
BSc Information Technology Management for Business / Course details
Year of entry: 2023
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Course unit details:
Fundamentals of Data Analytics
Unit code | BMAN11060 |
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Credit rating | 20 |
Unit level | Level 1 |
Teaching period(s) | Full year |
Available as a free choice unit? | No |
Overview
The course sets the foundation for the Data Analytics theme of the ITMB curriculum. It introduces students to core concepts in data analytics, business intelligence and machine learning, while fostering a critical understanding of the assumptions underpinning these methodologies and the ethical and legal implications of data analysis.
Pre/co-requisites
Academic programmes that course is available to: ITMB
Aims
The course unit aims to:
1. To equip students with a critical understanding of data analytics, business intelligence and machine learning in a business setting;
2. To help develop skills in the use of industry-leading software tools for business analytics, mainly Microsoft Excel and Tableau.
Learning outcomes
At the end of the course students should:
• Understand the fundamentals of data analytics, business intelligence and machine learning, and be able to reflect on ethical and legal implications of data use.
• Appreciate the importance of data wrangling as the foundation of meaningful data analytics, business intelligence and machine learning pipelines.
• Be familiar with a variety of descriptive analytics and visualization tools, and understand the complementary function of these methodologies.
• Have the ability to competently select and apply relevant visualization and statistical tools to identify patterns and trends in large sets of data in real-world problems, and to critically evaluate the results obtained.
• Be able to formulate, test and interpret simple regression models.
Syllabus
Introduction to Data Analytics
- Data Analysis model (eg: CRISP-DM) and BI/DA tools.
- Ethical and Legal Aspects of Data Analysis
- Data Wrangling
Introduction to Business Intelligence
- Statistical Tools
- Data Visualization
Introduction to Machine Learning
- Regression
- Bias Variance Trade-Off / Overfitting
Introduction to Data Visualization in Tableau
Introduction to Data Analytics in Excel
- Descriptive Statistics Toolbox
- Vlookups / Match & Index
- Pivot Tables
- Regression Analysis
- Use of Excel Macros
- VBA Scripting
Teaching and learning methods
SEMESTER 1:
1 hour of lecture and 1 hour of lab (10 weeks)
SEMESTER 2:
1 hour of lecture and 2 hours of labs (10 weeks)
Assessment methods
Formative assessment:
- Lab exercises
Summative assessment:
- Individual lab submissions (4 submissions @ 10% each), 40%
- Individual Excel dashboard and accompanying coursework report, 60%
Feedback methods
Informal advice and discussion during lectures or seminars.
Response to student emails and questions from a member of staff including feedback provided via an online discussion forum.
Written and/or verbal comments on assessed and non-assessed work.
Generic feedback posted on Blackboard regarding overall examination performance.
Recommended reading
Alberto Ferrari, Analysing Data with Power BI and Power Pivot for Excel, 2016
Anil Maheshwari, Data Analytics Made Accessible, 2019 Edition
Study hours
Scheduled activity hours | |
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Lectures | 20 |
Practical classes & workshops | 30 |
Independent study hours | |
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Independent study | 150 |
Teaching staff
Staff member | Role |
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Rotimi Ogunsakin | Unit coordinator |
Additional notes
For Academic Year 2023/24
Updated: March 2023
Approved by: March UG Committee