All Courses

IT Workshop

Follow a project-based approach. The goal of the course is to make students proficient in the use of modern digital technologies that enhance the productivity of software professionals by 10x.

Computer Science Principles and Programming

Understand the fundamental computing principles for students with little-to-no computing background. Explore Programming constructs, Data organization and Use of computational principles in problem-solving. Learn how to classify computational problems based on their complexity.

Introduction to Soft Skills

Get introduced to a whole host of various professional skills across effective verbal, non-verbal & visual communication skills with confidence built through repeated practice of public speaking activities. This course would also have a continuous credit component across the term.

Introduction to Data Science

Learn how to extract insight and knowledge from large amounts of data. Get a practical introduction to a data science analysis, including data collection and processing, data visualization and presentation, statistical model building using machine learning for scaling these methods.

Algorithms and Data Structures

The study of algorithms and data structures is fundamental to solving larger computing problems. Understand the essential information about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations.

Computer Systems

Students get to build a simple concurrent caching web proxy while exploring the interesting world of network programming, and tie together many of the concepts in the course, such as byte ordering, caching, process control, signals, signal handling, concurrency, and synchronization.

LSRW I

Get introduced to a whole host of various professional skills across effective verbal, non-verbal & visual communication skills with confidence built through repeated practice of public speaking activities. This course would also have a continuous credit component across the term.

LSRW II

Get introduced to a whole host of various professional skills across effective verbal, non-verbal & visual communication skills with confidence built through repeated practice of public speaking activities. This is the second part of the LSRW program.

Professional Development

Learn spoken, written, digital and visual communication. Communication can be in the form of presentations, emails, video calls, in-person conferences or minutes of a meeting.

Technical Writing

Learn Technical Writing, a writing discipline that is defined as simplifying the complex. This can help technical subject experts communicate ideas effectively. It results in relevant, useful and accurate information geared to specifically targeted audiences.

Interview Skills

Learn how to make a good first impression during an interview and revolves around the skills required to plan and prepare for an interview.

Introduction to Machine Learning

Learn the end-to-end process of investigating data with a machine learning lens. It will teach how to extract and identify useful features that best represent the data, a few of the most important machine learning algorithms, and how to evaluate the performance of the same.

Deep Learning

Learn the basics of deep learning and building deep neural networks using PyTorch. Students would get practical experience with PyTorch through coding exercises and projects implementing state of the art AI applications such as style transfer and text generation.

Reinforcement Learning

Learn the fundamentals of reinforcement learning and its elements. As part of the course, students would be introduced to OpenAI gym - which is a programming environment used for implementing RL agents. The key objective being to familiarize students with basic RL algorithms and applications.

Data collection and processing

Ingest data from unstructured and structured sources, and use relational models, time-series algorithms, graph and network processing, natural language processing, geographic information system processes to store and manage the data.

Statistical Modeling

Apply basic statistical techniques and analyses to understand properties of the data and design experimental setups for testing hypotheses or collecting new data.

Advanced ML Techniques

Apply advanced machine learning algorithms such as kernel methods, boosting, deep learning, anomaly detection, factorization models, and probabilistic modeling to analyze and extract insights from data.

Data Visualization

Visualize the data and results from the analysis, mainly focusing on visualizing and understanding high-dimensional structured data and the results of statistical and machine learning analysis.

Big Data

Scale the methods to big data regimes, where distributed storage and computation are needed to realize the capabilities of data analysis techniques.

Data Science Debugging

Scale the methods to big data regimes, where distributed storage and computation are needed to realize the capabilities of data analysis techniques.

Web Backend

This course presents an overview of a variety of Web backend topics across web server programs, such as Flask Framework. Students also learn web frameworks like Django, Spring, Rails and more.

Databases

Learn how to work with databases in this course. It presents an overview of creating a Relational Database (MySQL/Postgres), Object Relational Mapping (Hibernate), Document Oriented Database (Mongo) and more.

Web Frontend

Learn how to develop a website front-end with HTML, CSS, Bootstrap and more. In this course, students will also learn to work with JavaScript/Typescript, React Framework and Progressive web apps.

Mobile Apps

Learn how to use Kotlin/Java to develop mobile apps. In this course, students also preview some of the tools and technologies they will use to build and run their apps.

Continuous Integration and Continuous Delivery (CICD)

Learn how to set up containers, GIT actions, deploy testing frameworks such as JEST or Selenium, and monitoring apps and more in this course.