MIS10090 Data Analysis for Decision Makers

Academic Year 2020/2021

In the era of Analytics, 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 and so is a core part of Business Analytics.

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. 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. Practical exercises will use a spreadsheet tool such as Excel.
2. Probability and Distributions: discrete and continuous with examples from the real world
3. Inferential Statistics: how to infer population parameters from sample statistics. For example, estimate the average of a population, giving a confidence interval (margin of error).

This module is delivered using blended learning. Learning resources, including quizzes, are available on Brightspace and students engage in active learning exercises during face-to-face and/or online contact time.

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Curricular information is subject to change

Learning Outcomes:

On completion of this module students should be able to:
- Calculate, analyse and present useful statistical measurements from large-scale data sets;
- Use common probability distributions and statistical functions, and prepare spreadsheet models to store, manipulate and analyse quantitative data using these distributions;
- 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:
- Data Gathering and Presentation
- Descriptive Statistics
- Basic Probability
- Conditional Probability and Bayes's Theorem
- Probability Distributions and Random Variables
- Discrete Probability Distributions
- Continuous Probability Distributions
- The Normal Distribution
- Sampling
- Confidence Intervals
- Hypothesis Testing

Student Effort Hours: 
Student Effort Type Hours
Lectures

24
Tutorial

12
Specified Learning Activities

20
Autonomous Student Learning

70
Total

126
Approaches to Teaching and Learning:
Online learning approach incorporating:
- pre-lecture reading and reflection on online materials;
- lectures and tutorials (online);
- reflective learning;
- active/task-based learning: online quizzes and continuous assessment
- peer and group work, enquiry & problem-based learning: descriptive statistics assignment 
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 Open Book Exam Component Scale Must Pass Component % of Final Grade
Group Project: Team assignment: Descriptive Statistics report Week 6 n/a Graded No

30
Continuous Assessment: Online Assessment of work to date via Brightspace Week 11 n/a Graded No

30
Examination: Written Exam (online) 2 hour End of Trimester Exam Yes Graded No

40

Carry forward of passed components
Yes
 
Resit In Terminal Exam
Autumn Yes - 2 Hour
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

Recommended but not compulsory:
Lind, D. A., W. G. Marchal and S. A. Wathen. (2012). Basic Statistics for Business and Economics. McGraw-Hill

Alternative for background reading:
Berenson, M., D. Levine and T. Krehbiel. (2012). Basic Business Statistics: Concepts and Applications. Pearson Prentice Hall