BSc in Data Science and Machine Learning — study plan
- Credit hours
- 135
- Levels
- 4
- Courses
- 62
- Degree
- bachelor
The BSc in Data Science and Machine Learning study plan at University of Nizwa is an accredited bachelor programme spanning about 2 years (4 academic levels), requiring 135 credit hours across 62 courses of required and elective study. The table below lists the courses and their credit hours for each level.
University Requirements 17 credits
| Code | Course | Credits |
|---|---|---|
| ARAB100 | Arabic Language I | 2 |
| ARAB101 | Arabic Language II | 2 |
| COMP101 | Computer Skills | 2 |
| ENGL150 | English Language I | 2 |
| ENGL152 | English Language II | 2 |
| CARC300 | Career Counseling | 1 |
| MNGT100 | Introduction to Entrepreneurship | 3 |
| HIST150 | Islamic Civilization | 3 |
College Requirements 21 credits
| Code | Course | Credits |
|---|---|---|
| MATH116 | Pre-Calculus | 4 |
| MATH211 | Calculus I | 4 |
| MATH145 | Linear Algebra | 3 |
| MATH212 | Calculus II | 3 |
| STAT101 | Introduction to Statistics | 4 |
| STAT210 | Principles of Probability | 3 |
Major Requirements 79 credits
| Code | Course | Credits |
|---|---|---|
| COMP151 | Introduction to Algorithms | 4 |
| COMP152 | Structured Programming | 3 |
| COMP222 | Object Oriented Programming | 3 |
| COMP244 | Database Concepts and Applications | 3 |
| COMP255 | Data Structures | 3 |
| COMP270 | Web Development | 3 |
| DSML101 | Data Science Basics | 3 |
| DSML111 | Data Wrangling | 3 |
| DSML200 | Information Visualization | 3 |
| DSML211 | Tools and Algorithms for Data Science | 3 |
| DSML212 | Data Analytics | 3 |
| DSML222 | Advanced Data Science Programming | 3 |
| DSML300 | Practicing Machine Learning and AI | 3 |
| DSML305 | Graph and Social Network Analysis | 3 |
| COMP450 | Digital Image Processing | 3 |
| DSML344 | Programming Models for Big Data | 3 |
| DSML351 | Boosting Algorithms and Ensemble Learning | 3 |
| DSML355 | Time Series Analysis in Data Science | 3 |
| DSML405 | Deep Learning and Graphical Models | 3 |
| DSML446 | Advanced Seminar in Data Science and Machine Learning | 1 |
| DSML494 | Data Science & Society: Ethical, Legal, Social Issues | 2 |
| DSML498 | Internship in Data Science and Machine Learning | 12 |
| DSML499 | Graduation Project in Data Science and Machine Learning | 6 |
Program Electives 75 credits
| Code | Course | Credits |
|---|---|---|
| STAT212 | Sampling Techniques Elective | 3 |
| STAT266/L | Computational Techniques in Statistics Elective | 3 |
| STAT320 | Mathematical Statistics Elective | 3 |
| STAT339/L | Regression Analysis Elective | 3 |
| COMP350 | Numerical Methods for Computing Elective | 3 |
| DSML352 | Data Science Through Statistical Reasoning and Computation Elective | 3 |
| DSML370 | Business Intelligence Elective | 3 |
| STAT387 | Stochastic Processes Elective | 3 |
| DSML410 | Large Language Models Elective | 3 |
| STAT420 | Operation Research I Elective | 3 |
| STAT340 | Probability Models in Decisions Making Elective | 3 |
| STAT351/L | Simulation Elective | 3 |
| DSML420 | Statistical Speech and Language Processing Elective | 3 |
| STAT428 | Quality and Reliability Elective | 3 |
| STAT442 | Operations Research II Elective | 3 |
| STAT460 | Information Theory Elective | 3 |
| DSML470 | Topics in Computer Vision Elective | 3 |
| DSML472 | Video Analytics and Action Recognition Elective | 3 |
| DSML473 | Pattern Recognition Elective | 3 |
| DSML477 | Privacy and Security for Data Sciences Elective | 3 |
| DSML480 | Representation and Generative Learning Elective | 3 |
| DSML485 | Reinforcement Learning Elective | 3 |
| DSML495 | Advanced Topics in Machine Learning/AI Elective | 3 |
| DSML496 | Advanced Topics in Data Science Elective | 3 |
| DSML497 | Emerging trends in Machine Learning/AI Note: This list is not exhaustive as the section may recommend any relevant course from Information Systems or Computer E Elective | 3 |
This plan is transcribed from the university's official source. Always confirm the plan approved for your own cohort before registering. Official source
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Frequently asked questions about BSc in Data Science and Machine Learning
How many credit hours is BSc in Data Science and Machine Learning at University of Nizwa?
BSc in Data Science and Machine Learning at University of Nizwa requires completing 135 credit hours to graduate, spread across 62 courses.
How many years is BSc in Data Science and Machine Learning at University of Nizwa?
The study plan for BSc in Data Science and Machine Learning spans about 2 years (4 levels/semesters) under the approved plan.
What degree does BSc in Data Science and Machine Learning award?
BSc in Data Science and Machine Learning at University of Nizwa awards a Bachelor's degree after completing 135 credit hours.