COMPUTER AND INFORMATION SYSTEMS DEPARTMENT
INFS6510 – Introduction to Data Analytics
Section: Online
INSTRUCTOR INFORMATION
INSTRUCTOR: Dr. G. Alan Davis OFFICE: Wheatley Center - #222
E-MAIL: davis@rmu.edu PHONE: 412.397.6440
OFFICE HOURS: Posted on
WEBSITE: (or via rmu.edu - search for “davis”)
COURSE INFORMATION
COURSE MATERIAL:
Required Text: Business Intelligence, Analytics, and Data Science: A Managerial Perspective - Fourth Edition, by Sharda, Delen, & Turban (Pearson/Prentice Hall, 2018)
Required Software: Microsoft Excel
Microsoft Power BI – available via Amazon AppStream
NodeXL software – available via Amazon AppStream
COURSE DESCRIPTION:
INFS6510 Introduction to Data Analytics provides the student with a broad overview of the modern Analytics landscape, including the tools and techniques that are successfully utilized by 21st century organizations. Students learn about the history and evolution of Data Analytics (DA), Business Analytics (BA), and Business Intelligence (BI), from standardized reporting to a flexible, integrated Information Ecosystem that provides modern decision makers with essential, accurate, and timely information. The emphasis of this course centers on the proper deployment and use of DA, BA, and BI techniques and technologies to best meet the information requirements of modern decision makers. An overview of current DA, BA, and BI tools is also provided, including Data Warehousing, Data Mining, OLAP, and Business Performance Management (BPM).
PRIMARY GOAL:
The primary goal of INFS 6510 Intro to Data Analytics is to provide the student with an overview of the theory, best practices, and tools associated with creating a well integrated information environment within a modern organization.
OBJECTIVES:
At the completion of the course, the student will be able to:
Topic 1: Overview of Business Intelligence (BI), Analytics, & Data Science
• Argue the need for computerized support of managerial decision making
• Explain the evolution of computerized support to the current state of analytics/data science
• Describe the Business Intelligence (BI) methodology and concepts and relate them to DSS
• Differentiate the various types of analytics
• Describe the analytics ecosystem and identify key players and career opportunities
Topic 2: Descriptive Analytics I: Nature of Data, Statistical Modeling, & Visualization
• Describe the nature of data as it relates to Business Intelligence (BI) and Analytics
• Identify the methods used to make real-world data analytics-ready
• Describe statistical modeling and its relationship to business analytics
• Differentiate Descriptive and Inferential Statistics
• Define business reporting, and understand its historical evolution
• Cite the importance of data/information visualization
• Compare and contrast different types of visualization techniques
• Argue the value that visual analytics brings to business analytics
• Discuss the capabilities and limitations of dashboards
Topic 3: Descriptive Analytics II: Business Intelligence (BI) & Data Warehousing
• Cite the basic definitions and concepts of data warehouses
• Discuss data warehousing architectures
• Describe the processes used to develop and manage data warehouses
• Explain data warehousing operations
• Explain the role of data warehouses in decision support
• Explain data integration and the extraction, transformation, and load (ETL) processes
• Discuss the essence of business performance management (BPM)
• Describe Balanced Scorecard and Six Sigma performance measurement systems
Topic 4: Predictive Analytics I: Data Mining Process, Methods, & Algorithms
• Define data mining as an enabling technology for BI
• Differentiate the objectives and benefits of business analytics and data mining
• Cite the wide range of applications of data mining
• List and describe the steps involved in data preprocessing for data mining
• Define various methods and algorithms of data mining
• Recognize and describe existing data mining software tools
• Describe the pitfalls and myths of data mining
Topic 5: Text, Web, & Social Media Analytics
• Describe text analytics and the need for text mining
• Compare and contrast text analytics, text mining, and data mining
• Discuss the different application areas for text mining
• Describe the process of implementing a text mining project
• List and describe the different methods to introduce structure to text-based data
• Describe Sentiment Analysis and explore related applications
• Cite common methods for Sentiment Analysis
• Discuss Speech Analytics as it relates to Sentiment Analysis
Topic 6: Prescriptive Analytics - Optimization & Simulation
• Explain the applications of Prescriptive Analytics techniques in combination with reporting and predictive analytics
• Describe the basic concepts of analytical decision making
• Evaluate the concepts of analytical models for selected decision problems, including linear programming and simulation models
• Discuss how spreadsheets can be used for analytical modeling and solutions
• Differentiate the basic concepts of optimization and when to use them
• Interpret the structure of a linear programming model
• Compare and contrast Sensitivity Analysis, What-If Analysis, and Goal Seeking
• Distinguish the concepts and applications of different types of simulations
• Identify potential applications of discrete event simulations
Topic 7: Big Data Concepts & Tools
• Describe what Big Data is and how it is changing the world of analytics
• Discuss the motivation for and business drivers for Big Data analytics
• Describe the wide range of enabling technologies for Big Data analytics
• Compare and contrast Hadoop, MapReduce, and NoSQL and how they relate to Big Data analytics
• Discuss the role of and capabilities/skills for data scientist as a new analytics profession
• Compare and contrast the complementary uses of data warehousing and Big Data
• Discuss the vendors of Big Data tools and services
• Describe the need for and appreciate the capabilities of stream analytics
• Apply stream analytics
Topic 8: Future Trends, Privacy, & Managerial Considerations
• Explore the emerging technologies that may impact Analytics, Business Intelligence (BI), and Decision Support
• Describe the emerging Internet of Things (IoT) phenomenon, potential applications, and the IoT ecosystem
• Discuss the current and future use of cloud computing in business analytics
• Argue how geospatial and location-based analytics are assisting organizations
• Explain the organizational impacts of analytics applications
• List and describe representative privacy, major legal, and ethical issues of an Analytics implementation
• Identify and evaluate key characteristics of a successful Data Science Professional
COURSE STRUCTURE:
The methods used in INFS 6510 - Introduction to Data Analytics include lecture and classroom discussion through examples and demonstration. At times, the instructor may make use of a computer projector and/or presentation software in a classroom lecture. The course may also include articles from leading publications in the Data Analytics (DA), Business Analytics (BA), and Business Intelligence (BI) fields. The students may be asked to review associated articles and/or do other independent research to compare and contrast DA, BA, and/or BI software tools and vendors.
STUDENT RESPONSIBILITIES
READING ASSIGNMENTS:
The student is responsible for doing all the respective reading assignments prior to class.
WRITTEN ASSIGNMENTS:
The student is responsible for completing all assignments within the allotted periods of time as outlined by the instructor. Written assignment due dates will be established either in the syllabus or provided to the students when relevant lectures are completed.
Important notes:
1. The student is responsible to back up his/her valuable diskette files appropriately
2. The student must protect his/her assignments, files, diskettes, etc. from copying by other students and against viruses.
3. Significant time outside of class is necessary to work on the various components of the written assignments.
FOLLOW-UP:
IIf a student does not fully understand a lecture subject or assignment and would like further explanation; the student is responsible to raise the topic(s) for discussion in class. If further explanation is required on an individual basis, the student is encouraged to see the instructor during office hours or make an appointment.
ASSIGNMENT DUE DATES:
R
It is the student’s responsibility complete assignments when they are due. Due dates are announced during class and clearly posted in the weekly schedule at the end of this syllabus. Assignments that are submitted after due dates will be PENALIZED 10% for each day assignment is late (NO EXCEPTIONS). It is the responsibility of the student (not the instructor) to stay current on class assignments.
A
AATTENDANCE:
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Attendance will be taken at the beginning of each class period. The CIS Department’s 25% Absence Policy will be enforced; that is, if a student misses 25% or more of the allotted semester classes, he/she will automatically receive a letter grade of F. The student is responsible for keeping a record of missed classes.
If a student is absent from a class session, that student is responsible for turning in (on time) any assignments that are due or completed/collected during that class session. It is the responsibility of the student (not the instructor) to stay current on class assignments.
MAKE-UP EXAMINATIONS:
Make-up examinations will ONLY be given in emergency situations. The instructor will make the final decision as to what constitutes an emergency situation and whether or not a make-up examination will be given.
EVALUATION CRITERIA:
Your final grade will be calculated using weighted percentages, with each of the following categories contributing, as listed:
Midterm Exam 20%
Final Exam 20%
OLAP Project 10%
BPM / Dashboard Project 10%
SNA Project 10%
Discussions Questions (4) 30%
100%
Your final grade will be calculated as follows:
GRADING SCALE:
92.51 – 100% A
89.51 – 92.5 A -
87.51 – 89.5 B +
79.51 – 87.5 B
66.51 – 79.5 C
00.00 – 66.5 F
ACADEMIC INTEGRITY POLICY
The fundamentals of Academic Integrity are valued within the Robert Morris University community of scholars. All Students are expected to understand and adhere to the standards of Academic Integrity as stated in the RMU Academic Integrity Policy, which can be found on the RMU website at rmu.edu. Any student who violates the Academic Integrity Policy is subject to possible judicial proceedings which may result in sanctions as outlined in the policy. Depending upon the severity of the violations, sanctions may range from receiving a zero on an assignment to being dismissed from the university. If you have any questions regarding the policy, please consult your course instructor.
PLAGIARISM POLICY
Plagiarism, taking someone else's words or ideas and representing them as your own, is expressly prohibited by Robert Morris University. Good academic work must be based on honesty. The attempt of any student to present as his or her own work that which he or she has not produced is regarded by the faculty and administration as a serious offense. Student academic dishonesty includes but is not limited to:
• Copying the work on another during an examination or turning in a paper or an assignment written, in whole or in part, by someone else;
• Copying from books, magazines, or other sources, including Internet or other electronic databases like ProQuest and InfoTrac, or paraphrasing ideas from such sources without acknowledging them;
• Submitting an essay for one course to a second course without having sought prior permission from your instructor;
• Giving a speech and using information from books, magazines, or other sources or paraphrasing ideas from sources without acknowledging them;
-Knowingly assisting others in the dishonest use of course materials, such as papers, lab data, reports and/or electronic files to be used by another student as that student's work.
• NOTE on team or group assignments: When you have an assignment that requires collaboration, it is expected that the work that results is credited to the team unless individual parts have been assigned. However, the academic integrity policy applies to the team and its members. All outside sources must be credited as outlined above.
ACCOMMODATIONS FOR STUDENTS WITH DISABILITIES
Robert Morris University welcomes students with disabilities into all of the University's educational programs. If you have (or think you may have) a disability that would impact your educational experience in this class, please contact Services for Students with Disabilities (SSD) to schedule a meeting with the SSD Coordinator, Molly Hill. Ms. Hill will confidentially discuss your needs, review your documentation, and determine your eligibility for reasonable accommodations. To learn more about SSD and available supports, please visit the SSD Website at rmu.edu/ssd, email ssd@rmu.edu, call (412)-397-6884, or visit the SSD office, located in Nicholson Center, Room 280.
FINAL NOTE TO STUDENTS
The instructor reserves the right to modify any schedule or policy in this class syllabus at any time throughout the class. Modifications may be made as necessary to improve the learning experience or learning environment of the student. Any such modifications will be announced during regular class or exam meeting times.
Finally, any (anonymous) data extracted from the course may be used for research purposes.
GENERAL TOPIC OUTLINE
| | | | |
|WEEK |DESCRIPTION |EST. TIME |REFERENCE TO TEXTBOOK MATERIALS, TUTORIALS, |
| | |(based on 8 week |or READING SUPPLEMENTS |
| | |session) | |
| | | | |
|1 |Introduction to Class & Assignments |1 week |Chapter 1 & Related Materials |
| | | |Discussion Topic #1 Due: Modern Analytics |
| |Intro to BI, Analytics, & Data Science | | |
| | | | |
|2 |Descriptive Analytics I: Nature of Data, |1 week |Chapter 2 & Related Materials |
| |Statistical Modeling, & Visualization | | |
| | | |Discussion Topic #2 Due: Dashboard Best |
| | | |Practices |
| | | | |
|3 |Descriptive Analytics II: Business Intelligence &|1 week |Chapter 3 & Related Materials |
| |Data Warehousing | | |
| | | |Discussion Topic #3 Due: |
| | | |Dimensional Modeling |
| | | | |
|4 |Predictive Analytics I: Data Mining Process, |1 week |Chapter 4 & Related Materials |
| |Methods, & Algorithms | | |
| | | |Discussion Topic #4 Due: |
| | | |Big Data Analytics |
| | | | |
| | | |Start Project #1 – OLAP/BI Analytics |
| | | | |
|5 |Text, Web, & Social Media Analytics |1 week |Chapter 5 & Related Materials |
| | | | |
| |OLAP Tutorial | |Midterm Exam (Chapters 1 – 4) |
| | | | |
| |Prescriptive Analytics: Optimization & Simulation|1 week |Chapter 6 & Related Materials |
|6 | | | |
| |Intro to MS-Power BI | |MS-Power BI Tutorial |
| | | | |
| | | |Submit Project #1 – OLAP/BI Analytics |
| | | | |
| | | |Start Project #2 – BPM/Dashboard |
| | | | |
|7 |Big Data Concepts & Tools |1 week |Chapter 7 & Related Materials |
| | | | |
| | | |Project #2: BPM/Dashboard Due |
| | | | |
| | | |Start Project #3 – SNA Project |
| | | | |
|8 |Future Trends, Privacy, & Managerial |1 week |Chapter 8 & Related Materials |
| |Considerations | | |
| | | |Project #3: SNA Project Due |
| | | | |
| | | |Final Exam (Chapters 5 – 8) |
YOU CONTROL YOUR GRADE!!!
You are in complete control of your grade . . .
1. I do NOT “give” grades; I only report the grade that you earn in the course.
2. I do NOT allow “extra credit” assignments to raise your grade.
3. I do NOT allow “do overs” on assignments or exams.
4. I do NOT allow the use of cell phones during in-class examinations.
5. I DO penalize for each day an assignment is late; therefore, turn in assignments on time.
6. I am happy to meet with any student who does not understand the material or an assignment. I am available during regular office hours, or by appointment.
7. If you do not earn the grade that you wanted in the class, blame the person in the mirror!
Some of my favorite quotations related to education . . .
• Educators open the door, but you must enter by yourself. – Chinese Proverb
• I never teach my pupils, I only provide the conditions in which they can learn.
– A. Einstein
• If you think education is expensive, try ignorance. – D. Bok
• You pay for your education . . . but you must earn your grade! – G. Davis
• I have never “failed” a student . . . students always fail on their own! – G. Davis
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− Course Syllabus −
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