MS (Data Science and Artificial Intelligence) – Online
Eligibility
• Bachelor’s degree in any subject with a minimum of two courses in mathematics
• Master’s degree in any subject with a minimum of two courses in mathematics at either undergraduate (Bachelor’s) or post-graduate (Master’s) level
Admission Procedure
Candidates who meet the eligibility criteria can Apply Now in order to enrol in the program.
Fee Structure
|
Course Fee |
USD 550 / INR 45,600 (per semester) |
|
Admission Fee |
USD 200 / INR 17,600 (One time) |
|
Student Aid Fund |
USD 10 / INR 830 (per semester) |
First Semester Courses & Faculty
Data Analysis and Visualization
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)
Mr. Gaurav Gupta
CTO & Head – Platform, Zerocode Learning Bangalore
Alumnus IIM Bangalore, IIT Roorkee
Mr. Vineet Srivastava
Head – Partner Program, Zerocode Learning Bangalore,
Alumnus IIT Roorkee
Leading the development and implementation of innovative training modules, driving technology strategy and execution. With over 20 years of experience in the tech industry, including leadership roles at intel and siemens with expertise in supply chain solutions, Machine Learning, Innovation, and Automative Design Solutions. In supply chain solutions, machine learning, and automotive design. Committed to leveraging technology to enhance outcomes and foster innovation in the edtech sector
Serial entrepreneur in Deep Tech, IT, and digital Transformation technologies. Built products, IP & platforms successfully introduced to the market. Built profitable business. Functional expert in strategy, product creation, delivery, and marketing. Last corporate role with Philips Electronics.
Probability & Statistics for Machine Learning
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.
Conditional probability, Random Variables, CDF, PDF, Variance, Standard Deviation, Expectation and Moments of the Distribution, Main distributions
Prof. Swagatam Das, ISI Kolkata, India
Dr. Sunil Kumar Prajapati, Associate Professor
School of Basic Sciences (Mathematics), IIT Bhubaneswar
Swagatam das earned his B.E. in Electronics and Telecommunications Engineering, M.E. with a specialization in Control Engineering, and Ph.D.(Engineering) degrees from Jadavpur University, India, in the years 2003, 2005, and 2009, respectively. He is currently a professor at the Electronics and Communication Sciences Unit (ECSU) of the Indian Statistical Institute, Kolkata, India. He is also serving as the Professor-in-Charge of the Computer and Communication Sciences Division (CCSD) of his Institute for the term 2024 – 26. He previously held the position of Professor and Deputy Director at the Institute for Advancing Intelligence (IAI), TCG CREST, Kolkata, India, from April 01, 2023, to March 31, 2024. His research interests encompass deep learning and non-convex optimization, and he has published over 400 research articles in peer-reviewed journals and international conferences. Dr. Das is the founding Co-Editor-in-Chief of Swarm and Evolutionary Computation, an international journal by Elsevier. He has served or is currently serving as an Associate Editor for several prominent journals, including the IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Cybernetics, IEEE Transactions on Evolutionary Computation, Pattern Recognition (Elsevier), Neurocomputing (Elsevier), Information Sciences (Elsevier), IEEE Trans. on Systems, Man, and Cybernetics: Systems, among others. He is a member of the editorial board of Information Fusion (Elsevier), Progress in Artificial Intelligence (Springer), Applied Soft Computing (Elsevier), Engineering Applications of Artificial Intelligence (Elsevier), and so on. Dr. Das has received over 33,000 Google Scholar citations and an H-index of 87 to date. He has actively participated in the program committees and organizing committees of renowned international conferences such as NeurIPS, AAAI, AISTATS, ACM Multimedia, BMVC, IEEE CEC, GECCO, and more. He currently serves as an ACM Distinguished Speaker. He received the 2012 Young Engineer Award from the Indian National Academy of Engineering (INAE) and the 2015 Thomson Reuters Research Excellence India Citation Award for being the highest-cited researcher in Engineering and Computer Science in India between 2010 and 2014.
- Postdoctoral Fellow, Department of Mathematics, Ben-Gurion University of the Negev, Be’er Sheva, Israel September, 2016-June, 2017.
- Postdoctoral Fellow, Einstein Institute of Mathematics, The Hebrew University of Jerusalem, Israel, October, 2014-August,
- NBHM Postdoctoral Fellow, Stat Math Unit, Indian Statistical Institute Bangalore, Karnataka, India, July 2013-September 2014.
- Visiting Scientist, Stat Math Unit , Indian Statistical Institute Bangalore, Karnataka, India, April 2013-June 2013.
Machine Learning Techniques
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
Prof. Andries Engelbrecht, Stellenbosch University, South Africa
Prof. Andries received the Masters and PhD degrees in Computer Science from the University of Stellenbosch, South Africa, in 1994 and 1999, respectively. He is currently appointed as the Voigt Chair in Data Science in the Department of Industrial Engineering, with a joint appointment as Professor in the Computer Science Division, Stellenbosch University. Prior to his appointment at Stellenbosch University, he has been at the University of Pretoria, Department of Computer Science (1998-2018), where I was appointed as South Africa Research Chair in Artificial Intelligence (2007-2018), the head of the Department of Computer Science (2008-2017), and Director of the Institute for Big Data and Data Science (2017-2018).
In addition to a number of research articles, he has written two books, Computational Intelligence: An Introduction and Fundamentals of Computational Swarm Intelligence.
Linear Algebra for Machine Learning
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.
Dr. Ratikant Behera,IISc Bangalore, India
Dr. Behera is currently with the Department of Computational and Data Sciences Indian Institute of Science, Bangalore, India. His research interests are Scientific Machine Learning, Tensor Computations and Applications, Artificial Neural Networks, Numerical Linear Algebra, Generalized Inverses, Wavelets in Scientific Computing, and Quantum Computing. He completed his PhD from IIT Delhi, India, and post-doctoral research from Joseph Fourier University, Grenoble, France, and the University of Notre Dame, South Bend, USA.
Introduction to Artificial Intelligence
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
Dr. Seifedine Kadry,Noroff University College, Norway
Professor Seifedine Kadry has a Bachelor degree in 1999 from Lebanese University, MS degree in 2002 from Reims University (France) and EPFL (Lausanne), PhD in 2007 from Blaise Pascal University (France), HDR degree in 2017 from Rouen University. At present his research focuses on Data Science, education using technology, system prognostics, stochastic systems, and applied mathematics. He is an ABET program evaluator for computing, and ABET program evaluator for Engineering Tech. He is a Fellow of IET, Fellow of IETE, and Fellow of IACSIT. He is a distinguish speaker of IEEE Computer Society.
Course Brochure
Frequently asked Questions
Two hours daily on weekdays from 6 PM to 8 PM, and 10 hours on weekends from 9 AM to 2 PM.
Yes, the degree is fully recognized as a master’s degree, which is recognized in India as well as internationally.
The training is optional, but all the lectures will be practical-oriented, which will prepare the students for industry-ready.
The outline for the first semester course is now available on the website, and the rest will be finalized soon.
The course is structured for easy attendance by employed professionals.
Classes will consist of both recorded lectures and live interactive sessions. Recordings of the live lectures will also be available for later use. Each student’s engagement time with the recorded lectures will be monitored.
Yes, anyone from any country can join the course, as the degrees from SAU are globally recognized.
Yes, the programs at South Asian University (SAU) are recognized by the University Grants Commission (UGC) in India as well as international universities worldwide.