Diploma in Applied AI and Analytics

Introduction

The Diploma in Applied AI and Analytics program aims to provide students with a strong foundation in Artificial Intelligence and Data Analytics. The curriculum includes core modules and specialized subjects, offering a comprehensive understanding of AI, Data Analytics, DevOps, and Full Stack Development.

Graduated with GPA 3.65.

Learning Objectives

1. Statistics for Data Science: This module introduces students to elementary probability theory and statistical concepts. Topics include descriptive statistics, rules of probability, probability distributions, and statistical estimation and hypothesis testing.

2. Front-End Web Development: Students learn to develop front-end web applications using HTML, CSS, and frameworks like Bootstrap.

3. Back-End Web Development: This module covers server-side programming and database-driven web applications using Node.js, Express.js, and MySQL to create RESTful APIs.

4. Fundamentals of Programming: Students use JavaScript to solve tasks, covering variables, data types, operators, control structures, functions, arrays, objects, and classes.

5. Fundamentals of Computing: This module provides an understanding of computer networking concepts, operating systems, and UNIX commands/tools for user management, software installation, and network administration.

6. Mathematics for AI: Students learn key mathematical concepts in data mining, machine learning, and model building, including linear algebra, SVD, PCA, and numerical algorithms.

7. Data Engineering: covers the fundamental concepts to build and work with data pipelines. Students are taught how to work with relational database, non-relational (NOSQL) database, and large data stores such as data warehouses and how to integrate data from multiple data sources into a single repository using Extract, Transform and Load (ETL) workflows. Students are also introduced to concepts of big data and its related challenges.

8. Data Visualisation: generate reports and dashboards that aid organisations to gain deeper insights into their business data. Students will learn best practices for creating effective data visualizations to support strategic data analysis and data-driven decisions using popular industry software such as Tableau and Python.

9. Deep Learning: deep neural network architectures and its practical applications. Students will learn to frame problems and prepare machine trainable datasets. They will apply deep learning frameworks such as Tensorflow, Keras, and PyTorch, to train deep learning models. Eventually, students will create and train their own deep learning models in various applications such as image recognition using convolutional neural networks, natural language processing using recurrent neural networks, creative deep learning using generative AI, as well as problem solving using reinforcement learning

10. Data Structures & Algorithm (AI): Advanced Object-Oriented concepts, data structures, and algorithms using Python, including stacks, queues, linked lists, and dictionaries.

11. Practical AI: Students learn data preparation, feature engineering, and applying AI to domains like digital marketing, cybersecurity, Fintech, and advanced manufacturing, using data from social media platforms.

12. DevOps: DevOps knowledge in integrating their AI applications with docker and containerized cloud services. Automating the AI workflow through Infrastructure-as-Code automation tools and services is essential for bringing AI code into production.