MIS10060 Introduction to Business Analytics

Academic Year 2023/2024

In today's data-driven world, businesses of all sizes are presented with an opportunity to transform raw data into actionable insights. This module is designed to equip you with the knowledge and skills required to navigate the complex landscape of business analytics effectively.

Throughout this module, we will explore some of the following key themes:

– Understanding the significance of data in today's business environment. You'll gain insights into how data has transformed industries, driving innovation, efficiency, and competitive advantage.

- Exploring the core concepts and principles that underpin business analytics.

- Best practices for data collection, data cleaning, and data transformation to ensure the quality and reliability of your data.

- Exploring and understanding your data through data visualisation and mining techniques, to uncover hidden patterns and relationships.

- Using predictive models to enable businesses to forecast future trends and outcomes.

- Responsible analytics practices and the ethical aspects of data collection, usage, and sharing.

Throughout this module, you'll have the opportunity to engage in hands-on exercises, real-world case studies, and discussions that bridge the gap between theory and practical application. By the end of the module, you should understand the foundational principles of business analytics but also possess the skills to extract valuable insights from data, drive informed decisions, and contribute to the success of organisations in an increasingly data-centric world.

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Curricular information is subject to change

Learning Outcomes:

On completion of the module students should be able to:

1. Understand the fundamental concepts, terminology, and principles of business analytics.

2. Collect, clean, and transform data into a suitable format for analysis. Perform exploratory data analysis to visualise and interpret data, identify patterns, and generate hypotheses.

3. Summarise and interpret historical data using descriptive statistics and apply predictive modelling techniques to make data-driven predictions and forecasts.

4. Exhibit sound ethical responsibility in the handling of data and adhering to ethical and regulatory standards in analytics projects.

Indicative Module Content:

-- Foundational Knowledge
-- Data Understanding
-- Exploratory Data Analysis (EDA)
-- Descriptive and Predictive Analytics
-- Data Visualization
-- Critical Thinking and Problem Solving.

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:

MIS20010


Module Requisites and Incompatibles
Incompatibles:
COMP10030 - Algorithmic Problem Solving, MIS20010 - Business Analytics


 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Assignment: End of semester individual assignment Coursework (End of Trimester) n/a Graded No

60

Group Project: Group Project and Presentation. Unspecified n/a Graded No

30

Continuous Assessment: Assignments/Mini-Projects/MCQ Throughout the Trimester n/a Graded No

10


Carry forward of passed components
No
 
Resit In Terminal Exam
Spring No
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?

Automated feedback on MCQ; Team feedback (Grade plus comment) pre team project, plus general feedback to the class; Solutions to self-assessment exercises on VLE.

Autumn
     
Lecture Offering 1 Week(s) - 2, 3, 4, 5, 6, 7, 9, 10, 11, 12 Mon 15:00 - 15:50
Lecture Offering 1 Week(s) - Autumn: Weeks 2-12 Tues 16:00 - 16:50
Small Group Offering 1 Week(s) - Autumn: Weeks 2-12 Thurs 10:00 - 10:50
Small Group Offering 2 Week(s) - Autumn: Weeks 2-12 Thurs 11:00 - 11:50
Autumn
     

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