IT5100B

Industry Readiness: Stream Processing

The global availability of data has reached a level where aggregating data into generic, general-purpose “stores” is no longer feasible. Having data collections statically available for querying by interested parties on demand is increasingly becoming the way of the past. Instead, a new paradigm, called Data Streaming, has emerged recently. In this paradigm, data is bundled into high-throughput "streams" that are sharded efficiently across a large number of network nodes. Consumers, sometimes counted in hundreds of thousands, or millions, "subscribe" to data subsets and are notified when new data becomes available, being under the obligation to process it immediately, or lose it. Consequently, data storage is no longer centralized, but rather distributed into many smaller-sized abstract collections. This new approach to "Big Data" requires a new set of tools, platforms, and solution patterns. In this course we propose to explore several facets of this new paradigm:

• The Stream paradigm introduced in Java 8.
• Platforms that implement Data Streaming, such as Kafka, and the Java bindings in the library KafkaConnect.
• Computing paradigms for stream processing, such as Reactive Programming, and the library RxJava.
• High-performance stream computing platforms, such as Flink.

The course will be using Java as the main vehicle for introducing concepts and showcasing examples.

Semesters taught: AY23/24 Semester 2, AY24/25 Semester 2, AY25/26 Semester 1, AY25/26 Semester 2

IT5000

Introduction to Computing Foundations

IT5000 Introduction to Computing Foundations is a bootcamp-style course that aims to provide the prerequisite knowledge and skills for students to embark on their studies in computing foundations, and ease new students into the academic and computing environments they will be immersed in at the NUS School of Computing.

Broadly, the bootcamp introduces 3 areas:

1. Computational thinking (the basis of programming)
2. Discrete structures (mathematical foundation of computer science)
3. Linear algebra and probability (mathematical foundation of artificial intelligence and machine learning, which are pervasive in today's computing)

Pure Mathematics & Discrete Structures

We begin discussing some fundamental mathematical structures and the relationships between them. Particularly, numbers and arithmetic (which is unsurprisingly important), shapes (geometry and its extensions have wide applications including graphics, game design and AI), and sets (the foundations of mathematics). Then, we will learn how to perform logical reasoning on propositions of all kinds (propositions on numbers, shapes etc.). We then discuss what it means to do mathematics and how to write proofs, before finally introducing the Curry-Howard Correspondence and the BHK (Brouwer-Heyting-Kolmogorov) interpretation of propositional intuitionistic logic with a demonstration of Interactive Theorem Proving with Lean 4.

While on the surface, the purpose of this session is to introduce students to fundamental mathematics concepts, we hope to inspire further mathematics study by showing that the art of mathematics and the art of programming are, really, one and the same.

Semesters taught: AY23/24 Semester 2, AY24/25 Semester 1

CS1010E

Programming Methodology

This module introduces the fundamental concepts of problem solving by computing and programming using an imperative programming language. It is the first and foremost introductory course to computing. Topics covered include computational thinking and computational problem solving, designing and specifying an algorithm, basic problem formulation and problem solving approaches, program development, coding, testing and debugging, fundamental programming constructs (variables, types, expressions, assignments, functions, control structures, etc.), fundamental data structures (arrays, strings, composite data types), basic sorting, and recursion.

Semesters taught: AY18/19 Semester 1, AY18/19 Semester 2, AY19/20 Semester 1, AY19/20 Semester 2, AY19/20 Special Term 2, AY20/21 Semester 1

IT5003

Data Structures & Algorithms

This module introduces non-computing students to efficient computational problem solving in an accelerated pace. Students will learn to formulate a computational problem, identify the data required and come up with appropriate data structures to represent them, and apply known strategies to design an algorithm to solve the problem. Students will also learn to quantify the space and time complexity of an algorithm, prove the correctness of an algorithm, and the limits of computation. Topics include common data structures and their algorithms (lists, hash tables, heap, trees, graphs), algorithmic problem solving paradigms (greedy, divide and conquer, dynamic programming), and NP-completeness.

Semesters taught: AY20/21 Semester 2