MS Data Science and Artificial Intelligence

MS(DS&AI)

MS Data Science and Artificial Intelligence (MS(DS&AI))

The Master of Science (MS) in Data Science and Artificial Intelligence program at South Asian University, Delhi, is an innovative course designed for students passionate about the emerging fields of data science and AI. This program provides a thorough understanding of data analytics, machine learning, artificial intelligence, and other allied subjects, equipping students with the essential skills to excel in the digital age. Hosted through SAU’s virtual campus, the program offers both flexibility and accessibility, making it ideally suited for learners seeking to balance their education with various other commitments. By combining theoretical knowledge with hands-on projects, students acquire the practical expertise needed to solve complex challenges and drive innovation. The curriculum is designed to align with industry demands, preparing students for a variety of roles in data science, AI development, and research. Enrol in SAU’s MS (DS and AI) program to join the next generation of tech leaders to study and innovate data-driven solutions and artificial intelligence.

Level

Master

Credit

Tuition Fee

$550 Per Semester^*

Admission Cycle

January, July

Apply Now

Eligibility

Following Eligibility are required for enrolling in this programme

B.Tech./B.E. in any branch
UG with at least two mathematics courses*
M.Sc./MS with at least two mathematics courses*
MCA

^*Students without minimum two mathematics courses must take an add-on mathematics course, “Foundation of Mathematics,” as a compulsory subject in the first semester.

Duration

Minimum Two (02) Years and Maximum four (04) Years

Fee Structure

S. No.	Fee Type	Amount	One Time/Recurring
1	Registration Fee	USD 20/ INR 1740	One Time
2	Tuition Fee	USD 550/ INR 47850	Per Semester
3	Admission Fee	USD 200/ INR 17400	One Time
4	Student Aid Fund	USD 10/ INR 870	Per Semester

Scholarship: 25% tuition fee concession will be given on SAU-VC programs to the following:

Current SAU Students
SAU Alumni
Teachers from SAARC countries

The installment policy for all virtual campus degree programmes will be similar to that of the SAU policy.

Admission Process

A candidate who meet the eligibility criteria can enroll in the programme.

Course Structure

Semester 1

Credit 4

CSE-581

Data Analysis and Visualization

Title

Data Analysis and Visualization

Code

CSE-581

Credit

Introduction

Data Collection, Data extraction and cleaning (Pandas), Handling Missing Data, Imputation techniques, Data Formatting and Transformation, Reshaping data, String operations, Data Merging, Cleaning a messy dataset, Manipulating data for analysis, Data Visualization (Matplotlib, Plotly, Seaborn), Modeling (Scikit-learn, Keras), Exploratory Data Analysis (EDA)

Evaluation Methodology

There are three components Internal Assignment(Quiz/Assignments), Mid-Term Examination and Final-Term Examination. The weightage of individual Component is as follows:

#	Component	#	Weightage(%)
1	Internal Assignment(Quiz/Assignments)	:	30 %
2	Mid-Term Examination	:	30 %
3	Final-Term Examination	:	40 %

Semester 1

Credit 4

CSE-582

Probability & Statistics for Machine Learning

Title

Probability & Statistics for Machine Learning

Code

CSE-582

Credit

Introduction

Parameter estimation, Sample mean and variance, Confidence intervals, Hypothesis testing, Regression analysis, Correlation analysis, linear Regression, Logistic Regression, Forecasting and Confusion Matrix, Modeling and Forecasting Time Series Data.

Evaluation Methodology

There are three components Internal Assignment(Quiz/Assignments), Mid-Term Examination and Final-Term Examination. The weightage of individual Component is as follows:

#	Component	#	Weightage(%)
1	Internal Assignment(Quiz/Assignments)	:	30 %
2	Mid-Term Examination	:	30 %
3	Final-Term Examination	:	40 %

Semester 1

Credit 4

CSE-583

Machine Learning techniques

Title

Machine Learning techniques

Code

CSE-583

Credit

Introduction

Linear/Logistic Regression, K-Means, SVM, PCA, K-Nearest Neighbor, Ensemble learning (bagging, boosting, stacking), Gradient Boosting, XGBoost, CatBoost, LightGBM, Dimensionality reduction (PCA, t-SNE), Semi-supervised and self-supervised learning, Interpretability in machine learning models

Evaluation Methodology

There are three components Internal Assignment(Quiz/Assignments), Mid-Term Examination and Final-Term Examination. The weightage of individual Component is as follows:

#	Component	#	Weightage(%)
1	Internal Assignment(Quiz/Assignments)	:	30 %
2	Mid-Term Examination	:	30 %
3	Final-Term Examination	:	40 %

Semester 1

Credit 4

CSE-584

Linear Algebra for Machine Learning

Title

Linear Algebra for Machine Learning

Code

CSE-584

Credit

Introduction

Study of vectors, matrices, linear transformations, Vector spaces, Eigenvalues, eigenvectors, singular value decomposition (SVD), matrix factorization, and their applications in data science and machine learning.

Evaluation Methodology

There are three components Internal Assignment(Quiz/Assignments), Mid-Term Examination and Final-Term Examination. The weightage of individual Component is as follows:

#	Component	#	Weightage(%)
1	Internal Assignment(Quiz/Assignments)	:	30 %
2	Mid-Term Examination	:	30 %
3	Final-Term Examination	:	40 %

Semester 1

Credit 4

CSE-585

Introduction to Artiﬁcial Intelligence

Title

Introduction to Artiﬁcial Intelligence

Code

CSE-585

Credit

Introduction

Knowledge Representation, Logic and Automated Reasoning, AI Planning and Scheduling, AI Perception Technologies (Computer Vision & NLP), Autonomous Systems, Introduction to Neural Networks (CNNs, RNNs, LSTMs), Transformers, Discriminative and Generative AI, Pretrained Models and Fine-Tuning, Prompt Engineering, AI Optimization Techniques, AI Inferencing, AI Ethics and Bias, Explainability and Interpretability in AI, Variants of Transformers Designed for Multimodal Data

Evaluation Methodology

There are three components Internal Assignment(Quiz/Assignments), Mid-Term Examination and Final-Term Examination. The weightage of individual Component is as follows:

#	Component	#	Weightage(%)
1	Internal Assignment(Quiz/Assignments)	:	30 %
2	Mid-Term Examination	:	30 %
3	Final-Term Examination	:	40 %

Semester 2

Credit 4

CSE-586

Data Engineering

Title

Data Engineering

Code

CSE-586

Credit

Evaluation Methodology

There are three components Internal Assignment(Quiz/Assignments), Mid-Term Examination and Final-Term Examination. The weightage of individual Component is as follows:

#	Component	#	Weightage(%)
1	Internal Assignment(Quiz/Assignments)	:	30 %
2	Mid-Term Examination	:	30 %
3	Final-Term Examination	:	40 %

Semester 2

Credit 4

CSE-587

Deep Learning

Title

Deep Learning

Code

CSE-587

Credit

Evaluation Methodology

There are three components Internal Assignment(Quiz/Assignments), Mid-Term Examination and Final-Term Examination. The weightage of individual Component is as follows:

#	Component	#	Weightage(%)
1	Internal Assignment(Quiz/Assignments)	:	30 %
2	Mid-Term Examination	:	30 %
3	Final-Term Examination	:	40 %

Semester 2

Credit 4

CSE-588

Big Data Analytics

Title

Big Data Analytics

Code

CSE-588

Credit

Evaluation Methodology

There are three components Internal Assignment(Quiz/Assignments), Mid-Term Examination and Final-Term Examination. The weightage of individual Component is as follows:

#	Component	#	Weightage(%)
1	Internal Assignment(Quiz/Assignments)	:	30 %
2	Mid-Term Examination	:	30 %
3	Final-Term Examination	:	40 %

Semester 2

Credit 4

CSE-589

Data and AI Ethics

Title

Data and AI Ethics

Code

CSE-589

Credit

Evaluation Methodology

There are three components Internal Assignment(Quiz/Assignments), Mid-Term Examination and Final-Term Examination. The weightage of individual Component is as follows:

#	Component	#	Weightage(%)
1	Internal Assignment(Quiz/Assignments)	:	30 %
2	Mid-Term Examination	:	30 %
3	Final-Term Examination	:	40 %

Semester 2

Credit 4

CSE-590

Computer Vision

Title

Computer Vision

Code

CSE-590

Credit

Evaluation Methodology

There are three components Internal Assignment(Quiz/Assignments), Mid-Term Examination and Final-Term Examination. The weightage of individual Component is as follows:

#	Component	#	Weightage(%)
1	Internal Assignment(Quiz/Assignments)	:	30 %
2	Mid-Term Examination	:	30 %
3	Final-Term Examination	:	40 %

Semester 3

Credit 4

CSE-675

Generative AI

Title

Generative AI

Code

CSE-675

Credit

Evaluation Methodology

There are three components Internal Assignment(Quiz/Assignments), Mid-Term Examination and Final-Term Examination. The weightage of individual Component is as follows:

#	Component	#	Weightage(%)
1	Internal Assignment(Quiz/Assignments)	:	30 %
2	Mid-Term Examination	:	30 %
3	Final-Term Examination	:	40 %

Semester 3

Credit 4

CSE-676

Natural Language Processing

Title

Natural Language Processing

Code

CSE-676

Credit

Evaluation Methodology

There are three components Internal Assignment(Quiz/Assignments), Mid-Term Examination and Final-Term Examination. The weightage of individual Component is as follows:

#	Component	#	Weightage(%)
1	Internal Assignment(Quiz/Assignments)	:	30 %
2	Mid-Term Examination	:	30 %
3	Final-Term Examination	:	40 %

Semester 3

Credit 4

CSE-677

Quantum Machine Learning

Title

Quantum Machine Learning

Code

CSE-677

Credit

Evaluation Methodology

There are three components Internal Assignment(Quiz/Assignments), Mid-Term Examination and Final-Term Examination. The weightage of individual Component is as follows:

#	Component	#	Weightage(%)
1	Internal Assignment(Quiz/Assignments)	:	30 %
2	Mid-Term Examination	:	30 %
3	Final-Term Examination	:	40 %

Semester 3

Credit 4

CSE-678

AI in Cybersecurity

Title

AI in Cybersecurity

Code

CSE-678

Credit

Evaluation Methodology

There are three components Internal Assignment(Quiz/Assignments), Mid-Term Examination and Final-Term Examination. The weightage of individual Component is as follows:

#	Component	#	Weightage(%)
1	Internal Assignment(Quiz/Assignments)	:	30 %
2	Mid-Term Examination	:	30 %
3	Final-Term Examination	:	40 %

Elective

Distributed Computing

AI Agents
Computational Intelligence
Cloud native AI

Reinforcement Learning

Semester 3

Credit 4

Elective-1

Title

Elective-1

Credit

Evaluation Methodology

There are three components Internal Assignment(Quiz/Assignments), Mid-Term Examination and Final-Term Examination. The weightage of individual Component is as follows:

#	Component	#	Weightage(%)
1	Internal Assignment(Quiz/Assignments)	:	30 %
2	Mid-Term Examination	:	30 %
3	Final-Term Examination	:	40 %

Elective

Distributed Computing

AI Agents
Computational Intelligence
Cloud native AI

Reinforcement Learning

Semester 4

Credit 4

Elective-2

Title

Elective-2

Credit

Evaluation Methodology

There are three components Internal Assignment(Quiz/Assignments), Mid-Term Examination and Final-Term Examination. The weightage of individual Component is as follows:

#	Component	#	Weightage(%)
1	Internal Assignment(Quiz/Assignments)	:	30 %
2	Mid-Term Examination	:	30 %
3	Final-Term Examination	:	40 %

Elective

Distributed Computing

AI Agents
Computational Intelligence
Cloud native AI

Reinforcement Learning

Semester 4

Credit 4

Elective-3

Title

Elective-3

Credit

Evaluation Methodology

There are three components Internal Assignment(Quiz/Assignments), Mid-Term Examination and Final-Term Examination. The weightage of individual Component is as follows:

#	Component	#	Weightage(%)
1	Internal Assignment(Quiz/Assignments)	:	30 %
2	Mid-Term Examination	:	30 %
3	Final-Term Examination	:	40 %

Elective

Distributed Computing

AI Agents
Computational Intelligence
Cloud native AI

Reinforcement Learning

Semester 4

Credit 12

CSE-681

Dissertation

Title	:	Dissertation
Code	:	CSE-681
Credit	:	12

Programme Coordinator

	Dr. Jagdish Chand Bansal Professor Department of Mathematics, FMS, SAU India jcbansal(at)sau.ac.in
	Dr. Bijoy Chand Chatterjee Associate Professor Department of Computer Science & Engineering, FET, SAU India bijoycc(at)sau.int

MS Data Science and Artificial Intelligence

MS(DS&AI)