In this subject you will be provided with specialist knowledge and tools required to formulate solutions to complex data problems encountered by data scientists. You will learn various data exploration techniques and analysis tools. Selected topics include data cleaning, data normalisation, data visualisation and data exploration. One or more applications … For more content click the Read more button below.
In this subject you will be provided with specialist knowledge and tools required to formulate solutions to complex data problems encountered by data scientists. You will learn various data exploration techniques and analysis tools. Selected topics include data cleaning, data normalisation, data visualisation and data exploration. One or more applications associated with each problem will also be discussed. You will learn the fundamentals of exploratory data analysis techniques, statistical learning, and correlation analysis to solve these problems. You will also learn to implement data exploration methods and analysis tools using the R programming language.
Capstones provide students with a way of integrating and applying knowledge and skills gained throughout their course.
No
Academic progress review - Schedule A subject
No
Subject instances
To view instance specific details which include - Assessments, Class requirements and Subject instance coordinators - please select your preferred instance via the drop-down menu at the top right-hand side of this page.
Learning resources
Recommended - Book - Data Analysis with R
Title:Data Analysis with R
Resource requirement:Recommended
Author/editor:Tony Fischetti
Year:2015
Publisher:Packt Publishing
Recommended - Book - The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Title:The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Resource requirement:Recommended
Author/editor:Trevor Hastie, Robert Tibshirani, Jerome Friedman
Year:2017
Publisher:Springer
Recommended - Book - Storytelling with data: A data visualization guide for business professionals
Title:Storytelling with data: A data visualization guide for business professionals
Resource requirement:Recommended
Author/editor:Cole Nussbaumer Knaflic
Year:2015
Edition/volume:1
Publisher:Wiley
Prescribed - Book - R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
Title:R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
Resource requirement:Prescribed
Author/editor:Hadley Wickham and Garrett Grolemund
Year:2017
Publisher:O'Reilly Media, Inc, USA; 1st edition
Recommended - Book - Think Stats: Exploratory Data Analysis
Title:Think Stats: Exploratory Data Analysis
Resource requirement:Recommended
Author/editor:Allen B. Downey
Year:2011
Publisher:Amazon
Recommended - Book - Data Mining : Practical Machine Learning Tools and Techniques
Title:Data Mining : Practical Machine Learning Tools and Techniques
Resource requirement:Recommended
Author/editor:Ian H. Witten, Eibe Frank, Mark A. Hall
Year:2006
Publisher:Morgan Kaufman
Recommended - Book - Machine Learning: A Probabilistic Perspective
Title:Machine Learning: A Probabilistic Perspective
Resource requirement:Recommended
Author/editor:Kevin P. Murphy
Year:2012
Publisher:The MIT Press
Career ready
Work based learning (placement):No
Graduate capabilities
COMMUNICATION - Digital Capability
DISCIPLINE KNOWLEDGE AND SKILLS
INQUIRY AND ANALYSIS - Creativity and Innovation
INQUIRY AND ANALYSIS - Critical Thinking and Problem Solving
Subject intended learning outcomes
On successful completion you will be able to:
1.
Investigate and critically analyse common problems encountered by data scientists in practice.
2.
Formulate comprehensive solutions to data science problems
3.
Effectively construct data analytics tools for application to complex data sets.
4.
Develop comprehensive data reduction and data cleaning techniques for application to dimensionality problems.
5.
Critically evaluate the performance of data exploration and data analysis techniques.
Requisite rules
Prerequisites: CSE4DBF or MAT4NLA; OR Students must be admitted in one of the following courses: SMIOTB, LMBISC, LMBAN, BM005, LMFAN.
