summary of up-to-date grades will be periodically provided to the students so that they can manage their course work. Students are encouraged to discuss their difficulties related to the course and assignments with me anytime.
| Mid-Term | 15% |
| Final Exam | 15% |
| Group Project | 20% |
| Individual Assignments | 40% |
| Quiz/Pop Assignment/Class Participation | 10% |
| Total Points | 100% |
Cheating Policy
Cheating, in any form, will result in an automatic grade of “F” in the course, the removal of the student from the course, and immediate reporting of the student’s actions to the Office of the Dean of JHJ School of Business. Cheating includes collaboration on any outside assignments, which might be made on an individual basis for a grade, including regular homework assignments and the preparation of case materials for submission. It also includes plagiarism, unauthorized preparation of notes for use on examinations, uses of such notes during an examination, looking at another student’s examination answers, allowing another student to look at your own examination answers, or the requesting or passing of information during an examination. This policy is intended to protect the honest student from unfair competition with unscrupulous individuals who might attempt to gain an advantage through cheating. Students who become aware of suspicious activities on the part of others are asked to promptly notify the professor so that immediate corrective action can be taken.
TSU SASO (Student Accessibility Services Office) Policy:
Texas Southern University (TSU) complies with non-discriminatory policies for students with disabilities under the guidelines of the Americans with Disabilities Act (ADA) of 1990, the ADA Amendment Act of 2008, and the Rehabilitation Act of 1973 (Section 504). These Acts mandate equal opportunities for qualified individuals with disabilities in higher education public facilities, programs, activities and services. Under these federal guidelines the University is required to protect the civil rights of students with disabilities; maintain the student’s confidentiality; and provide reasonable academic accommodations/auxiliary aids to students with documented disabilities.
It is the policy of Texas Southern University to provide reasonable and appropriate accommodations for qualified individuals who are students with documented disabilities. The University will adhere to all applicable Federal and State laws regulations and guidelines with respect to providing reasonable accommodations as required to for equal education opportunity.
It is the student’s responsibility to contact the ADA/Section 504 Coordinator, in a timely manner if he/she desires to arrange for any accommodations. Any student who may need accommodations based on the impact of a disability should contact the Student Accessibility Services Office (SASO) at 713-313-7691 or 713-313-4210. The Student Accessibility Services Office (SASO) is located in the TSU Student Health Center and their web-link is as follows: http://students.tsu.edu/departments/disability-services/
If any student needs accommodations approved and documented by SASO please notify me at the beginning of the semester, so that appropriate accommodations can be arranged. Please note that accommodations can only be extended if the same have been approved and documented through the appropriate administrative departments and channels at Texas Southern University and no accommodations can be extended without appropriate documentation from the TSU SASO office and such accommodations will be explicitly limited to as approved and documented by the TSU SASO office. Also, no accommodations can be extended prior to the date such accommodations were approved and the instructor received official notification, and, accommodations cannot be applied retroactively.
COVID-19 Guidelines for the Students
Face coverings are required in our classroom: Per TSU’s Administrative Directive which aligns with locally mandated orders for Harris County, face coverings that cover the nose, mouth, and chin are required to be worn in all learning spaces at TSU (e.g., in classrooms, laboratories and studios). Any student who violates this directive will be asked to immediately leave the learning space and will be allowed to return only when they are wearing a face covering. Subsequent episodes of noncompliance will result in a Student Code of Conduct complaint being filed with the Dean of Students Office, which may result in sanctions being applied. The student will not be able to return to the learning space until the matter is resolved.
MIS 686 Data Analytics and Visualization
Schedule of Class Activities (TENTATIVE)
| Date | Topics to be discussed | Quiz/Exams/Projects, & Due Dates (TBD) |
| Aug 19, 21 | Chapter 1: Data Mining and Analytics
|
|
| Aug 26, 28 | Chapter 3: Databases and Data Warehouses | Quiz 1, Individual Assignment 1 |
| Sep 2, 4 | Chapter 4: Data Visualization | Quiz 2 – GROUP PROJECT ASSIGNMENTS |
| Sep 9, 11 | Chapter 4: Data Visualization (Continue) & Chapter 5: Spreadsheet Models (Excel) & Tableau | Quiz 3, Individual Assignment 2 |
| Sep 16, 18 | Chapter 4: Data Visualization (Continue) & Chapter 5: Spreadsheet Models (Excel) & Tableau | |
| Sep 23, 25 | Chapter 4: Data Visualization (Continue) & Chapter 5: Spreadsheet Models (Excel) & Tableau | Quiz 4 Individual Assignment 3 |
| Sep 30, Oct 1 | Mid-Term Exam Review / Mid-Term Exam | |
| Oct 5, 7 | Chapter 4: Data Visualization (Continue) & Chapter 5: Spreadsheet Models (Excel) & Tableau | |
| Oct 12, 14 | SAP Analytics | Quiz 5, Individual Assignment 4 |
| Oct 19, 21 | SAP Analytics | |
| Oct 26, 28 | Chapter 8: Programming Data Mining and Analytic Solutions (Python & R) | Quiz 6, Individual Assignment 5 |
| Nov 2, 4 | Chapter 8: Programming Data Mining and Analytic Solutions (Python & R) | |
| Nov 9, 11 | Chapter 8: Programming Data Mining and Analytic Solutions (Python & R) | GROUP PROJECTS Presentation |
| Nov 16, 18 | FINAL EXAM (TBD) | |
Note: Any changes to the topics, assignments, and due dates will be announced.
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