DASC 5333 Database Systems for Data Science
CSCI 4333 Design of Database Systems
Spring 2024
General Information and Course Policies

1. General Information

23422 CSCI 4333.1 Design Of Database Systems MW 1:00-2:20 Delta 241
23426 CSCI 4333.2 Design Of Database Systems MW 4:00-5:20 Delta 242
23421 DASC 5333.1 Database Systems for Data Science T 1:00-3:50 Delta 242

1.1 Instructor

Dr. Kwok-Bun Yue, Professor of Computer Science and Computer Information Systems, Chair, Data Science
Delta 163, 281-283-3864, yue at uhcl.edu; URL: http://dcm.uhcl.edu/yue/

My regular office hour will be held on 1/16/2024 to 4/29/2024: MW 2:20PM to 3:50PM, 5:20-5:50pm and Tuesday 3:50-4:10PM. Office hours will be conducted in person (Delta 163) and upon request, via Zoom meeting: 616 099 762. You can schedule a meeting with me outside my office hours by sending an email to me: yue @ uhcl dot edu. You are encouraged to communicate your questions with me through email. I usually respond quick.

1.2 Teaching Assistant

1.2.1 CSCI 4333.1 and CSCI 4333.2

Evan Raju

For regular correspondence with the TA, send it to UHCL Email Id: rajue4545@uhcl.edu. Set up the UHCL spam filter server for your UHCL account to accept this email address as an approved sender. Otherwise, your email may be quarantined by the spam filter server. If you want me to be aware of any particular communications with the TA, you may copy the email to me.

Tentative TA Office hours:

Monday: 10am - 1pm, 5:30pm - 6:30pm
Tuesday: 10am - 1pm
Wednesday: 10am - 1pm, 5:30pm - 6:30pm
Thursday: 1pm - 4pm

Evan will be stationed in the Delta Lab during her office hours. You may also request Zoom for TA: https://uhcl.zoom.us/my/evanraju1997

1.2.2 DASC 5333

Pavan Kumar Kodavali

For regular correspondence with the TA, send it to UHCL Email Id: kodavali@uhcl.edu. Set up the UHCL spam filter server for your UHCL account to accept this email address as an approved sender. Otherwise, your email may be quarantined by the spam filter server. If you want me to be aware of any particular communications with the TA, you may copy the email to me.

Tentative TA Office hours:

Monday: 9:30 AM to 12:30 PM (3 hours) and 3:00 PM to 6:00 PM (3 hours)
Tuesday: 10:00 AM to 1:00 PM (3 hours)
Thursday: 10:00 AM to 1:00 PM (3 hours) and 4:00 PM to 6:00 PM (2 hours)

Pavan will be stationed in the Delta Lab during her office hours. You may also request Zoom for TA: https://uhcl.zoom.us/my/pavankodavali

1.3 Laboratory Administrations

You may address account and software problems of the DCM server to the systems administrator, Ms. Krishani Abeysekera. and her assistants. Always copy your email to me.

1.4 Other Useful Information

• UHCL emergency hot line (to check weather related closing, for example): 281-283-2221.

1.5 Textbooks (Recommended, Optional)

Ricardo., Katherine, & Urban, Susan (2015) Databases Illuminated, 3rd Edition, Jones & Bartlett, Mississauga, Ontario, Canada.

1.6 Course Description

CSCI 4333:

From Catalog: Prerequisite: CSCI 2315. Design of database systems, data description and manipulation languages, data models, entity-relationship model, relational model, SQL, relational algebra, normalization theory, DBMS, Internet, data base design, data flow diagrams and implementation of data base systems. Laboratory instruction.

DASC 5333:

From Catalog: Design of database systems, data definition and manipulation languages, data models, entity-relationship model, relational model, SQL, relational algebra, normalization theory, DBMS, Internet, database implementation. Focus on applying DB theory and practice to support data science applications. Laboratory instruction. Prerequisites: DASC 5032 or equivalence

1.7 Student Learning Outcomes (SLO)

CSCI 4333:

After completing the course, the students are expected to be able to

1. Describe the stages of database design, various database architectures and data models.
2. Explain the concepts of the entity-relationship model, and the relational model.
3. Explain the theoretical background of relational database, including relational algebra and relational calculus.
4. Implement relational database systems using DBMS, SQL, embedded SQL, including both data definition and manipulation languages
5. Explain the importance of normalization of databases, and convert a given relational database into different normal forms.

DASC 5333:

After completing the course, the students are expected to be able to

1. Describe the fundamental concepts of database systems.
2. Apply simple data modeling techniques using the UML class diagram and the relational model.
3. Explain the features and theory of the relational model.
4. Implement relational database systems for data science applications using relational DBMS, SQL, and embedded SQL with Python.
5. Explain the importance of normalization of relational databases, and convert a given relational database to appropriate normal forms.
6. Explain the concepts of NoSQL databases and implement simple NoSQL database solutions.

1.8 Prerequisites

The following courses or their equivalent are required:

• DASC 5032 Data Structures for Data Science or CSCI 2315 Data Structures

Languages: The course uses SQL, Python and (MongoDB Query Language (MQL) with Javascript, and/or Cypher (for Neo4j)). No prior SQL, MQL or Cypher language knowledge is assumed. Students are expected to know an object-oriented language, such as Python, Java, C# or C++. Proficiency in Python is important in data science in general, and this course in particular.

1.9 Course Format

Traditional lectures, homework and programming assignments.

2. Course Policies and Guidelines

Please see: http://dcm.uhcl.edu/yue/course_policy.html

3. Grading Policy

Grades will be assigned based solely on homework and examination scores. No other factors will be considered. In particular, students have requested me to reconsider their grades using the following reasons in the past:

1. Expected a higher grade
2. Good course participation
3. Good improvement during the semester; better final grades than mid-term grades
4. Have put in extra efforts
5. Need to avoid probation
6. Financial needs
7. Loss of scholarship
8. Loss of job opportunity
9. Loss of practical training opportunity
10. Avoid probation; avoid suspension
11. Need to graduate
12. Company relocation
13. Immigration status needs
14. Family needs
15. Sickness during the semester
16. and many others.

These requests had all been declined politely but firmly in the past.

There will also be no 'special project' that you can work on to improve your grades after the final examination. Anything I offer to one student will be offered to the entire class.

The total score is computed using the following percentages:

Homework: 30%
Mid-term Exam: 30%
Final Exam: 40%

Last Day to Drop/Withdraw: April 9, 2024 (Tuesday)

Grade Assignment Table