FIT5197 - Modelling for data analysis - 2018

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)

Dr Asef Nazari

Unit guides

Offered

Caulfield

  • First semester 2018 (On-campus)
  • Second semester 2018 (On-campus)

Monash Online

  • Teaching Period 2 2018 (Online)
  • Teaching Period 5 2018 (Online)

Prerequisites

(FIT9133 or FIT9131) and MAT9004

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 explores the statistical modelling foundations that underlie the analytic aspects of Data Science. Motivated by case studies and working through examples, this unit covers the mathematical and statistical basis with an emphasis on using the techniques in practice. It introduces data collection, sampling and quality. It considers analytic tasks such as statistical hypothesis testing and exploratory versus confirmatory analysis. It presents basic probability distributions, random number generation and simulation as well as estimation methods and effects such as maximum likelihood estimators, Monte Carlo estimators, Bayes theorem, bias versus variance and cross validation. Basic information theory and dependence models such as regression and log-linear models are also presented, as well as the role of general modelling such as inference and decision making, and predictive models.

Outcomes

At the completion of this unit, students should be able to:

  1. perform exploratory data analysis with descriptive statistics on given datasets;
  2. construct models for inferential statistical analysis;
  3. produce models for predictive statistical analysis;
  4. perform fundamental random sampling, simulation and hypothesis testing for required scenarios;
  5. implement a model for data analysis through programming and scripting;
  6. interpret results for a variety of models.

Assessment

Monash Online: In-semester assessment: 100%

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

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

Additional information on this unit is available from the faculty at: