<Course #> <Prof Name> Updated 12/18/2016
TCMG 568 - 6M1 FOUNDATION OF INFORMATION ANALYTICS
Semester: / Spring 2017 / Instructor: / Muhammad UddinCourse Number: / TCMG 568 - 6M1 / Office: / None
Credit Hours: / 3 / E-mail: /
Class Location: / Tech 113 / Phone: / 203-543-9688
Regular Class Time: / Monday, 6:15 to 8:45pm / Office Hours: / By appointment
1. Course Description:
This course is a required course for the concentration in information analytics and will introduce the foundations of informatics. The course focuses on business/engineering managers, information professionals, business/technology analysts, as well as general audience who are interested in applying data mining, statistics and basic programming techniques to solve real-world problems. The basic principles of informatics that govern communication systems, quantitative techniques, data structure, data management, support and evidence based business and technology decision support will be explored.
2. Course Pre-requisites
Basic knowledge of computer software and database systems including Microsoft office
3. Course Learning Objectives:
1. To equip students with a basic background in databases and analytics programming concepts that relate to business intelligence and business analytics.
2. To develop technical skills necessary to manage programmers, developers and others in related areas.
3. To develop critical thinking and problem solving skills around business intelligence and analytics programming and data management methodologies.
4. To provide an overview of business intelligence solution architecture and the tools required to conduct and analyze information for decision support.
5. To provide exposure to the tools and platforms used in business intelligence.
6. To provide exposure to Big Data Analytics, tools and technologies.
4. Course Topics
· SQL Language
· Database programming and implementation fundamentals
· Relational database management systems (RDBMS) Concepts
· Microsoft SQL Server Administration and Managmenet Concepts
· Entity Relationship and data modeling concepts and tools
· Business Intelligence (Microsoft SQL Server BI),ETL, OLAP, OLTP, SSIS, SSRS, SSAS
· Introduction to Software development and Object Oriented Programming (OOP) – C#
· Introduction to data drivent application development
· Python Data Analysis.
· Fundamentals and Challenges of Big and Unstructured. Potential in real the world.
· Challenges and opportunities in big data
· Data Modeling: Classification, linear discriminant functions, regression modeling
· Linear/Predictive Modeling: Correlation à supervised learning, visualization, probability Applications
· Data and Business Analysis with R langauge and working with RStudio
5. Teaching Methods:
· Lecture on weekly topic coverage (mostly white board but may use some power points slides)
· Interacting with students to improve their learning and thinking process during class hours.
· Expecting them to come back with questions for next week when we start Lab work
· Lab work to give them hands-on training but not like short course academy style but through lens of graduate level course and standards, such as Visualizing the tools in the real world while learning and practicing during lab work to maximize the understanding and motivation.
6. Required Text Books & Materials:
1. (for database) Abraham Silberschatz, Henry F. Korth, and S. Sudarshan, Database Systems Concepts, 5th Edition, ISBN-10: 0072958863
2. (for data analytic) Michael Milton, Head First Data Analysis, O’Reilly. ISBN-10: 0596153937
Note: You are encouraged to ask the Reference Librarian at Wahlstrom Library for any other research information you may need regarding your project.
Note: These are optional textbooks. The course work is Lab oriented and spanned over various tools and areas and therefore one book can’t be enough. Instructor will provide relevant handouts, references and Lab manuals (electronic).
7. Recommended References:
· Microsoft Documentation
· Python documentation
· R documetnation
8. Important Dates
Refer to the UB Academic Calendar for important dates:
http://www.bridgeport.edu/academics/academic-calendar/
First Day for this class Monday, Jan 26, 2017
Midterm Exam Monday, March 27, 2017
Last Day of Classes Monday, April 24, 2017
Final Exam Monday, May 01, 2017
Final Grades Due Monday, May 03, 2017
9. Course Requirements:
Attendance, Class Participation and Current Events(news) 10%
Lab work 20%
Midterm 20%
Written Term Paper/Project & Oral Summary/Case Studies 20%
Exam (Final) 30%
Total 100%
9.1 Attendance, Class Participation and Current Events(news) 10%
The students are highly recommended to participate in the class, ask questions and bring interesting news to share with everybody and initiate constructive class discussions.
9.2 Lab work 20 %
Students are required to complete the lab work. Teacher will be helping them throughout to understand and accomplish the end goal. However, students need to show dedicated learning comitment throughout. Students are advised to spend several hours during the week to practice as much as possible so we can call fill any gaps during lecture/lab hours.
9.3 Midterm 20 %
The students are expected to perform in the midterm. Teacher will provide enough coverage for their preparation and we will go through midterm review before the day. We will go over midterm after students have finished it so we can address any gaps and to fix what went wrong if it did.
9.4 Written Term Paper/Project & Oral Summary/Case Studies 20 %
Teacher will provide them the project kick off document in the first week. We will go over it to develop a game plan for bi-weekly project updates and progress. Project will contain a IEEE style written paper in the area of business intelligence, data mining and data prediction. Students are highly encouraged to work hard and extend this work beyond the semester to have great publications, if they desired to.
9.5 Final Exam 30 %
Throughout the semester, we will be preparing for this day. We will be committed to peform as much as possible. We will keep the final exam expectations in mind from the day 1 so everybody learn constructively and build her or his career down the road. Student will earn good grades from their continued and honest hardwork. We will have a special review before this day for the entire coursework to make sure everbody is ready.
General
1. Class Attendance, Participation, Punctuality, Cheating and Plagiarism: Attendance at each class session is expected. Class lectures complement, but do not duplicate, textbook information. Together the students and instructor will be creating a learning organization. Students are expected to be on time for class. A significant portion of your learning will accrue through the constructive and respectful exchange of each other’s ideas (including mine!) and search for alternative solutions. You must be actively engaged in class discussions to improve your thinking and communication skills.
Be certain that your travel arrangements do NOT conflict with any of your team or individual presentations.
2. Preparation, Deadlines and Late Policy: Late assignments will be penalized 20% for each class day past the deadline. No excuses will be accepted. Don’t wait until the last minute to print out your assignment.
3. UB Policy: It is the student's responsibility to familiarize himself or herself with and adhere to the standards set forth in the policies on cheating and plagiarism as defined in Chapters 2 and 5 of the Key to UB http://www.bridgeport.edu/pages/2623.asp or the appropriate graduate program handbook.
Cheating and plagiarizing means using the work of others as your own. Copying homework, using papers from the Internet, any talking or looking around during exams and allowing others to look at your exam papers are examples of cheating.
As a UB policy, it is expected that each student that attends one hour of classroom instruction will require a minimum of two hours of out of class student work each week for approximately fifteen weeks for one semester.
10. Final Course Grade
Letter Grade / Range (%)A / 94.9 – 100.0
A- / 90 – 94.8
B+ / 87 – 89.9
B / 83 – 86.9
B- / 80 – 82.9
C+ / 77 – 79.9
C / 73 – 76.9
C- / 70 – 72.9
D+ / 67 – 69.9
D / 63 – 66.9
D- / 60 – 62.9
F / Below 60
11. Schedule & Assignments
1. Our weekly layout may change due to holidays, cancellations, class performance, or emergency situation and we will adjust our schedule accordingly and as/if needed, we will arrange for makeup classes if needed.
2. We may have extra Sunday sessions if needed and you can come optionally if you need help.
3. Project PDF will be delivered separately and we will talk about it in first lecture. I will provide you the game plan and topics to choose from. We will make groups for teamwork of projects/research paper.
4. Lab manuals will be available in PDF for each lab for you to follow and learn from it.
5. All PDFs material and reference study material will be uploaded to Canvas prior to first lecture.
6. We will make project groups (3 students) to do the joint work on project and on case studies.
7. Case studies PDF will be provided to you on canvas.
8. Though you see only four labs but our sessions may span over two weeks for a single lab to learn effectively.
9. You are expected and recommended to revise what you learn every week and continue on Lab work on weekly basis, following each weekly lecture.
10. There is a folder, on Canvas as “General Study References”. It is highly recommended that you read the technical and white papers from great players in the market. It is not required but to motivate you throughout the semester for your extra knowledge and ideas. Check the folder time to time to see if I have put any new material in it. If you come across any interesting ideas, don’t hesitate to share with all of us in the class.
Week / Date / Time / Topic & Assignments1 / 01/23 / 6:15pm to 8:45pm / Lecture work
1. Introduction to Course Structure (Syllabus as guidelines) and Data Science, Databases and Analytics concepts in nutshell
2. Defining our end goals for this course in light of class lectures, labs, research paper, presentations, case studies, homework, exams and final project/paper and groupings.
3. Getting to know each other (Instructor and Students)
4. Canvas website portal overview for you to get access the relevant documents and PDFs.
5. Intro to Relational Database Management Systems (Microsoft SQL Server)
Lab work
1. Open up Lab1-PDF manual before lecture begins and read through
2. Login to the computers and look around what software’s we have.
3. Read the FinalProject_DA.PDF
4. Starting looking into Reference Study Material for the whole course to get motivated, moving forward.
5. Look around on Canvas folders and see what you have got so far.
2 / 01/30 / Lecture work
1. Introduction to Structure Query Language (SQL) and its variations
2. Relational Database Management Systems (Microsoft SQL Server, Oracle, etc)
3. Database concepts, data modeling, data architecture, data structures and data optimization concepts (including flat, hierarchical, relational, object-oriented, network, snowflake, star schema and operational data stores)
4. Database development and administration, data backups and restores, disaster recovery and highly availability solutions, DDL, DML.
3 / 02/06 / Lab work
1. Getting started with Lab 1
2. Lab 1 – Working with Database Engines, Creating and working with database using SQL and GUI (Instructor lead session)
3. Run the sample Code provided on Canvas for this lab.
4. Research Paper/Project Update
4 / 02/13 / Lab 1 continued
5 / 02/20 / Introduction to Business intelligence.
1. Microsoft Business intelligence (SSIS, SSRS, SSAS)
2. Introduction to Data Warehouse, OLAP, OLTP, ETL.
3. Introduction to various BI tools from various vendors in the real world.
4. Research Paper/Project Update
Lab 2– Working with MS BI Tools
.
6 / 02/27 / Lab 2 continued
Research Paper/Project Update
7 / 03/06 / 1. Introduction to Programming and Software Development.
2. Introduction to OOP, Web application, Web services, XML, SOA and Cloud computing.
3. Introduction to C#, and Web programming
4. Depth Introduction to Python
5. Data Analyis and Data Mining with Python
Review for Midterm – Refreshing what we have learned so far
8 / 03/13 / SPRING BREAK
9 / 03/20 / Midterm and Midterm review afterwards
Research Paper/Project Update
10 / 03/27 / 1. Introduction to Big Data and Big Data Analytics and Processing
2. Introduction to Data retrieval and processing.
3. Very Large Information Systems and Information Retrieval
4. Introduction to Data Mining
5. Introduction to unstructured data real world examples (Social Networking, text, audio and video, healthcare data, etc)
6. Machine learning
7. Python and R language
8. Case Studies
Lab 3 – Data Analysis using Python and OOP
11 / 04/03 / Lab 3 continued
Research Paper/Project Update
12 / 04/10 / 1. Business Analytics (Descriptive, Predictive and Prescriptive)
2. Data Modeling, Regression Modeling, Classification, Linear discriminate function
3. Linear/Predictive Modeling: Correlation, supervised segmentation, visualization, probability applications.
4. Data Analyis using Pythong and R Language.
5. Case Studies
Lab 4 – Data Analysis using R language
13 / 04/17 / Lab 4 continued and Final Exam review
Research Paper/Project Update
14 / 04/24 / Project Presentations and paper e-copy due.
15 / 05/01 / Final Exam
12. General Policies for the Course
12.1 Academic Honesty:
· It is the student's responsibility to familiarize himself or herself with and adhere to the standards set forth in the policies on cheating and plagiarism as defined in Chapters 2 and 5 of the Key to UB http://www.bridgeport.edu/pages/2623.asp or the appropriate graduate program handbook.
· If you are caught cheating or plagiarizing, you will be warned once and you will receive a zero (0) grade for that assignment. A second offense will result in an F grade for the course.
12.2 Attendance:
· For on-campus classes, the fourth unexcused absense will result in a failure of the class.
12.3 Work Effort:
· As a UB policy, for a three credit course, it is expected that each student that attends one hour of classroom instruction will require a minimum of two hours of out of class student work each week for approximately fifteen weeks for one semester in compliance with the Carnegie Unit of Credit.