This course will start with the core principles of Data Science, and will equip you with the basic tools and techniques of data handling, exploratory data analysis, data visualization, data-based inference, and data-focussed communication. The course will also introduce you to the fundamentals of Machine Learning - prediction, classification, clustering, anomaly detection - to set the computational framework for Data Science. The goal is to motivate you to work closely with data and make data-driven decisions in your field of study. The course will also touch upon ethical issues in Data Science and motivate you to explore the cutting-edge applications related to Big Data, Neural Networks and Deep Learning. Python will be the language of choice to introduce hands-on computational techniques.
| AUs | 3.0 AUs |
| Grade Type | |
| Prerequisite | CZ1003, CE1003, CZ1103, CE1103 |
| Not Available To Programme | |
| Not Available To All Programme With | |
| Not Available As BDE/UE To Programme | |
| Not Available As Core To Programme | |
| Not Available As PE To Programme | |
| Mutually Exclusive With | CE1015, CE1115, CE4073, CH0494, CV0003, CZ1015, CZ1115, CZ4073, EE0005, MA0218, MS0003, PS0002 |
| Not Offered As BDE | |
| Not Offered As Unrestricted Elective | |
| Exam |
Available Indexes
| Mon | Tue | Wed | Thu | Fri | |
|---|---|---|---|---|---|
| 930 | |||||
| 1000 | |||||
| 1030 | |||||
| 1100 | |||||
| 1130 | |||||
| 1200 | |||||
| 1230 | |||||
| 1300 | |||||
| 1330 | |||||
| 1400 | |||||
| 1430 | |||||
| 1500 | |||||
| 1530 | |||||
| 1600 | |||||
| 1630 | |||||
| 1700 | |||||
| 1730 | |||||
| 1800 |
Other offerings
Other Relevant Mods
CZ1007
Data Structures
CZ1103
Introduction To Computational Thinking & Programming
CZ1104
Linear Algebra For Computing
CZ1105
Digital Logic
CZ1106
Computer Organisation & Architecture
CZ1107
Data Structures & Algorithms
CZ1115
Introduction To Data Science & Artificial Intelligence
CZ1120
Introduction To Digital Communications & Networking
CZ2001
Algorithms