MS in Data Science and AI

• Understand how to use Python for Data Science and the importance of Python APIs. Learn how to create APIs with Python.
• Deep-dive into the nitty-gritty of Data Manipulation and Data Handling in Python using NumPy and Pandas.
• Know the importance of Data Preprocessing and Visualization and their Python implementation.

• Re-visit Linear Algebra and Advanced Statistics for machine learning.
• Deep-dive into Descriptive Statistics, Inferential Statistics, and Probability.

• Introduction to machine learning, why it is essential, and how it works.
• Understand regression, classification, and clustering algorithms. Also, learn when to use which algorithm.

• Understand what linear and logistic regression is and the difference between them.
• Learn and implement Decision Tree and Random Forest algorithms, and understand when to use decision tree vs random forest.
• Acquire hands-on skills in Support Vector Machine and Gradient Descent.
• Learn what K-Nearest Neighbors and Time Series Forecasting is.

• Master the fundamentals of K-means clustering along with Dimensionality reduction. Learn linear discriminant analysis and principal component analysis.
• Understand the importance and usage of Performance Metrics and Classification Reports.
• Learn how to evaluate machine learning models using the Confusion matrix.

• Learn the basics of Artificial Intelligence and Deep Learning. Get a deeper understanding of neural networks and their different types.
• Understand the internal workings of Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), along with various real-time scenarios.

• Explore the fundamentals of LSTM and its advantages over RNN. Learn to make them suitable for complex time series data analysis and sequence-based tasks.
• Deep-dive into the mechanics of Transformer architecture, learn how they have improved traditional NLP techniques, and why they are better at understanding context.
• Learn what BERT is and how it can understand the deep contextual meaning of sentences. Learn its application in text classification, sentiment analysis, and question answering.
• Understand GPT models and the importance of autoregressive learning. Learn how GPT models are trained on vast datasets and how to fine-tune them for different creative applications.
• Learn the evolution of large language models (LLMs) and their architecture, to perform complex language understanding and generation tasks.

• Use ETL processes to make data usable, along with data manipulation and visualization techniques to gather insights and pre-process data.
• Understand how to do Feature Engineering to enhance the performance of machine learning models. Learn to select the right supervised or unsupervised algorithm for varied use cases and problems.
• Learn how to evaluate and monitor machine learning models.

• Recommendation Engine: Learn how to create recommendation engines using collaboration filtering and content-based methods. Then, render personalized recommendations using datasets from E-Commerce platforms and video streaming services.
• Rating Predictions: Learn how to predict user ratings using machine learning models on various datasets such as movie ratings, product reviews, and customer satisfaction analysis.
• Census: Analyse and interpret Census data using various machine-learning techniques. Learn to find patterns using multiple criteria, such as demographics, age, etc.
• House Price Prediction: Learn to build a machine learning model in this certification course that can predict house prices by being trained on historical house prices.
• Object Detection: Understand the fundamentals of object detection. Learn how to detect objects in images and videos.
• Stock Market Analysis: Learn to analyze stock market data using time series analysis and learn how to build predictive models for stock prices and market trends.
• Banking Problem: Learn to solve real-world banking problems using machine learning models. Work on problems such as fraud detection, credit risk assessment, and improved decision-making.
• AI Chatbot: Work on creating powerful chatbots powered by AI. Learn how to design a conversational AI using Natural Language techniques.

The objectives are to develop understanding of the basic principles and techniques of image processing and image understanding, and to develop skills in the design and implementation of computer vision software.

To introduce students the fundamentals of image formation; To introduce students the major ideas, methods, and techniques of computer vision and pattern recognition; To develop an appreciation for various issues in the design of computer vision and object recognition systems; and To provide the student with programming experience from implementing computer vision and object recognition applications

A research methodology course equips students with the foundational skills and knowledge needed to conduct rigorous and effective research across various disciplines. Through this course, students learn the principles and techniques essential for designing, executing, and interpreting research studies. They delve into topics such as formulating research questions, selecting appropriate data collection methods, understanding sampling techniques, and mastering data analysis methods, both qualitative and quantitative. Moreover, the course covers ethical considerations, emphasising responsible and transparent research practices. Students gain proficiency in constructing research proposals, reviewing existing literature, and presenting findings with clarity and precision.

This course is highly relevant to understand the systematic scientific research writing process. This process helps in putting in perspective all conceptual learning and provides a framework for continuous growth in one’s own work environment.

The Capstone Consulting Project in Data Science and Artificial Intelligence is the culminating experience for students pursuing a specialisation in these fields. This course provides students with the opportunity to apply their knowledge and skills to real-world problems through a hands-on consulting project. Working in teams, students will collaborate with industry partners or organisations to address challenging data science and AI problems.

This course requires submission of Master Thesis.