Instructor: Dr. Liping Liu, 360 CBA Building, +5947, liping@uakron.edu
Credits: 3 hours
Text Books:
- Carlos Coronel and Steven Morris. Database Systems: Design, Implementation, & Management 13th Edition Cengage Learning, 2018. (ISBN: 978-1337627900)
- Subhashini Chellappan and Dharanitharan Ganesan, MongoDB Recipes: With Data Modeling and Query Building Strategies, APres, 2019, (ISBN: 1484248902)
- Liping Liu, Database Design and Management using Tools, available on ecourse.org
Time and Location: Mondays and Wednesdays: 3:30-4:45 PM; August 26-December 10, 2024. Regular Classroom: CBA 176 (Computer Lab).
Office Hours: 1:30-3:30 PM Mondays and Wednesdays (No appointments are necessary).
Course Description: This is an introductory course to database design and management. It covers the issues of database management, Data Modeling, Relational Database Theory, Structured Query Language (SQL), and No-SQL databases. Optional topics may include data warehouse design, PL/SQL programming, MS Access Programming, or programming database using C#, Java, Visual Basic, or ASP.NET. Pre-requisites: 6300:250 and 48 completed credit hours
Philosophy: This course aims at conceptual understanding and logic exercising through a gentle introduction to relational database theory and a careful coverage of Structured Query Language. Tools are used to enhance the learning of concepts and logics. In addition, the familiarity with the tools will make the students more marketable.
Course Objectives: Upon satisfactory completion of this course, a student should be able to
- Understand database concepts, database system components, and systems development processes and skills.
- Apply the Entity-Relationship modeling technique, and the concepts of data-integrity and normal forms to design departmental level database systems
- Implement a database design using Microsoft Access
- Perform intermediate database administration tasks using SQL
Weekly Schedule:
- Week 1: Database and Data Modeling Concepts and Oracle Data Modeler
- Week 2: Labor Day break (no class)
- Week 3: Relational Model and Forward Engineering
- Week 4: Advanced Data Modeling: Recursive Relationships, Gerunds, Weak Entities, Super-Sub Entities
- Week 5: Exam I
- Week 6: SQL: Database Implementation in Oracle using DDL
- Week 7: SQL: Simple Queries, Multiple Table Queries, Using Criteria, Sorting
- Week 8: SQL: Using expressions and Group Functions and sub-queries
- Week 9: SQL: Advanced Queries
- Week 10: Exam II
- Week 11: Normalization for Relational Model: 1-3rd Normal Forms
- Week 12: No-SQL Database: JSON
- Week 13: No-SQL Database: Basic MongoDB Queries
- Week 14: No-SQL Database: Advanced MongoDB Queries
- Week 15: Final Exam (Dec 19-13, 2024)
Exams: This course will have three major exams as scheduled above. Each exam includes both hands-on and written problems.
Assignments: Homework is assigned once a week for 12 weeks; each consists of conceptual questions and hands-on projects classified into three grading categories: correctness, closeness, and completeness. The correctness problems will be graded by ecourse.org, and closeness questions are graded and/or commented by instructors. Students will earn points automatically for each completeness question if it is finished (it has to be deemed complete). Assignments are due at the beginning of classes meetings on Mondays (except for holidays). No late homework will be graded. Please show your work in a neat and orderly fashion. Write or type your work on one side and in every other line. Use standard size paper (8 1/2'' by 11''). Do not use spiral notebook paper.
Attendance: Attendance is MUST and will be 10% of your final grade. Attendance will be managed by ecourse.org. The formula for computing your attendance grade is non-linear. It will take one point off for the first absence, 2 points off the second, 3 points off the third, and 4 points off the fourth. If you missed the equivalent of three-week classes, you fail the course automatically. Under special situations, you can take some classes online with the following guidelines:
- You must obtain permission from the instructor at least one day ahead of each online session
- Follow the lectures or recordings to perform all in-class hands-on exercises and take notes. Within one day from the class submit your notes and the finished exercises to ecourse.org as Proof of Attendance.
- All weekly assignments are due at the same time as in-person classes. All exams must be onsite.
Quizzes: I will use quizzes regularly to check your completion or preparation of assignments
Makeup: Each student with appropriate excuses may have at most one chance to makeup homework or quiz. Note that it is your privilege but not your right to have this special favor. Also, all makeups must be completed within one week of due date and before answer key is released.
Grades: Your final grades will be calculated by the following formulas:
35% (HW) + 55% (Tests) + 10% (Attendance)
A = 93-100%; A– = 90-92%; B+ = 87-89%; B = 83-86%; B– = 80-82%; C+ = 77-79%; C = 73-76%; C– =70-72%; D = 60-69%; F = 59% and less
Misconduct: Academic misconduct by a student shall include, but not limited to: disruption of classes, giving and receiving unauthorized aid on exams or in the preparation of assignments, unauthorized removal of materials from the library, or knowingly misrepresenting the source of any academic work. Academic misconduct by an instructor shall include, but not limited to: grading student work by criteria other than academic performance or repeated and willful neglect in the discharge of duly assigned academic duties. Convicted violations may result in grade penalties, besides the school official ones, such as increased scrutiny of future submissions, reduced benefits of curving, if any, and/or the reduction of overall grade.
On Collaboration: All for-credit assignments, except for those designated as group projects, must be done independently, and collaboration in providing or asking for answers to those assignments constitutes cheating.
On AI Tools: In this class, I allow students to use AI tools to help their learning. However, submitting AI generated work for credits is a violation of academic code. If a submitted work is suspected to be AI generated, the student will be asked to reproduce the submitted work in front of the instructor.
Looking for additional help? Students looking for additional assistance outside of the classroom are advised to consider working with a peer tutor through Knack. The University of Akron CBA has partnered with Knack to provide students with access to verified peer tutors who have previously aced this course. To view available tutors, visit uakron.joinknack.com and sign in with your student account. At the same time, if you are doing well in this class, please go to uakron.joinknack.com where you can create a verified tutoring profile and begin helping other students.
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