DATA EXPLORATION AND ANALYSIS

CSE5DEV

2020

Credit points: 15

Subject outline

The goal of this subject is to provide you with specialist knowledge and tools required to formulate solutions to complex data p problems encountered by data scientists. You will learn various data exploration techniques and analysis tools. Selected problems include numerical data exploration, data cleaning and normalization, data reduction, clustering analysis and predictive analysis. One or more applications associated with each problem will also be discussed. To solve these problems, you will learn fundamentals of exploratory data analysis techniques, data reduction tools, statistical learning, logistic regression and predictive analysis. You will also learn the techniques to implement data exploration methods and analysis tools using R programming language.

SchoolEngineering and Mathematical Sciences

Credit points15

Subject Co-ordinatorNasser Sabar

Available to Study Abroad/Exchange StudentsYes

Subject year levelYear Level 5 - Masters

Available as ElectiveNo

Learning ActivitiesN/A

Capstone subjectNo

Subject particulars

Subject rules

Prerequisites CSE4DBF or MAT4NLA or admission into one of the following courses SMIOTB

Co-requisitesN/A

Incompatible subjectsN/A

Equivalent subjectsN/A

Quota Management StrategyN/A

Quota-conditions or rulesN/A

Special conditionsN/A

Minimum credit point requirementN/A

Assumed knowledgeN/A

Readings

The Elements of Statistical Learning: Data Mining, Inference, and Prediction

Resource TypePrescribed

Resource RequirementN/A

AuthorTrevor Hastie, Robert Tibshirani, Jerome Friedman

Year2009

Edition/VolumeN/A

PublisherSpringer

ISBNN/A

Chapter/article titleN/A

Chapter/issueN/A

URLN/A

Other descriptionN/A

Source locationN/A

Data Analysis with R

Resource TypeRecommended

Resource RequirementN/A

AuthorTony Fischetti

Year2015

Edition/VolumeN/A

PublisherPackt Publishing

ISBNN/A

Chapter/article titleN/A

Chapter/issueN/A

URLN/A

Other descriptionN/A

Source locationN/A

Evolving Fuzzy Systems --- Fundamentals, Reliability, Interpretability, Useability, Applications

Resource TypeRecommended

Resource RequirementN/A

AuthorEdwin Lughofer

Year2011

Edition/VolumeN/A

PublisherSpringer

ISBNN/A

Chapter/article titleN/A

Chapter/issueN/A

URLN/A

Other descriptionN/A

Source locationN/A

Think Stats: Exploratory Data Analysis

Resource TypeRecommended

Resource RequirementN/A

AuthorAllen B. Downey

Year2011

Edition/VolumeN/A

PublisherAmazon

ISBNN/A

Chapter/article titleN/A

Chapter/issueN/A

URLN/A

Other descriptionN/A

Source locationN/A

Machine Learning: A Probabilistic Perspective

Resource TypePrescribed

Resource RequirementN/A

AuthorKevin P. Murphy

Year2012

Edition/VolumeN/A

PublisherThe MIT Press

ISBNN/A

Chapter/article titleN/A

Chapter/issueN/A

URLN/A

Other descriptionN/A

Source locationN/A

Data Mining : Practical Machine Learning Tools and Techniques

Resource TypeRecommended

Resource RequirementN/A

AuthorIan H. Witten, Eibe Frank, Mark A. Hall

Year2006

Edition/VolumeN/A

PublisherMorgan Kaufman

ISBNN/A

Chapter/article titleN/A

Chapter/issueN/A

URLN/A

Other descriptionN/A

Source locationN/A

Career Ready

Career-focusedNo

Work-based learningNo

Self sourced or Uni sourcedN/A

Entire subject or partial subjectN/A

Total hours/days requiredN/A

Location of WBL activity (region)N/A

WBL addtional requirementsN/A

Graduate capabilities & intended learning outcomes

Graduate Capabilities

Intended Learning Outcomes

01. Investigate and critically analyse common problems encountered by data scientists in practice.
02. Formulate comprehensive solutions to data science problems
03. Effectively construct data analytics tools for application to complex data sets.
04. Develop comprehensive data reduction and data cleaning techniques for application to dimensionality problems.
05. Critically evaluate the performance of data exploration and data analysis techniques.

Subject options

Select to view your study options…

Start date between: and    Key dates

Bendigo, 2020, Semester 2, Day

Overview

Online enrolmentNo

Maximum enrolment sizeN/A

Subject Instance Co-ordinatorNasser Sabar

Class requirements

Computer Laboratory Week: 31 - 43
One 2.00 h computer laboratory per week on weekdays during the day from week 31 to week 43 and delivered via face-to-face.

Lecture Week: 31 - 43
One 1.00 h lecture per week on weekdays during the day from week 31 to week 43 and delivered via face-to-face.

Assessments

Assessment elementCommentsCategoryContributionHurdle% ILO*
Assignment on data exploration (equivalent to 1,300 words) Written reportN/AN/AN/ANo25 SILO1, SILO2, SILO4
Assignment on data analysis (equivalent to 1,300 words) Written reportN/AN/AN/ANo25 SILO1, SILO2, SILO3, SILO4
One 2-hour examination equivalent to 2,000 wordsN/AN/AN/ANo50 SILO1, SILO2, SILO4, SILO5

Melbourne (Bundoora), 2020, Semester 2, Day

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Subject Instance Co-ordinatorNasser Sabar

Class requirements

Computer Laboratory Week: 31 - 43
One 2.00 h computer laboratory per week on weekdays during the day from week 31 to week 43 and delivered via face-to-face.

Lecture Week: 31 - 43
One 2.00 h lecture per week on weekdays during the day from week 31 to week 43 and delivered via face-to-face.

Assessments

Assessment elementCommentsCategoryContributionHurdle% ILO*
Assignment on data exploration (equivalent to 1,300 words) Written reportN/AN/AN/ANo25 SILO1, SILO2, SILO4
Assignment on data analysis (equivalent to 1,300 words) Written reportN/AN/AN/ANo25 SILO1, SILO2, SILO3, SILO4
One 2-hour examination equivalent to 2,000 wordsN/AN/AN/ANo50 SILO1, SILO2, SILO4, SILO5