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IS30330

Academic Year 2016/2017

Quantitative Data Analysis (IS30330)

Subject:
Information Studies
College:
Social Sciences & Law
School:
Information & Comms Studies
Level:
3 (Degree)
Credits:
5
Module Coordinator:
Professor Benjamin Cowan
Semester:
Semester One
Mode of Delivery:
N/A
How will I be graded?
40%

Curricular information is subject to change.

The use of quantitative data to explore hypotheses about the world is a cornerstone of scientific enquiry. We are now living in a world with an unprecedented volume of datasets which can be used when making real world decisions and predictions. The understanding of how to collect, analyse and interpret quantitative data is a core skill for information professionals in the data driven age.

The course will give students a background into the processes and concepts related to collecting, analysing and interpreting quantitative data. The course is fundamentally practical in nature and will focus on introducing students to the R statistical programming language and R studio as well as the concepts important in data analysis and statistics. The course is aimed at students with no previous programming, mathematics or statistical analysis experience. The lectures are structured as a 1hr lecture with an additional 50 minute practical, where students will be guided step by step through analysis of datasets in R.

Core Course Text
Field, A., Miles, J., & Field, Z. (2012). Discovering Statistics using R. Sage Publications

About this Module

Learning Outcomes:

Learning Outcomes
On successful completion of the module students should be able to:

• Understand the benefits and drawbacks of the scientific method and quantitative data approaches
• Recognise and explain the appropriateness of statistical test for data analysis
• Execute a number of common statistical tests in R
• Interpret findings from these tests accurately
• Report findings from quantitative data appropriately and accurately

Student Effort Hours:
Student Effort Type Hours
Lectures

12
Computer Aided Lab

12
Specified Learning Activities

30
Autonomous Student Learning

71
Total

125

Requirements, Exclusions and Recommendations

Not applicable to this module.



Module Requisites and Incompatibles
Incompatibles:
Intro to Quantitative Methods (PEP10060), Research Methods (STAT10010), ExplDataAnalysis&IntroStatInfo (STAT10020), Intro. to Quant. Research I (STAT40470), Intro. to Quant. Research II (STAT40480)

 

Description % of Final Grade Timing
Assignment: Report 1

35

 Week 7
Assignment: Report 2

35

 Week 11
Class Test: Class Test

30

 Week 12

Compensation

This module is not passable by compensation

Resit Opportunities

In-semester assessment

Remediation

If you fail this module, you may avail of a resit opportunity. Details of resit arrangements will be provided by the module coordinatorand/or on Blackboard, including submission deadlines.

Name Role
Michele Cloonan Lecturer / Co-Lecturer
Mr Diego Garaialde Lecturer / Co-Lecturer
Dr Bahareh Heravi Lecturer / Co-Lecturer
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