6 points, SCA Band 2, 0.125 EFTSL
Postgraduate - Unit
Refer to the specific census and withdrawal dates for the semester(s) in which this unit is offered.
(or ) and
A working knowledge of Python.
Monash Online offerings are only available to students enrolled in the Graduate Diploma in Data ScienceGraduate Diploma in Data Science (http://online.monash.edu/course/graduate-diploma-data-science/?Access_Code=MON-GDDS-SEO2&utm_source=seo2&utm_medium=referral&utm_campaign=MON-GDDS-SEO2) via Monash Online.
This unit focuses on big data processing, including latest big data technologies (Spark), and NoSQL database (MongoDB). The data processing covers data frames, and various advanced data analytics for big data. Programming exercises and assignments use Spark, MongoDB, Data Frames, and ML Lib.
Upon successful completion of this unit students should be able to:
- identify and explain big data technologies;
- apply parallel processing using Spark and Data Frames;
- use and evaluate NoSQL databases;
- apply common data analytics and machine learning algorithms in a big data environment;
- evaluate the suitability of different processing techniques for big data processing.
Examination (2 hours): 60%; In-semester assessment: 40%
In-semester assessment: 100%
Minimum total expected workload equals 144 hours per semester comprising:
- Contact hours for on-campus students:
- Two hours/week lectures
- Two hours/week laboratories
- Contact hours for Monash Online students:
- Two hours/week online group sessions.
Online students generally do not attend lecture, tutorial and laboratory sessions, however should plan to spend equivalent time working through resources and participating in discussions.
- Additional requirements (all students):
- A minimum of 8 hours per week of personal study (22 hours per week for Monash Online students) for completing lab/tutorial activities, assignments, private study and revision, and for online students, participating in discussions.
See also Unit timetable information