cse5dev data exploration and analysis
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 (Pre 2022)
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
Learning resources
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Resource TypeBook
Resource RequirementPrescribed
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 TypeBook
Resource RequirementRecommended
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 TypeBook
Resource RequirementRecommended
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 TypeBook
Resource RequirementRecommended
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 TypeBook
Resource RequirementPrescribed
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 TypeBook
Resource RequirementRecommended
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
Subject options
Select to view your study options…
Bendigo, 2020, Semester 2, Day
Overview
Online enrolmentNo
Maximum enrolment sizeN/A
Subject Instance Co-ordinatorNasser Sabar
Class requirements
Computer LaboratoryWeek: 31 - 43
One 2.00 hours computer laboratory per week on weekdays during the day from week 31 to week 43 and delivered via face-to-face.
LectureWeek: 31 - 43
One 1.00 hour lecture per week on weekdays during the day from week 31 to week 43 and delivered via face-to-face.
Assessments
| Assessment element | Category | Contribution | Hurdle | % | ILO* |
|---|---|---|---|---|---|
Assignment on data exploration (equivalent to 1,300 words)Written report | N/A | N/A | No | 25 | SILO1, SILO2, SILO4 |
Assignment on data analysis (equivalent to 1,300 words)Written report | N/A | N/A | No | 25 | SILO1, SILO2, SILO3, SILO4 |
One 2-hour examination equivalent to 2,000 words | N/A | N/A | No | 50 | 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 LaboratoryWeek: 31 - 43
One 2.00 hours computer laboratory per week on weekdays during the day from week 31 to week 43 and delivered via face-to-face.
LectureWeek: 31 - 43
One 2.00 hours lecture per week on weekdays during the day from week 31 to week 43 and delivered via face-to-face.
Assessments
| Assessment element | Category | Contribution | Hurdle | % | ILO* |
|---|---|---|---|---|---|
Assignment on data exploration (equivalent to 1,300 words)Written report | N/A | N/A | No | 25 | SILO1, SILO2, SILO4 |
Assignment on data analysis (equivalent to 1,300 words)Written report | N/A | N/A | No | 25 | SILO1, SILO2, SILO3, SILO4 |
One 2-hour examination equivalent to 2,000 words | N/A | N/A | No | 50 | SILO1, SILO2, SILO4, SILO5 |