FIT5145 - Introduction to data science - 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)

Professor Wray Buntine (Semester 1)
Dr Mahsa Salehi (Semester 2)

Unit guides

Offered

Caulfield

  • First semester 2019 (On-campus)
  • Second semester 2019 (On-campus)
  • Summer semester B 2019 (On-campus)

Monash Online

  • Teaching Period 1 2019 (Online)
  • Teaching Period 4 2019 (Online)

Prerequisites

(FIT9131 or FIT9133) and FIT9132

For students enrolled in E3001, E3002, E3005, E3010, E3011, E3007 completing the Software Engineering specialisation: FIT2099 and FIT3171

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 looks at processes, case studies and simple tools to understand the many facets of working with data, and the significant effort in Data Science over and above the core task of Data Analysis. Working with data as part of a business model and the lifecycle in an organisation is considered, as well as business processes and case studies. Data and its handling is also introduced: characteristic kinds of data and its collection, data storage and basic kinds of data preparation, data cleaning and data stream processing. Styles of data analysis and outcomes of successful data exploration and analysis are reviewed. Standards, tools and resources are also reviewed. Basic curation and management are reviewed: archival and architectural practice, policy, legal and ethical issues.

Outcomes

On successful completion of this unit a student should be able to:

  1. analyse the role of data in organisations, including curation and management issues;
  2. apply basic tools for performing exploratory data analysis and visualisation;
  3. apply basic tools for managing and processing big data;
  4. apply basic predictive modeling and data analysis methods;
  5. determine data storage and processing requirements for a data science project;
  6. identify data resources and standards.

Assessment

NOTE: From 1 July 2019, the duration of all exams is changing to combine reading and writing time. The new exam duration for this unit is 2 hours and 10 minutes.

For Monash Online: In-semester assessment: 100%

On-campus: Examination (2 hours and 10 minutes): 50%; In-semester assessment: 50%

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