MIS630 Data and Knowledge Management Course Syllabus

Transcription

1 MIS630 Data and Knowledge Management Course Syllabus I. Contact Information Professor: Joseph Morabito, Ph.D. Office: Babbio 419 Office Hours: By Appt. Phone: II. Required Course Materials Textbook: Database Concepts (6 th Edition), by David Kroenke & David Auer, Prentice Hall, 2012 Supplementary Readings, Exercises, and Assignments: All other readings, exercises, and assignments are posted to our electronic course site. III. Course Objectives and Learning Goals This course will focus on the design and management of data in the organization. Data form the basis of modern business analytics and decision making in organizations. This course will explore all aspects of data, including strategic planning, modeling and representation, semantics, quality, and other related issues. Course outcomes; at the end of the class, students will be able to: 1. Understand the role of data in the competitiveness of organizations 2. Elucidate on the various data management functions and strategies 3. Describe and elucidate on the various types of database schemas 4. Analyze functional dependencies and normalize data 5. Design relational databases 6. Build SQL queries 7. Develop and critique entity-relationship (ER) data models 8. Describe systems theory and layered models. Build information and object-oriented models 9. Elucidate advanced data modeling issues- e.g., temporal data modeling, meta-data, etc. 10. Identify and analyze data quality in a business context 11. Develop and evaluate strategic data plans; e.g., - Master data management plan - Enterprise data strategy & models 12. Understand the growing importance and issues associated with data warehousing, business intelligence and analytics, and big data.

2 IV. Assignments 1. Class Participation Homework & Discussion 30% Summary of Class-Participation-Assignments: Team homework on Research Paper due Lecture 2 Team homework on Normalization problem due Lecture 4 Team homework on ER modeling problem due Lecture 5 Team homework on Info Modeling problem due Lecture 8 Team homework on Data Quality due Lecture 10 Team homework on Strategic Data planning due Lecture Team Data Function Presentation due Lecture 6 25% Data Functions analysis on your company 3. Team Project on ER Modeling due Lecture 11 25% 4. Final Individual Project due no later than Last Lecture (#15) 20% TOTAL 100% Brief Description of Assignments (more thorough descriptions will be provided during the semester): Class Participation and Homeworks To get the most learning from this course, you must actively participate in the classroom experience. Participation first means coming to class. Participation also means actively participating in the classroom experience and completing the assigned homeworks. Other Assignments Requirements for each assignment will be presented as each becomes due.

3 V. Academic Honesty Policy Ethical Conduct The following statement is printed in the Stevens Graduate Catalog and applies to all students taking Stevens courses, on and off campus. Cheating during in-class tests or take-home examinations or homework is, of course, illegal and immoral. A Graduate Academic Evaluation Board exists to investigate academic improprieties, conduct hearings, and determine any necessary actions. The term academic impropriety is meant to include, but is not limited to, cheating on homework, during in-class or take home examinations and plagiarism. Consequences of academic impropriety are severe, ranging from receiving an F in a course, to a warning from the Dean of the Graduate School, which becomes a part of the permanent student record, to expulsion. Reference: The Graduate Student Handbook, Stevens Institute of Technology. Consistent with the above statements, all homework exercises, tests and exams that are designated as individual assignments must contain the following signed statement before they can be accepted for grading. I pledge on my honor that I have not given or received any unauthorized assistance on this assignment/examination. I further pledge that I have not copied any material from a book, article, the Internet or any other source except where I have expressly cited the source. Name (Print) Signature Date: Please note that assignments in this class may be submitted to a web-based anti-plagiarism system, for an evaluation of their originality. VI. Grading Scale Grade Score Grade Score A C A C B F <70 B B C

4 VII. Submission Requirements We expect professional, high-quality work on all assignments. Writing style, grammar, spelling, and overall presentation will be considered in determining your grades. Unless otherwise noted, all written assignments must be typed on a computer, with a 12-point font and one-inch margins. All assignments must be submitted either in person (for face-to-face classes) or as an attachment in Moodle s system (for Web Campus classes). Late Penalties: 1-2 days late: Half letter grade 3+ days late: Full letter grade Under no circumstances will an assignment be accepted after the last official day of class. Any missing assignments when the class ends will receive a 0.

5 LECTURES See Schedule for Dates (there may be more than one lecture on a given date) 1. Introduction and Data Management Functions (10) Data governance, security, architecture, development, operations, content, etc. 2. Team Presentations of Research Papers: Data Management and the Organization Approaches to managing data (locus of managing data in terms of technology and organizational design), benefits and costs of data integration, strategic IS planning 3. Database Schemas and Relational Database Design Hierarchical, network, object, relational, object-relational databases Functional, multi-value, and join dependencies, normalization 4. Abstraction and Entity-Relationship (ER) Modeling Chen & UML; logical & physical; high-level & application 5. Extended ER and Subtyping Class Exercises on ER Modeling and Relational Database Design 6. Team Data Management Functions Presentation Due 7. Systems Theory and Information Modeling Object-oriented modeling and complex semantics, specification & use-case 8. Query Languages SQL & QBE 9. Data Quality Approaches Ontological and business context 10. Additional Data Modeling Topics Meta-data, temporal data modeling, semantic modeling, etc. 11. Team ER Modeling Project (Presentation) Due 12. Strategic Data Management Data as a Strategic Asset Enterprise modeling, subject area databases (e.g., ERP), master data management, etc. 13. Data Analytics and Business Performance Introduction to business intelligence and data mining 14. Introduction to Data Warehousing Introduction to multi-dimensional data structures, big data 15. Final Term Papers Due (Individual Assignment)

6 SCHEDULE Week of Subject Assignment Due 1 Course Introduction Overview of Data Management 2 Team Homework on Research Paper Due Data Management Data Integration Info Systems Planning 3 Database Conceptual Schemas and Relational Database Design Introduction to Data Management Overall structure of course including a description of assignments Teams formed Lecture on data management functions Homework Team Research Paper Homework (Due next week). See next to last slide of Lecture 1 for requirements Data Functions Assignment on last slide of Lecture 1 (due Lecture 6) Read Kroenke Chapter 1 Research paper presentation due. Deliverable is PowerPoint slides with notes (Assume a 30-minute presentation) Select one of the following papers: 1. Goodhue Managing the Data Resource 2. Goodhue The Impact of Data Integration 3. Earl Strategic Information Systems Planning 4. Sen Data Warehouse Process Maturity Class lecture/discussion on data management, data integration, and information system planning. Tutorial on Database Read Kroenke Chapter 2 Team Homework on last slides of Lecture 3: Normalization (Due Next Week)

7 Week of Subject Assignment Due 4 ER Modeling Database Design Team Homework Due on Normalization Tutorial on Abstraction and Data Modeling (ER) Read Kroenke Chapters 4 and 5 Team Homework on Normalization Problem Due Class discussion of normalization assignment Team Problem Homework on ER modeling on last 2 slides of Lecture 4. Due next week 5 Team Homework Due on ER modeling Class Exercises on ER Modeling 6 Team Presentation on Data Functions Extended ER - Subtyping and Sample Data Patterns Team presentation of ER homework Additional class exercises on ER modeling and relational database design Kroenke Chapter 5 Requirements for Team Project on ER Modeling (due Lecture 11) Each team will present for 30 minutes (see Lecture 1) 7 Information Modeling Object-Oriented 8 SQL Team Homework Due on Info Modeling 9 Data Quality Information Continuum Systems Theory and Information Modeling Read Design by Contract by Bertrand Meyer Homework on Info Modeling due next week Query Languages (SQL) Team Homework on Information Modeling Due Kroenke Chapters 3, 3A Class exercises on SQL Data Quality Guest Lecturer Read paper by Wand Read paper by Strong Read paper The Architectural Continuum and an Introduction to Knowledge Binding by Morabito, Sack & Bhate (IEEE 2000) Homework on Data Quality due next week

8 Week of Subject Assignment Due 10 Team Homework on Data Quality or Info Continuum Additional Data Management Topics ER, Temporal, Semantic 11 Team Project on Data Modeling (ER) Additional Data Modeling Topics Requirements for Final Term Paper (Individual Assignment) Team Presentation on ER Modeling (30 Minutes) 12 Data as a Strategic Asset Additional Data Management Topics (Continued) Strategic Data Management Enterprise Models Temporal Data Meta-data Master Data Management Streaming Data 13 Data Analytics Data Analytics and Business Performance Introduction to Business Intelligence and Data Mining Kroenke Chapter 8 Review of Final Project Requirements 14 Data Warehouse Big Data Data Warehousing and Multi-dimensional Data Modeling Introduction to Big Data 15 Final Project Due (Individual Assignment Term Paper) Final Individual Project (Term Paper)

9 TOPIC LIST Strategic data planning Data governance Enterprise data integration Data management and the organization case studies - Case study: Data management approaches - Case study: The costs and benefits of data integration Data design for transaction processing vs. decision support - OLTP vs. OLAP Data management functions (10 functions) - Strategy, governance, architecture, development, security, services, etc. Abstraction and modeling - Types of abstraction and their application - Conceptual, logical, and physical design - Enterprise, subject-area, and application models Data and information modeling - Conceptual entity-relationship (ER) modeling - Object-oriented - Use-case Metadata development and application - Modeling metadata - Enterprise repositories vs. functional repositories Database schemas (conceptual schema) - Hierarchical - Network - Relational - Object-oriented - Object-relational Database design - Functional dependencies and normalization - Logical and physical database design Query languages: SQL, DDL, QBE, transaction and query processing Data quality approaches and techniques - Dimensions of data quality: intrinsic, accessibility, contextual, representational - Role of aggregation and timing in data quality Master data and reference data management - Customer data - Product data Data, analytics, and business performance - Competing on intelligence with internal processes - Competing on intelligence with external processes Introduction to data warehousing, OLAP, and data mining - Exploration of data warehouse and data mining

10 - Concept, governance, and architecture of data warehouse, marts, MDDB, mining, OLAP Strategic data policies and guidelines - Enterprise data and models - Governance - Markets, customers, and competitors - Leadership - Analysts and knowledge worker skills and training - Communities of analysts Case studies and modeling projects throughout course