Program Overview
The Bachelor of Science in Data Science and Machine Learning Techniques at Grameen University is a four-year undergraduate program designed to develop highly skilled professionals in the rapidly evolving fields of data science, artificial intelligence, and machine learning. The program equips students with the knowledge, tools, and analytical skills needed to collect, process, and interpret complex data, develop predictive models, and drive decision-making in business, healthcare, finance, and technology sectors.
This program combines a strong foundation in mathematics, statistics, programming, and algorithms with hands-on experience in machine learning, AI techniques, and data-driven problem-solving to prepare graduates for both industry and advanced research opportunities.
Program Objectives
Graduates of this program will be able to:
- Develop expertise in data analysis, visualization, and machine learning models.
- Apply statistical and computational methods to solve real-world problems.
- Design, implement, and evaluate machine learning algorithms and AI solutions.
- Communicate insights from complex data effectively to technical and non-technical audiences.
- Prepare for professional careers or advanced studies in AI, data science, and related fields.
Curriculum Structure
The curriculum spans eight semesters over four years and includes a combination of theoretical courses, practical labs, and research projects.
Key components include:
- General Education Courses: foundational courses in mathematics, communication, and social sciences.
- Core Data Science Courses: statistics, probability, programming (Python, R), databases, data mining, and big data analytics.
- Machine Learning and AI Courses: supervised and unsupervised learning, deep learning, neural networks, reinforcement learning, natural language processing.
- Laboratory Practicals: hands-on experience in data analysis, model building, AI applications, and software tools.
- Elective Courses: specialized topics such as computer vision, NLP, robotics, or applied AI.
- Capstone Project / Research: independent data science or machine learning project addressing real-world problems.
The program requires approximately 130–140 credit hours for completion, combining both theoretical and applied learning.
Core Learning Areas
- Data Analysis & Statistics
Exploring descriptive and inferential statistics, exploratory data analysis, and statistical modeling. - Programming & Software Tools
Developing proficiency in Python, R, SQL, and relevant machine learning frameworks. - Machine Learning & Artificial Intelligence
Understanding supervised, unsupervised, and reinforcement learning, deep learning, neural networks, and AI algorithms. - Big Data & Cloud Computing
Techniques for handling large-scale datasets, distributed computing, and cloud-based AI solutions. - Research & Project-Based Learning
Designing experiments, modeling real-world data, and interpreting results in actionable ways.
Learning and Teaching Approach
The program uses a combination of lectures, seminars, workshops, lab sessions, and project-based learning. Students engage with real-world datasets, work on industry-oriented projects, and develop critical problem-solving and programming skills under faculty supervision.
Assessment Methods
Students are assessed through:
- Written examinations and quizzes
- Laboratory reports and coding assignments
- Project work and presentations
- Continuous assessment and capstone project evaluation
These assessments ensure mastery of both theoretical knowledge and practical data science competencies.
Career Prospects
Graduates of the BSc in Data Science and Machine Learning Techniques program will be prepared for careers in:
- Data Science and Analytics
- Artificial Intelligence and Machine Learning Engineering
- Business Intelligence and Decision Support Systems
- Healthcare Analytics and Bioinformatics
- Financial Analytics and Risk Management
- Research and Academia
The program also provides a strong foundation for postgraduate studies such as MSc or PhD in Data Science, AI, Machine Learning, or related disciplines.
Admission Requirements
To gain admission, applicants should:
- Have completed Higher Secondary Certificate (HSC) or equivalent, preferably with a background in Science, Mathematics, or Computer Studies.
- Demonstrate proficiency in mathematics, problem-solving, and analytical reasoning.
- Meet any additional admission criteria such as written tests or interviews as determined by the university.
Facilities and Academic Support
Grameen University provides resources to enhance learning and research, including:
- Modern computing laboratories with AI and ML software tools
- Access to digital libraries, journals, and cloud-based datasets
- Research mentoring and academic advising
- Industry internship and project support
These facilities ensure students develop practical, hands-on experience and are fully prepared for professional or research careers in data science and machine learning.
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Dept. Contact Info
Bachelor of Science in Data Science and Machine Learning Techniques
Faculty Consultation
- Sunday & Tuesday
- 10:00 AM - 12:00 PM
- Room 305, Academic Building (In-person)
Important Links
Join a New Event for Bachelor of Science in Data Science and Machine Learning Techniques Students
Skill Development Workshop
- Sunday & Tuesday
- 10:00 AM - 12:00 PM
- Room 305, Academic Building (In-person)