TCMG 530



TCMG 568 - 6M1 FOUNDATION OF INFORMATION ANALYTICSSemester:Spring 2017Instructor:Muhammad UddinCourse Number:TCMG 568 - 6M1Office:NoneCredit Hours:3E-mail:muddin@bridgeport.edu Class Location:Tech 113Phone:203-543-9688Regular Class Time:Monday, 6:15 to 8:45pm Office Hours:By appointmentCourse 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.Course Pre-requisitesBasic knowledge of computer software and database systems including Microsoft officeCourse Learning Objectives:To equip students with a basic background in databases and analytics programming concepts that relate to business intelligence and business analytics.To develop technical skills necessary to manage programmers, developers and others in related areas.To develop critical thinking and problem solving skills around business intelligence and analytics programming and data management methodologies.To provide an overview of business intelligence solution architecture and the tools required to conduct and analyze information for decision support.To provide exposure to the tools and platforms used in business intelligence.To provide exposure to Big Data Analytics, tools and technologies.Course TopicsSQL LanguageDatabase programming and implementation fundamentalsRelational database management systems (RDBMS) ConceptsMicrosoft SQL Server Administration and Managmenet ConceptsEntity Relationship and data modeling concepts and toolsBusiness Intelligence (Microsoft SQL Server BI),ETL, OLAP, OLTP, SSIS, SSRS, SSASIntroduction to Software development and Object Oriented Programming (OOP) – C#Introduction to data drivent application developmentPython Data Analysis.Fundamentals and Challenges of Big and Unstructured. Potential in real the world.Challenges and opportunities in big dataData Modeling: Classification, linear discriminant functions, regression modelingLinear/Predictive Modeling: Correlation supervised learning, visualization, probability ApplicationsData and Business Analysis with R langauge and working with RStudio 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 workLab 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.Required Text Books & Materials:1.(for database) Abraham Silberschatz, Henry F. Korth, and S. Sudarshan, Database Systems Concepts, 5th Edition, ISBN-10: 00729588632.(for data analytic) Michael Milton, Head First Data Analysis, O’Reilly. ISBN-10: 0596153937Note: 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).Recommended References:Microsoft DocumentationPython documentationR documetnationImportant DatesRefer to the UB Academic Calendar for important dates: Day for this classMonday, Jan 26, 2017Midterm ExamMonday, March 27, 2017Last Day of ClassesMonday, April 24, 2017Final ExamMonday, May 01, 2017Final Grades DueMonday, May 03, 2017Course Requirements:Attendance, Class Participation and Current Events(news)10%Lab work20%Midterm20%Written Term Paper/Project & Oral Summary/Case Studies20%Exam (Final)30%Total100%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.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.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.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.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.GeneralClass 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. 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. 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 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.Final Course GradeLetter GradeRange (%)A94.9 – 100.0A-90 – 94.8B+87 – 89.9B83 – 86.9B-80 – 82.9C+77 – 79.9C73 – 76.9C-70 – 72.9D+67 – 69.9D63 – 66.9D-60 – 62.9FBelow 60Schedule & AssignmentsOur 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.We may have extra Sunday sessions if needed and you can come optionally if you need help.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.Lab manuals will be available in PDF for each lab for you to follow and learn from it.All PDFs material and reference study material will be uploaded to Canvas prior to first lecture.We will make project groups (3 students) to do the joint work on project and on case studies.Case studies PDF will be provided to you on canvas.Though you see only four labs but our sessions may span over two weeks for a single lab to learn effectively.You are expected and recommended to revise what you learn every week and continue on Lab work on weekly basis, following each weekly lecture.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.WeekDateTimeTopic & Assignments101/236:15pm to 8:45pmLecture workIntroduction to Course Structure (Syllabus as guidelines) and Data Science, Databases and Analytics concepts in nutshellDefining 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.Getting to know each other (Instructor and Students)Canvas website portal overview for you to get access the relevant documents and PDFs.Intro to Relational Database Management Systems (Microsoft SQL Server)Lab workOpen up Lab1-PDF manual before lecture begins and read throughLogin to the computers and look around what software’s we have.Read the FinalProject_DA.PDFStarting looking into Reference Study Material for the whole course to get motivated, moving forward.Look around on Canvas folders and see what you have got so far. 201/30Lecture workIntroduction to Structure Query Language (SQL) and its variationsRelational Database Management Systems (Microsoft SQL Server, Oracle, etc)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)Database development and administration, data backups and restores, disaster recovery and highly availability solutions, DDL, DML.302/06Lab workGetting started with Lab 1Lab 1 – Working with Database Engines, Creating and working with database using SQL and GUI (Instructor lead session)Run the sample Code provided on Canvas for this lab.Research Paper/Project Update402/13Lab 1 continued502/20Introduction to Business intelligence.Microsoft Business intelligence (SSIS, SSRS, SSAS)Introduction to Data Warehouse, OLAP, OLTP, ETL. Introduction to various BI tools from various vendors in the real world. Research Paper/Project UpdateLab 2– Working with MS BI Tools .602/27Lab 2 continued Research Paper/Project Update703/06Introduction to Programming and Software Development.Introduction to OOP, Web application, Web services, XML, SOA and Cloud computing.Introduction to C#, and Web programmingDepth Introduction to PythonData Analyis and Data Mining with PythonReview for Midterm – Refreshing what we have learned so far 803/13SPRING BREAK903/20Midterm and Midterm review afterwardsResearch Paper/Project Update1003/27Introduction to Big Data and Big Data Analytics and ProcessingIntroduction to Data retrieval and processing.Very Large Information Systems and Information RetrievalIntroduction to Data MiningIntroduction to unstructured data real world examples (Social Networking, text, audio and video, healthcare data, etc)Machine learningPython and R languageCase StudiesLab 3 – Data Analysis using Python and OOP1104/03Lab 3 continuedResearch Paper/Project Update1204/10Business Analytics (Descriptive, Predictive and Prescriptive)Data Modeling, Regression Modeling, Classification, Linear discriminate functionLinear/Predictive Modeling: Correlation, supervised segmentation, visualization, probability applications.Data Analyis using Pythong and R Language.Case Studies Lab 4 – Data Analysis using R language1304/17 Lab 4 continued and Final Exam review Research Paper/Project Update1404/24Project Presentations and paper e-copy due.1505/01 Final Exam General Policies for the CourseAcademic 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 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.Attendance:For on-campus classes, the fourth unexcused absense will result in a failure of the class.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.Assignment Content Expectations and Evaluation:The following rubric will be used for evalutation of written submissions:Grade CriteriaA ExemplaryB AdequateC PoorContentThe content was clear and useful. The writer gave specific answers relevant to the topic. The content was generally relevant, but was somewhat unclear or confusing at times.It was unclear as to how the content related to the field being considered.Critical AnalysisThe writer was thought provoking and showed strong insight in applying class materials to the cased or topic.The writer was able to reflect on the topic. A Thoughtful assessment was included.The writer did little more than restate facts and other people’s (authors’) opinions.Spelling and GrammarSpelling and grammar rules were followed. Technical writing rules were followed.Although most of the paper was well written, a more than a few grammar, spelling, and/or technical errors were present.The paper was poorly written – making it difficult to determine the writers points. Numerous grammatical and spelling errors were present. The text did not follow rules for technical writing.Overall QualityThe paper meets the requirements for the degree program for student is enrolledThe paper lacks one or a few elements that are important for the programThe paper was poorly written.Deadlines and Late Policy:<Assignments must be submitted by the deadline to receive any credit.><Late homework / project assignment submission will not be accepted.><Late homework will only be considered for half credit><Make-up exams / quizzes will not be allowed (except for prior instructor approval for a documented emergency)>Personal Devices:<The use of portable devices is prohibited. Students must turn off and stow all personal devices (laptops, phones, tablets, etc.) during class.><Laptops may be used in class.>Special Student Situations:Veterans and student service members with special circumstances or who are activated are encouraged to notify the instructor as soon as possible and to provide Activation Orders.Any students with disabilities or other special needs requiring special accommodations in this course should work with the Office of Disability Services. The Professor will cooperate with the Office of Disability Services to provide appropriate accommodations for the student. For additional details, refer to The Key to UB, “Disability Service.”AppendixesTerm paper DetailsA detailed PDF will be provided to review during first week.Group of 3 will work on a topic in field of data mining and data analytics. The following will be expected of each groupIntroduce a segment of data mining and data analyticsCompose a literature review of 2-3 pages after reading/consulting 20-25 references.Identify a room for improvement and document them in their words. -> Thinking processIdentify a problem or two and elobrate on it. -> Formulating processIdentify few tools (including out of the box) for possible usage for the paperIdentify a data set to work with.Design a data storage (SQL, Excel and CSV based)Propose a solution or an approachImplementation and testingConclusion and Future workGroup will provide bi-weekly updates and discuss with instructor any issues or hurdles so can be resolved in timely fashion.Note: Due to limited time for the semester, a student or a group will be allowed to extend the project work into the summer and grade extension will be granted (“I” grade to a complete grade) provided, he or she is not graduating and their priliminary work is satisfactory enough for the extension request. We will discuss more about this in first week.End goal: We will be working with the knowledge learned, tools learned and skill developed in the course and lab work towards a very constructive project/paper, that not only help us to earn good grade but also to develop writting and thinking skills towards a very successful academic and real world career, down the road.Possible publication for the conference and/or journal. ................
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