Database System is essential to learn not only for Software Development but also for Machine Learning, System Design and Data Analytics. In this class I learn the followwing:

1. Understanding the requirement for a back-end system and designing Enhanced Entity Relation Diagram (ERD).

2. Converting ERD into Relational Schema and Physical SQL Schema.

3. Developing Views and Stored Procedures from the Relational Schema, and a web-application for User interface.

Although this class is one of the hardest class I have taken in Georgia Tech, it is one of those classes that I have learnt a lot from this. As a Computer Science student, I think any algorithm class is essential since it helps a student have algorithmic mindset. In class, I learnt about:

1. Graph Traversals

2. Flows in Network and Min-Cut Max flow

3. Linear and Dynamic Programming

4. Approximation and Randomization Algorithms

This class is very similar to Internet of Things and it is one of the fundamental class in HCI since it deals with concepts of designing and developing wearable and accessible technologies. 

It also involves practical application of sensors, Arduino and Machine Learning.

This course is one of the most essential class for any Computer Science student since it provides a broad foundation in the fundamentals of how computer systems and networks function together. Here are the concepts that I learnt through this course:

1. Understanding the internal workings of a CPU including its components and how they interact

2. Learning about different types of memory (Cache, RAM, Secondary Storage) and how they work together to optimise performance

3. How Operating system manages resources like memory, processes and devices using threads and scheduling algorithms 

4. Learning about communication protocols that enable devices to connect and exchange data over a network.

This course taught me qualitative ways to conduct research in HCI. Here are the following concepts are learnt in this class:

1. Data collection methods such as observations, interviews, participatory design, probes and other design research methods.

2. Analysis methods such as Grounded Theory, qualitative / quantitative coding, Inter-rater reliability, and Interpreting artifacts.

3. Learnt about the risks posed by researching on human subjects and how to consider them when designing a study.

4. Techniques for research methods used in tech industry which is based on Design research and Rapid Contextual Design (RCD).

This course is important to provide an idea about techniques in Machine Learning. Here are the concepts that I learnt in this course:

1. Basic Math for Data Science and Machine Learning such as Linear Algebra, Probability and statistics, Information Theory and Optimization

2. Unsupervised machine learning for data exploration such as Clustering Analysis, Dimensionality Reduction and Kernel Density estimation

3. Supervised learning for Predictive Data Analysis such as Tree-based models, Support Vector machines, Linear classification and regression and Neural Networks