MIS20010 Business Analytics

Academic Year 2023/2024

Business Analytics focuses on the use of mathematical, statistical, data-oriented and computer-based techniques to help businesses operate optimally. It seeks to understand what has happened and predicting what is going to happen, and making optimal decisions to take advantage of that.

The main techniques we consider are Correlation, Regression, Time-Series Forecasting, Linear Programming, Classification, and Clustering. For each technique we study, we learn how it works on paper before proceeding to software-based implementations. We also consider practical applications of these techniques, including some or all of sales forecasting, credit risk, resource allocation, employee assignment, investment, customer churn, inventory, and product mix.

Emphasis is placed not only on formulating and using models to obtain solutions, but on understanding case study problems, choosing the appropriate models, and interpreting and critiquing the results.

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Curricular information is subject to change

Learning Outcomes:

On completion of this module, students should be able to understand a range of quantitative business problems and identify suitable analytics models for addressing them; and be able to explain, carry out in practice, and interpret the results of models including regression, time series forecasting, correlation, linear programming, classification, and clustering.

Student Effort Type Hours
Lectures

24

Small Group

12

Specified Learning Activities

36

Autonomous Student Learning

40

Total

112

Requirements, Exclusions and Recommendations
Learning Exclusions:

Introduction to Business Analytics (1st-year module from the BSc in Quantitative Business)

Learning Recommendations:

This module requires that students already have knowledge equivalent to UCD MIS10090, Data Analysis for Decision Makers -- probability and statistics, and basic Excel. The level of required mathematics is about Ordinary Level Leaving Certificate, occasionally a bit higher.


Module Requisites and Incompatibles
Incompatibles:
MIS10060 - Introduction to Bus Analytics


 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Examination: End-of-trimester computer based classroom exam 2 hour End of Trimester Exam Yes Graded No

50

Continuous Assessment: Large group project and/or class activities. Throughout the Trimester n/a Graded No

50


Carry forward of passed components
No
 
Resit In Terminal Exam
Autumn Yes - 2 Hour
Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
• Online automated feedback
• Self-assessment activities

How will my Feedback be Delivered?

Not yet recorded.

Name Role
Dr Mel Devine Lecturer / Co-Lecturer
Ms Bing CHEN Tutor
Spring
     
Lecture Offering 1 Week(s) - 20, 21, 23, 24, 25, 26, 29, 31, 32, 33 Mon 15:00 - 16:50
Lecture Offering 2 Week(s) - 20, 21, 23, 24, 25, 26, 29, 31, 32, 33 Mon 11:00 - 12:50
Small Group Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Tues 15:00 - 15:50
Small Group Offering 2 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Tues 14:00 - 14:50
Small Group Offering 3 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Thurs 11:00 - 11:50
Small Group Offering 4 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Thurs 12:00 - 12:50
Small Group Offering 5 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Tues 16:00 - 16:50
Small Group Offering 6 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Thurs 10:00 - 10:50
Small Group Offering 7 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Tues 09:00 - 09:50
Small Group Offering 8 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Tues 10:00 - 10:50
Small Group Offering 9 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Tues 11:00 - 11:50
Spring
     

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