Curriculum
In order complete the Bachelor Program in Computer Science at TTU, students need to accumulate at least 126 credits (not including credits for national defense and MOET courses: 22 credits)
- TTU core courses: 27 credits
- SoE core courses: 21 credits
- CS major courses: 15 credits
- Courses required from concentration area: 15 credits
- Electives (including internship, independent study, and seminar): 48 credits. Students are expected to earn at least 12 credits not from SoE.
TTU core courses
Provided fundamental knowledge towards liberal arts education before students take major courses at TTU. Students need to accumulate 27 credits as follows:
| No. | Code | Course Name | Credits | |
| Theory | Practice | |||
| 1 | HUM101 | Writing and Ideas | 3 | |
| 2 | HUM102 | Culture and Literature | 3 | |
| 3 | HIS101 | Civilizations | 3 | |
| 4 | HIS102 | Modern times | 3 | |
| 5 | MATH101 | Calculus I | 3 | |
| 6 | ECON101 | Microeconomics | 3 | |
| 7 | ECON102 | Macroeconomics | 3 | |
| 8 | MGT101 | Introduction to Management | 3 | |
| 9 | MGT102 | Leadership and Communications | 3 | |
SoE core courses
Provide knowledge of mathematics and natural sciences to set up solid foundations for studying specialized courses in CS major later (21 credits totally)
| No. | Code | Course Name | Credits | |
| Theory | Practice | |||
| 1 | MATH201 | Calculus II | 3 | |
| 2 | MATH110 | Linear Algebra | 3 | |
| 3 | PHYS101 | Introductory Mechanics | 2 | 1 |
| 4 | PHYS110 | Introductory Electricity and Magnetism | 2 | 1 |
| 5 | CS111 | Introduction to Computer Science | 2 | 1 |
| 6 | STA206 | Probability & Statistics in Engineering | 3 | |
| 7 | CPS201 | Computational Methods in Engineering | 3 | |
CS core courses
Provide basic knowledge needed for CS major including: algorithms, discrete structures, computer organization, operating systems, and programming language.
| No. | Code | Course Name | Credits | |
| Theory | Practice | |||
| 1 | CS201 | Data Structure and Algorithms | 3 | |
| 2 | CS202 | Discrete Mathematics for CS | 3 | |
| 3 | CS203 | Computer Organization | 3 | |
| 4 | CS204 | Design & Analysis of Algorithms | 3 | |
| 5 | CS205 | Introduction to Operating Systems | 3 | |
CS major courses
Related to storing, managing, and buiding models for data mining.
| No. | Code | Course Name | Credits | |
| Theory | Practice | |||
| 1 | CS311 | Introduction to Database | 2 | 1 |
| 2 | CS331 | Introduction to Data Mining | 2 | 1 |
| 3 | CS441 | Data Visualization | 2 | 1 |
| 4 | CS432 | Intro. to Machine Learning | 2 | 1 |
| 5 | CS411 | Big Data & Cloud Computing | 2 | 1 |
Elective courses for this concentration include:
- STA301 – Bayesian statistics
- STA302 – Probability & Stochastic Processes
- CS412 – Information Retrieval and Web Search
- CS413 – Data Preprocessing/cleansing
- CS414 – Data pipeline/project
- CS432 – Advanced Data Mining
- CS433 – Applied Machine Learning
- CS442 – Practical statistical learning
- CS450 – Data science topics
- MATH203 – Calculus III
- CS481 – Internship
Towards the application of artificial intelligence and machine learning to solve many problems in different areas of life.
| No. | Code | Course Name | Credits | |
| Theory | Practice | |||
| 1 | CS330 | Introduction to AI | 2 | 1 |
| 2 | CS332 | Intro. to Machine Learning | 2 | 1 |
| 3 | CS433 | Applied Machine Learning | 2 | 1 |
| 4 | CS434 | Neural network & Deep Learning | 2 | 1 |
| 5 | CS301 | Bayesian statistics | 2 | 1 |
Elective courses for this concentration include:
- CS333 – Intro. to Computer Vision
- CS411 – Big Data & Cloud Computing
- CS435 – Practical Deep learning in Natural Language Processing
- CS436 – Practical Deep learning in Computer Vision
- CS437 – Pattern Recognition
- CS441 – Model evaluation
- CS442 – Practical statistical learning
- MATH203 – Calculus III
- CS481 – Internship
Related to design and implementation of software system.
| No | Code | Course Name | Credits | |
| Theory | Practice | |||
| 1 | CS301 | Software Design and Implementation | 2 | 1 |
| 2 | CS311 | Introduction to Database | 2 | 1 |
| 3 | CS401 | Distributed Systems | 2 | 1 |
| 4 | CS440 | Computer Network | 2 | 1 |
| 5 | CS332 | Intro. to Machine Learning | 2 | 1 |
Elective courses for this concentration include:
- CS333 – Intro. to Computer Vision
- CS334 – Intro. to Natural Language Processing
- CS302 – Web Application Development
- CS303- Mobile Application Development
- CS304 – IoT Application Development
- CS411 – Big Data & Cloud Computing
- CS408 – Software Project
- CS481 – Internship
Electives
Besides required courses above according to students’ concentration area, students are free to select more courses to accumulate at least 126 credits and have to make sure that there are 36 credits earned from other schools (not School of Engineering). TTU core courses taught by other schools (8 out of 9 TTU core courses) can be counted in this category of 36 credits. Therefore, SoE students need to earn 12 more credits from other schools.
The rest of elective credits should be earned at SoE and distributed as follows: internship (6 credits), advanced mathematics (3-6 credits), CS ( 9-15 credits), and EE (9-12 credits).