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.
School: Engineering and Mathematical Sciences (Pre 2022)
Credit points: 15
Subject Co-ordinator: Nasser Sabar
Available to Study Abroad/Exchange Students: Yes
Subject year level: Year Level 5 - Masters
Available as Elective: No
Learning Activities: N/A
Capstone subject: No
Subject particulars
Subject rules
Prerequisites: CSE4DBF or MAT4NLA or admission into one of the following courses SMIOTB
Co-requisites: N/A
Incompatible subjects: N/A
Equivalent subjects: N/A
Quota Management Strategy: N/A
Quota-conditions or rules: N/A
Special conditions: N/A
Minimum credit point requirement: N/A
Assumed knowledge: N/A
Learning resources
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Resource Type: Book
Resource Requirement: Prescribed
Author: Trevor Hastie, Robert Tibshirani, Jerome Friedman
Year: 2009
Edition/Volume: N/A
Publisher: Springer
ISBN: N/A
Chapter/article title: N/A
Chapter/issue: N/A
URL: N/A
Other description: N/A
Source location: N/A
Data Analysis with R
Resource Type: Book
Resource Requirement: Recommended
Author: Tony Fischetti
Year: 2015
Edition/Volume: N/A
Publisher: Packt Publishing
ISBN: N/A
Chapter/article title: N/A
Chapter/issue: N/A
URL: N/A
Other description: N/A
Source location: N/A
Evolving Fuzzy Systems --- Fundamentals, Reliability, Interpretability, Useability, Applications
Resource Type: Book
Resource Requirement: Recommended
Author: Edwin Lughofer
Year: 2011
Edition/Volume: N/A
Publisher: Springer
ISBN: N/A
Chapter/article title: N/A
Chapter/issue: N/A
URL: N/A
Other description: N/A
Source location: N/A
Think Stats: Exploratory Data Analysis
Resource Type: Book
Resource Requirement: Recommended
Author: Allen B. Downey
Year: 2011
Edition/Volume: N/A
Publisher: Amazon
ISBN: N/A
Chapter/article title: N/A
Chapter/issue: N/A
URL: N/A
Other description: N/A
Source location: N/A
Machine Learning: A Probabilistic Perspective
Resource Type: Book
Resource Requirement: Prescribed
Author: Kevin P. Murphy
Year: 2012
Edition/Volume: N/A
Publisher: The MIT Press
ISBN: N/A
Chapter/article title: N/A
Chapter/issue: N/A
URL: N/A
Other description: N/A
Source location: N/A
Data Mining : Practical Machine Learning Tools and Techniques
Resource Type: Book
Resource Requirement: Recommended
Author: Ian H. Witten, Eibe Frank, Mark A. Hall
Year: 2006
Edition/Volume: N/A
Publisher: Morgan Kaufman
ISBN: N/A
Chapter/article title: N/A
Chapter/issue: N/A
URL: N/A
Other description: N/A
Source location: N/A
Career Ready
Career-focused: No
Work-based learning: No
Self sourced or Uni sourced: N/A
Entire subject or partial subject: N/A
Total hours/days required: N/A
Location of WBL activity (region): N/A
WBL addtional requirements: N/A
Graduate capabilities & intended learning outcomes
Graduate Capabilities
Intended Learning Outcomes
Bendigo, 2020, Semester 2, Day
Overview
Online enrolment: No
Maximum enrolment size: N/A
Subject Instance Co-ordinator: Nasser 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 enrolment: Yes
Maximum enrolment size: N/A
Subject Instance Co-ordinator: Nasser 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 |