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ITI5149 Applied data analysis

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Unit Code, Name, Abbreviation

ITI5149 Applied data analysis (04 Sep 2020, 09:38am) [Applied Data Analysis (04 Sep 2020, 09:43am)]

Reasons for Introduction

Reasons for Introduction (04 Sep 2020, 09:43am)

This unit is a duplicate unit of FIT5149. The ITIxxxx units have been created for the Monash Indonesia offering of the Master of Data Science due to the different teaching mode.

Objectives

Objectives (04 Sep 2020, 09:44am)

On successful completion of this unit, you should be able to:

  1. analyse data sets with a range of statistical, graphical and machine-learning tools;
  2. evaluate the limitations, appropriateness and benefits of data analytics methods for given tasks;
  3. design solutions to real world problems with data analytics techniques;
  4. assess the results of an analysis;
  5. communicate the results of an analysis for both specific and broad audiences.

Unit Content

ASCED Discipline Group Classification (04 Sep 2020, 09:44am)

020307

Synopsis (04 Sep 2020, 09:45am)

This unit aims to provide students with the necessary analytical and data modeling skills for the roles of a data scientist or business analyst. Students will be introduced to established and contemporary Machine Learning techniques for data analysis and presentation using widely available analysis software. They will look at a number of characteristic problems/data sets and analyze them with appropriate machine learning and statistical algorithms. Those algorithms include regression, classification, clustering and so on. The unit focuses on understanding the analytical problems, machine learning models, and the basic modeling theory. Students will need to interpret the results and the suitability of the algorithms.

Teaching Methods

Mode (04 Sep 2020, 09:45am)

On-campus

Assessment

Assessment Summary (04 Sep 2020, 09:45am)

Examination (2 hours and 10 minutes): 50%, In-semester assessment: 50%

Workloads

Workload Requirements (04 Sep 2020, 09:46am)

Minimum total expected workload equals 144 hours per semester comprising:

  1. Contact hours for on-campus students:
    • Two hours/week lectures.
    • Two hours/week tutorials.
  2. Additional requirements:
    • A minimum of 8 hours per week of personal study for completing lab/tutorial activities, assignments, private study and revision, and for online students, participating in discussions.

Resource Requirements

Teaching Responsibility (Callista Entry) (04 Sep 2020, 09:47am)

FIT

Prerequisites

Prerequisite Units (04 Sep 2020, 09:47am)

ITI5197

Prohibitions (04 Sep 2020, 09:48am)

FIT5197

Location of Offering (04 Sep 2020, 09:50am)

Indonesia

Faculty Information

Proposer

Jeanette Niehus

Approvals

School:
Faculty Education Committee:
Faculty Board:
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Faculty Manager:
Dean's Advisory Council:
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Version History

04 Sep 2020 Jeanette Niehus Admin: New unit for Indonesia, this is a copy of FIT5149 content.

This version: