Madan Bhandari University of Science and Technology

Chitlang, Thaha Municipality Ward 9, Bagmati Province, Nepal

Curriculum
Master of Applied Science in
Data Science

October 2023
1. Program Description
The two-year Master of Applied Science in Data Science program is a research-based graduate
program that aims to provide students with advanced knowledge and research skills in the field of
data science. In this program, students will embark on a journey to explore data science, from
foundational concepts to cutting-edge research. Through a blend of core courses, electives, and
research, students will engage in deep learning and application of data science principles,
culminating in the completion of an original research thesis. This program is structured to nurture
critical thinking, problem-solving, and ethical research practices in data science.
1.1. Aims:
1. Knowledge Advancement: To provide students with a comprehensive understanding of
data science principles, methodologies and emerging trends, which enables them to become
experts in the field.
2. Research Excellence: To foster an innovative and research-oriented culture that enables
students to carry out independent studies and contribute to the data science field.
3. Interdisciplinary Learning: Through multidisciplinary study and collaboration, an
integrated perspective of data science's applications and impact on society is established.
4. Professional and Academic Development: To prepare students for both research and
industry roles in data science, equipping them with expertise in diverse career paths of data
science.
1.2. Objectives:
1. Implement machine learning algorithms to solve real-world data science problems.
2. Evaluate and compare the performance of machine learning models and choose
appropriate techniques for specific tasks.
3. Conduct advanced statistical analysis to derive insights from data.
4. Critically assess and apply ethical guidelines and research methods to ensure the validity
of data science research.
5. Create novel research contributions in the field of data science through the Master's
thesis.

2. Eligibility for Admission

• 4-year Bachelor’s degree in science/engineering/technology or other relevant fields from

recognized universities with CGPA of 2.75 out of 4.0 (or international equivalent)

3. Courses
A. Core Courses

S.N. Course code Course title Credit

 1 DS-CR-501 Programming for Data Science 2
2 DS-CR-502 Data Analytics and Visualization 3
3 DS-CR-503 Machine Learning for Data Science 3
4 DS-CR-504 Research Methods for Data Science 1
5 DS-CR-550 Data Engineering and Architecture 2
6 DS-CR-551 Deep Learning 3

B. Non-Credit Compulsory Courses

S.N. Course code Course title Credit

1 DS-NC-505 Development Policy 0

2 DS-NC-552 Entrepreneurship for Data Science 0

C. Technical Elective Courses

S.N. Course Course Title Credit

Code
1 DS-EL-561 Generative AI and Applications 3
2 DS-EL-562 Text Mining and Information Retrieval 3
3 DS-EL-563 Human-Computer Interaction 3
4 DS-EL-564 AI in IoT 3
5 DS-EL-565 AI in Agriculture 3
6 DS-EL-566 AI in Climate 3
7 DS-EL-567 AI in Tourism 3
8 DS-EL-568 Social Network Analysis 3
9 DS-EL-569 Healthcare Analysis 3
4. Course Structure
Duration of the course: 2 years
Semester I Semester II
Course Course Title Credit Course Course Title Credit
Code Code
DS-CR- Programming for 2 DS-CR- Data Engineering and 2
501 Data Science 550 Architecture
DS-CR- Data Analytics 3 DS-CR- Deep Learning 3
502 and Visualization 551
DS-CR- Machine Learning 3 DS-EL- Elective I ( one course 3
503 for Data Science 561~569 from the list related to
thesis)
DS-CR- Research 1 DS-EL- Elective II ( one course 3
504 Methods for Data 561~569 from the list related to
Science thesis)
DS-NC- Development 0 DS-NC- Entrepreneurship for 0
505 Policy 552 Data Science
DS-TH- Thesis
699

Semester III Semester IV

Course Course Title Credit Course Course Title Credit
Code Code
DS-TH- Thesis DS-TH- Thesis
699 699

Total credit for Thesis = 30 credit

Total credit for Master in Applied Sciences = 50 credit (14 credit core course + 6 credit
Technical elective + 30 credit Thesis)
5. Course Description
Cours Course Title Descriptions
e code

DS- Programming ● Introduce the fundamental concepts of programming with a

CR- for data science focus on data science
501 ● Learn the basics of coding, data structures, and data
manipulation to prepare them for more advanced data science
coursework.
● Create code to solve practical data science problems through
hands-on assignments.

DS- Data Analytics ● Explores the fundamental principles and practices of data
CR- and analytics and visualization.
502 Visualization ● Learn how to collect, clean, analyze, and visualize data to
extract valuable insights and communicate their findings
effectively.
● Descriptive and inferential statistical analysis.
● Study Data mining algorithms for Regression, Time Series
Analysis, and Classification Data
● Each student or team selects a dataset, conducts an analysis,
and creates a data visualization project.
● Project presentations and peer evaluations.

DS- Data ● Gain an in-depth understanding of data modelling, data

CR- Engineering storage, data processing, and the design of scalable data
550 and architectures.
Architecture ● Break down data engineering problems and architectural
designs into their constituent components.
● Generate original data engineering and architectural designs
for specialized data ecosystems.
● Construct innovative solutions to complex data engineering
and architectural challenges.

DS- Machine ● Explores the theory and practice of statistical machine

CR- Learning for learning.
503 Data Science ● Students will gain a deep understanding of various machine
learning algorithms, their statistical foundations, and practical
applications.
● Each student or team selects a real-world dataset and applies
machine learning techniques to solve a problem.
● Project presentations and peer evaluations.
DS- Deep Learning ● Explores the theory and practice of deep learning, a subset of
CR- machine learning that focuses on neural networks with multiple
551 layers.
● Students will gain expertise in building, training, and applying
deep learning models to various tasks, including image
recognition, natural language processing
● Each student or team selects a real-world dataset and applies
deep learning techniques to solve a problem.
● Project presentations and peer evaluations.

DS- Research ● Learn to apply research methods such as

CR- Methods for theoretical/empirical/exploratory research, modelling in the
504 Data Science context of data science.
● Describe various data collection methods, both quantitative and
qualitative.
● Interpret statistical and computational techniques used in data
analysis for research.
● Generate original research proposals and hypotheses in the data
science domain.
● Develop strong critical thinking skills, the ability to design and
conduct data-driven research, and the capacity to communicate
their findings effectively.

DS- Thesis ● Independent research under the guidance of a faculty advisor.

TH- ● Original research contribution in the field of data science.
699