DATA3001 is a Level 3 cross faculty course course. See the course overview below.
Units of Credit: 6
Prerequisite/s: Students are assumed to have completed all level I and level II courses in the 3959 program before enrolling in this course. The course is only available to students in program 3959
Cycle of offering: Term 3
More information: The Course outline will be made available closer to the start of term - please visit this website: www.unsw.edu.au/course-outlines
The Online Handbook entry ,contains up-to-date timetabling information.
If you're currently enrolled in DATA3001, you can log into UNSW Moodle for this course.
Important additional information as of 2023
The University requires all students to be aware of its policy on plagiarism.
For courses convened by the School of Mathematics and Statistics no assistance using generative AI software is allowed unless specifically referred to in the individual assessment tasks.
If its use is detected in the no assistance case, it will be regarded as serious academic misconduct and subject to the standard penalties, which may include 00FL, suspension and exclusion.
The course aims to provide a broad overview of Data Science and would provide a platform for further studies in Data Science and an understanding and appreciation of Data Science in the modern world.
This is the capstone course for the Data Science and Decisions program. The course will bring students in the three streams together to share their knowledge, expertise and training in a way that is typical of industry. Students will attend seminars by industry representatives from Data Science industries, and students will work on group projects related to real world industry problems. Typical groups will be composed of students across the three different streams of the Data Science and Decisions program. The course will expose students to Data Science as it is practiced in industry.
Assumed knowledge: students are assumed to have completed all level I and level II courses in the 3959 program before enrolling in this course.