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MIS10090

Academic Year 2026/2027

Data Analysis for Decision Makers (MIS10090)

Subject:
Management Information Systems
College:
Business
School:
Business
Level:
1 (Introductory)
Credits:
5
Module Coordinator:
Assoc Professor Sean McGarraghy
Trimester:
Spring
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

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 contact time.

About this Module

Learning Outcomes:

On completion of this module students should be able to:
- Discuss and use the main concepts and approaches of descriptive statistics;
- Calculate, analyse, visualise and present useful statistical measurements from large-scale data sets;
- Use common probability distributions and statistical functions;
- Devise, test and interpret inferential statistical statements about population parameters;
- Prepare spreadsheet models to store, manipulate and analyse quantitative data;
- Interpret the results of data analyses with a view to informing decision making.

Indicative Module Content:

Main topics:
- The role of data analysis in business and decision making
- Data Gathering, Visualisation 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

UNESCO highlights a set of key competencies that support individuals in their development and support society in achieving the UN Sustainable Development Goals (SDGs). UCD has adapted these competencies and is combining them with others to form a general framework of learning competencies. This module will help you develop the competencies below to the levels specified. 
Learning Competency Additional Information Level

Collaboration

 The ability to learn from others; to understand and respect the needs, perspectives and actions of others (empathy); to understand, relate to and be sensitive to others (empathic leadership); to deal with conflicts in a group; and to facilitate collaborative and participatory problem solving. Not addressed

Critical Thinking

 The ability to question norms, practices and opinions; to reflect on one’s own values, perceptions and actions. Advanced Beginner

Systems Thinking

 The ability to recognize and understand relationships; to analyse complex systems; to think of how systems are embedded within different domains and different scales; and to deal with uncertainty. Advanced Beginner

Strategic

 The ability to collectively develop and implement innovative actions that further sustainability at the local level and further afield. Not addressed

Integrated Problem Solving

 The overarching ability to apply different problem-solving frameworks to complex sustainability problems and develop viable, inclusive and equitable solution options that promote sustainable development, integrating the competencies in this list. Competent

Self-awareness

 The ability to reflect on one’s own role in the local community and (global) society; to continually evaluate and further motivate one’s actions; and to deal with one’s feelings and desires. Not addressed

Normative

 The ability to understand and reflect on the norms and values that underlie one’s actions; and to negotiate values, principles, goals, and targets, in a context of conflicts of interest and trade-offs, uncertain knowledge and contradictions. Not addressed

Anticipatory

 The ability to understand and evaluate multiple scenarios for the future – possible, probable and desirable; to create one’s own visions for the future; to apply the precautionary principle; to assess the consequences of actions; and to deal with risks and changes. Advanced Beginner

Digital Literacy and Judgement

 The ability to access, evaluate, create and communicate information in digital environments; to engage critically, ethically and responsibly with digital technologies and digital information; to understand their opportunities, limitations, risks and impact on individual’s digital identities; and to exercise informed judgement in digital participation and decision-making. Competent

AI Literacy & Agency

 The ability to understand, critically evaluate and responsibly engage with artificial intelligence; to recognise how AI systems are shaped by human values; to assess their ethical, social and environmental implications; and to exercise human judgement, agency and accountability in AI-related contexts. Not addressed

Wellbeing

 Wellbeing is having the resources and skills to meet life's challenges, including attributes such as personal development skills, resilience, stress management, strengths, lifestyle skills, nutrition, physical activity, sleep, alcohol/substance use, academic skills, time management, goal setting, interpersonal skills, group work, communication. Not addressed

The United Nations identified seventeen Sustainable Development Goals (SDGs) as core to the 2030 Agenda for Sustainable Development, and UCD contributes in general to SDG 4 Quality Education. Further SDGs explored within this module if relevant are listed below. A scale of 1 - 5 indicates the extent to which the SDG is covered.


 

Student Effort Hours:
Student Effort Type Hours
Specified Learning Activities

20
Autonomous Student Learning

70
Lectures

24
Tutorial

12
Total

126

Approaches to Teaching and Learning:
Lectures and tutorials are face-to-face. The learning approach incorporates:
- pre-lecture reading and reflection on online Brightspace materials;
- a 2-hour lecture each Monday of term and a 1-hour tutorial either Tue, Wed or Thu of the week;
- reflective learning, guided by class material exercises and tutorial assigned questions;
- active/task-based learning: online weekly quizzes (each Friday of term) and continuous assessment.

AI is permitted for limited support:
Students may use AI tools for limited support in this module (e.g., for practice questions or as a study aid). Students remain fully responsible for the accuracy and reliability of their work. Any use of AI must be cited appropriately. AI may not be used for assessment, including the weekly quizzes.

This module addresses the following of the 17 UN Sustainable Development Goals (SDGs), specifically:
Goal 4: Quality Education
and enables / improves engagement with the others by empowering students to analyse approaches to them using data.

Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Incompatibles:
ECON10030 - Intro Quantitative Economics, ECON20040 - Statistics for Economists

Equivalents:
Quantitative Analysis for Busi (MIS10010), Data Analysis Decision Makers (SBUS10050)


 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Exam (In-person): Main exam held during the exam period in early May. End of trimester
Duration:
2 hr(s)
 Standard conversion grade scale 40% No
80
 No
Quizzes/Short Exercises: Each Friday except Good Friday, 11 weeks of term: Brightspace quiz to assess students' understanding of topics covered that week. Each is worth 2%, best ten quizzes counted as total 20% of grade Week 1, Week 2, Week 3, Week 4, Week 5, Week 6, Week 7, Week 9, Week 10, Week 11, Week 12 Alternative linear conversion grade scale 40% No
20
 No

As part of UCD's student support, under the Additional Consideration policy, extensions may be available for the following assessments in the module: Assignment (including essay/poster), Portfolio, Reflective Assignment, Report(s), and Individual Project.


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
• Online automated feedback

How will my Feedback be Delivered?

There will be 11 weekly quizzes, the best ten counting for credit of 2% each. Once each quiz is finished, each student will be able to view their results and explanations of the correct approaches, and see where they need to improve. Also the lecturers will send a general email summarising the overall performance in each week's quiz and highlighting common themes.

Required for the normal distribution and inferential statistics:
Lindley, D. F. and W. F. Scott (1995). New Cambridge Statistical Tables. Cambridge University Press. Second edition.

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

Name Role
Dr Annunziata Esposito Amideo Lecturer / Co-Lecturer
Dr Istenc Tarhan Lecturer / Co-Lecturer

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Spring External & School Exams Offering 2 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 31, 32, 33 Fri 12:00 - 12:50
Spring Lecture Offering 2 Week(s) - 20, 21, 23, 24, 25, 26, 29, 30, 32, 33 Mon 13:00 - 14:50
Spring External & School Exams Offering 3 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 31, 32, 33 Fri 12:00 - 12:50
Spring Lecture Offering 3 Week(s) - 20, 21, 23, 24, 25, 26, 29, 30, 32, 33 Mon 15:00 - 16:50
Spring Small Group Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Tues 13:00 - 13:50
Spring Small Group Offering 2 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Tues 14:00 - 14:50
Spring Small Group Offering 3 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Thurs 13:00 - 13:50
Spring Small Group Offering 4 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Thurs 14:00 - 14:50
Spring Small Group Offering 5 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Tues 09:00 - 09:50
Spring Small Group Offering 6 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Tues 10:00 - 10:50
Spring Small Group Offering 7 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Tues 11:00 - 11:50
Spring Small Group Offering 8 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Thurs 10:00 - 10:50
Spring Small Group Offering 9 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Thurs 11:00 - 11:50
Spring Small Group Offering 10 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Thurs 13:00 - 13:50
Spring Small Group Offering 11 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Wed 14:00 - 14:50
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