DATA WRANGLING AND R

BUS5DWR

2019

Credit points: 15

Subject outline

This subject introduces you to the various techniques of data wrangling with a strong focus on hands-on experience in R and SQL programming.  It will cover the basic concepts in relational database design including Entity Relationship (ER) modelling and Structured Query Language (SQL) as a tool for basic data wrangling.  You will also learn various types of data sources and common data formats.  The subject teaches you R programming language for you to perform data wrangling tasks, including data import and export, basic data integration and data assessment.  Upon completion, you will be able to perform a variety of data wrangling tasks using SQL and R for different kinds of data types.

SchoolLa Trobe Business School

Credit points15

Subject Co-ordinatorKok-Leong Ong

Available to Study Abroad StudentsNo

Subject year levelYear Level 5 - Masters

Exchange StudentsNo

Subject particulars

Subject rules

Prerequisites Enrolled in LMBAN, LCBAN, LGBAN; or subject coordinator approval

Co-requisitesN/A

Incompatible subjectsN/A

Equivalent subjectsN/A

Special conditionsN/A

Readings

Resource TypeTitleResource RequirementAuthor and YearPublisher
ReadingsData Wrangling with R (Use R!)RecommendedBoehmke, B., 2016Springer International Publishing
ReadingsThe Essentials of Data ScienceRecommendedWilliams, G., 2017CRC Press
ReadingsMicrosoft SQL Server 2016: A Beginner's GuideRecommendedPetkovic, D., 2016McGraw-Hill Education

Graduate capabilities & intended learning outcomes

01. Develop knowledge of structured database design and demonstrate capability of data wrangling with SQL

Activities:
Seminar
Related graduate capabilities and elements:
Inquiry and Analytical Skills (Critical Thinking)

02. Develop knowledge of various data formats and motivation for data wrangling

Activities:
Seminar
Related graduate capabilities and elements:
Literacies and Communication Skills (Writing)

03. Demonstrate capabilities in hands-on coding knowledge as a way to process data

Activities:
Seminar
Related graduate capabilities and elements:
Discipline -Specific Knowledge and Skills (Discipline-Specific Knowledge and Skills)

04. Demonstrate data wrangling expertise via various data assessment through the use of SQL and R coding

Activities:
Seminar
Related graduate capabilities and elements:
Inquiry and Analytical Skills (Critical Thinking)

Subject options

Select to view your study options…

Start date between: and    Key dates

City Campus, 2019, Semester 1, Night

Overview

Online enrolmentYes

Maximum enrolment size120

Enrolment information Room capacity order of application

Subject Instance Co-ordinatorKok-Leong Ong

Class requirements

Seminar Week: 10 - 22
One 3.0 hours seminar per week on weekdays at night from week 10 to week 22 and delivered via face-to-face.

Assessments

Assessment elementComments% ILO*
Individual Assignment (2000 words)Demonstrate knowledge in database design and proficiency in data wrangling using SQL.40 01
Individual Assignment (1000 words)Demonstrate knowledge on various data sources, data format and motivation for data wrangling and its implications to business analytics.20 02
Individual Assignment (2000 words)Demonstrate proficiency in data wrangling using R and SQL.40 03, 04

City Campus, 2019, Semester 2, Night

Overview

Online enrolmentYes

Maximum enrolment size120

Enrolment information Room capacity order of application

Subject Instance Co-ordinatorKok-Leong Ong

Class requirements

Seminar Week: 31 - 43
One 3.0 hours seminar per week on weekdays at night from week 31 to week 43 and delivered via face-to-face.

Assessments

Assessment elementComments% ILO*
Individual Assignment (2000 words)Demonstrate knowledge in database design and proficiency in data wrangling using SQL.40 01
Individual Assignment (1000 words)Demonstrate knowledge on various data sources, data format and motivation for data wrangling and its implications to business analytics.20 02
Individual Assignment (2000 words)Demonstrate proficiency in data wrangling using R and SQL.40 03, 04