FIT5202 - Data processing for big data - 2019

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.

Faculty

Information Technology

Chief examiner(s)

Assoc Professor David Taniar

Unit guides

Offered

Caulfield

  • Second semester 2019 (On-campus)

Monash Online

  • Teaching Period 3 2019 (Online)

Prerequisites

(FIT9131 or FIT9133) and FIT9132

A working knowledge of Python.

Notes

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.

Synopsis

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.

Outcomes

Upon successful completion of this unit students should be able to:

  1. identify and explain big data technologies;
  2. apply parallel processing using Spark and Data Frames;
  3. use and evaluate NoSQL databases;
  4. apply common data analytics and machine learning algorithms in a big data environment;
  5. evaluate the suitability of different processing techniques for big data processing.

Assessment

On-campus:

Examination (2 hours): 60%; In-semester assessment: 40%

Monash Online:

In-semester assessment: 100%

Workload requirements

Minimum total expected workload equals 144 hours per semester comprising:

  1. Contact hours for on-campus students:
    • Two hours/week lectures
    • Two hours/week laboratories
  2. 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.

  3. 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

This unit applies to the following area(s) of study