Unit Outline

This unit will explain the relationship between data, information, and knowledge and introduce several different methods/tools for managing, storing, securing, modelling, visualising, and analysing data. This unit will provide an understanding of how data can be manipulated to meet the needs of users. Changing data into information can be accomplished with a range of tools, including Splunk, SQL, and Python. This unit introduces the techniques to enable the students to use these tools for managing data, visualising data, creating information, and allowing knowledge development. Overarching the whole unit is the importance of data security and how it can be achieved. Completing this unit will provide you with the opportunity to prepare for the Splunk Core Certified User certification exam. Students need to get permission from the unit coordinator to enrol in the online offering. This online offering is primarily for students enrolled in the Diploma of ICT Professional Practice (including the Undergraduate Certificate in ICT Professional Practice) or the Business Analytics major in the BBus who might need to complete the unit online. Online students will need to attend an online tutorial.

Intended Learning Outcomes

As per the Assessment and Results Policy 1.3, your results will reflect your achievement against specified learning outcomes.

On completion of this unit, you will be able to:


1	Securely store, convert and query data, information, and knowledge
2	Extract and analyse knowledge and information from data
3	Apply ICT knowledge, skills, and tools to design and develop efficient solutions to data-based problems

Requisites


REQUISITE TYPE	REQUISITES

Alterations as a result of student feedback

Update Assignment 1

Teaching arrangements


ATTENDANCE MODE	TEACHING TYPE	LEARNING ACTIVITY	CONTACT HOURS	FREQUENCY
On Campus	Lecture	A real-time (i.e. synchronous) interactive activity involving the whole class whose primary purpose is the presentation and structuring of information/ideas/skills to facilitate student learning. All students are expected to attend.	1	Weekly
On Campus	Tutorial	A structured real-time (i.e. synchronous) activity in a small-group setting where the primary purpose is the clarification, exploration or reinforcement of subject content presented or accessed at another time or place (e.g. lecture, preparatory work). It is reliant on student-teacher and student-student interaction and dialogue for achievement of its learning outcomes. The students enrolled in the tutorial are expected to attend.	2	Weekly

Attendance/Engagement Expectations

It is expected that you will attend all on-campus and onsite learning activities. This is to support your own learning and the development of a learning community within the unit.

If you miss a learning activity for a legitimate reason (e.g., illness, family commitments) teaching staff will attempt to provide alternative activities (e.g., make up readings) where it is possible.

If you are unable to attend regularly, please discuss the situation with your course coordinator and/or our UConnect support team.

How will I be Assessed?

Assessment schedule


ASSESSMENT TASK #	ASSESSMENT TASK NAME	DATE DUE	WEIGHT	LINKS TO INTENDED LEARNING OUTCOMES
ASSESSMENT TASK 1:	Data Analisys Assignment	Week 6	20 %	LO2, LO3
ASSESSMENT TASK 2:	Data Modelling Assignment	Week 9	20 %	LO1, LO3
ASSESSMENT TASK 3:	Weekly Tutorial Test	Week 11	30 %	LO1, LO2, LO3
ASSESSMENT TASK 4:	Data Mining Assignment	Week 13	30 %	LO1, LO2, LO3

Assessment details

Assessment Task 1: Data Analisys Assignment

TASK DESCRIPTION:

The data exploration assignment will assess the application of a range of content from the unit, including data search, big data exploration, visualisation and statistical analysis.

Students will be given a dataset where they will learn how to use Splunk to explore and extract useful information from that dataset.

TASK LENGTH:

Templates

DUE DATE:

Week 6

WEIGHT:

20 %

CRITERION #

CRITERION

MEASURES INTENDED LEARNING OUTCOME


1	Explain how meaningful information can be extracted from a dataset.	LO2
2	Apply data search, parallel computation, visualisation and statistical analysis using MS Excel to extract information from a dataset.	LO3

Assessment Task 2: Data Modelling Assignment

TASK DESCRIPTION:

The data modelling assignment will assess the application of a range of content from the unit, including data modelling, and SQL.

Students will be given a practical scenario from which they will design a database to address the data management task. Students will also insert and modify the data in the database to demonstrate the practicability of the design.

TASK LENGTH:

Templates

DUE DATE:

Week 9

WEIGHT:

20 %

CRITERION #

CRITERION

MEASURES INTENDED LEARNING OUTCOME


1	Design and create a database from a practical scenario.	LO1
2	Apply relational data modelling and SQL to solve a problem.	LO3

Assessment Task 3: Weekly Tutorial Test

TASK DESCRIPTION:

In the first half of each tutorial, tutors will deliver the practical tutorial content. Students are required to complete an online MyLO quiz in the second half of each tutorial.
These quizzes will assess the theoretical and practical content associated with the module for that week.

In-tutorial tests will start in Week 2 during the first tutorial.

The quiz must be completed within the week of student's assigned tutorial.

There will be quizzes on the following topics: Data lookup, Search Optimisation; Data Visualisation; Statistical Processing; Data Models; SQL; Data Wrangling; Data Mining; Exploratory Data Analysis

Note, each quiz is worth 3% of the total marks for the unit.

TASK LENGTH:

Up to 50 minutes per test

DUE DATE:

Week 11

WEIGHT:

30 %

CRITERION #

CRITERION

MEASURES INTENDED LEARNING OUTCOME


1	Design databases and write SQL for database creation and data manipulation.	LO1
2	Review, analyse, and visualise data.	LO2
3	Apply spreadsheet tools, SQL, and Python to conduct data analytics.	LO3

Assessment Task 4: Data Mining Assignment

TASK DESCRIPTION:

The data mining assignment will assess the application of a range of content from the unit, including data wrangling, data exploratory analysis, data mining, and security.

Students will be given a noisy and insecure dataset from which they will apply the techniques learned from the lectures and tutorials to clean the data and find abnormal data patterns. During the process, the students will need to process, store, and query the data and determine intermediate analytic results.

TASK LENGTH:

Templates

DUE DATE:

Week 13

WEIGHT:

30 %

CRITERION #

CRITERION

MEASURES INTENDED LEARNING OUTCOME


1	Explain how you load, process, and save data.	LO1
2	Explain your problem solving process when identifying and defining an approach to analyse a dataset.	LO2
3	Explain the steps you have taken in the design, implementation and evaluation of a solution to a data analytic problem.	LO3

How your final result is determined

To pass this unit, you need to demonstrate your attainment of each of the Intended Learning Outcomes, achieve a final unit grade of 50% or greater, and pass any hurdle tasks.

Submission of assignments

Where practicable, assignments should be submitted to an assignment submission folder in MYLO. You must submit assignments by the due date or receive a penalty (unless an extension of time has been approved by the Unit Coordinator). Students submitting any assignment in hard copy, or because of a practicum finalisation, must attach a student cover sheet and signed declaration for the submission to be accepted for marking.

Requests for extensions

If you are unable to submit an assessment task by the due date, you should apply for an extension.

A request for an extension should first be discussed with your Unit Coordinator or teaching support team where possible. A request for an extension must be submitted by the assessment due date, except where you can provide evidence it was not possible to do so. Typically, an application for an extension will be supported by documentary evidence: however, where it is not possible for you to provide evidence please contact your Unit Coordinator.

The Unit Coordinator must notify you of the outcome of an extension request within 3 working days of receiving the request.

Late Penalties

Assignments submitted after the deadline will receive a late penalty of 5% of the original available mark for each calendar day (or part day) that the assignment is late. Late submissions will not be accepted more than 10 calendar days after the due date, or after assignments have been returned to other students on a scheduled date, whichever occurs first. Further information on Late Penalties can be found on the Assessments and Results Procedure.

Review of results and appeals

You are entitled to ask for a review of the marking and grading of your assessment task if there is an irregularity in the marking standards or an error in the process for determining the outcome of an assessment. Details on how to request a review of a mark for an assignment are outlined in the Review and Appeal of Academic Decisions Procedure.