MIS10090 Data Analysis for Decision Mak

Academic Year 2019/2020

In the era of "Big Data", there is a challenge to turn data into insight. Data Analysis is the application of statistical techniques to describe and explore a set of data with the objective of highlighting useful information. Data Analysis is used to support evidence-based decision making.

This module is a foundation in data analysis for all business students and aims to serve the needs of subsequent courses in areas such as marketing, finance, accounting and business analytics. The three main areas introduced in this course are:
1/ Data Analysis using Excel: how to use a spreadsheet tool such as Excel. For example, to create a budget for a small business such as a coffee shop or to analyse data gathered from the coffee shop.
2/ Quantitative Analysis and Descriptive Statistics: how to gather and interpret large volumes of data in order to describe the information in concise and useful ways. For example, what is the average spend of a sample of customers in a coffee shop?
3/ Probability and Inferential Statistics: how to infer population parameters from sample statistics. For example, estimate how much is likely to be spent in the coffee shop in total.

This module is delivered using blended learning. Learning resources are available on Blackboard and participants engage in active learning exercises during face-to-face contact time.

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Curricular information is subject to change

Learning Outcomes:

On completion of this module students should be able to:
- Prepare spreadsheet models to store, manipulate and analyse quantitative data using common probability distributions and statistical functions.
- Calculate, analyse and present useful statistical measurements from large-scale data sets.
- Create and interpret inferential statistical statements about population parameters.
- Interpret the results of data analyses with a view to informing decision making.

Indicative Module Content:

Main topics:
- Analysis of quantitative data using common probability distributions and statistical functions.
- Calculation, analysis and presentation of useful statistical measurements from large-scale data sets.
- Creation and interpretation of inferential statistical statements about population parameters.
- Interpretation of the results of data analyses with a view to informing decision making.

Student Effort Type Hours
Lectures

24

Tutorial

12

Specified Learning Activities

24

Autonomous Student Learning

60

Total

120

Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Equivalents:
Quantitative Analysis for Busi (MIS10010), Data Analysis Decision Makers (SBUS10050)

 
Assessment Strategy  
Description Timing Component Scale Must Pass Component % of Final Grade
Examination: Written Exam 2 hour End of Trimester Exam Graded No

60

Continuous Assessment: Combination of MCQ's and assignments Varies over the Trimester Graded No

40


Carry forward of passed components
Yes
 
Resit In Terminal Exam
Autumn Yes - 2 Hour
Feedback Strategy/Strategies

• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

Name Role
Dr Aidan Boland Lecturer / Co-Lecturer

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