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MIS10060

Academic Year 2024/2025

Introduction to Business Analytics (MIS10060)

Subject:
Management Information Systems
College:
Business
School:
Business
Level:
1 (Introductory)
Credits:
5
Module Coordinator:
Dr Istenc Tarhan
Trimester:
Autumn
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

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.

About this Module

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 Hours:
Student Effort Type Hours
Specified Learning Activities

36
Autonomous Student Learning

40
Lectures

24
Small Group

12
Total

112

Approaches to Teaching and Learning:
A predominantly face to face teaching approach will be used with face-to-face lectures and pre-recorded material made available on Brightspace.

Activities include:

- Active/task-based learning
- Group work
- Face-to-face lectures
- Online quizzes

Requirements, Exclusions and Recommendations
Learning Exclusions:

MIS20010


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


 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Quizzes/Short Exercises: There will be three online quizzes throughout the semester. Week 5, Week 7, Week 9 Standard conversion grade scale 40% No
15
 No
Group Work Assignment: This assignment requies to apply several techniques such as descriptive analytics, visualisation, prediction and forecasting to analyze a real-world scenario. Week 10 Standard conversion grade scale 40% No
30
 No
Individual Project: In this assessment, you will be provided a specific business problem to solve. Week 14 Standard conversion grade scale 40% No
55
 No

Carry forward of passed components
Yes
 

Resit In Terminal Exam
Spring No
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

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

How will my Feedback be Delivered?

Automated feedback on MCQ; Team feedback (Grade plus comment) pre team project, plus general feedback to the class; Individual feedback for individual assignments

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