BSc Information Technology Management for Business / Course details

Year of entry: 2023

Course unit details:
Fundamentals of Data Analytics

Course unit fact file
Unit code BMAN11060
Credit rating 20
Unit level Level 1
Teaching period(s) Full year
Available as a free choice unit? No


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.


Only available to students on BSc ITMB.

Academic programmes that course is available to: ITMB


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.


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


1 hour of lecture and 1 hour of lab (10 weeks)


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
Lectures 20
Practical classes & workshops 30
Independent study hours
Independent study 150

Teaching staff

Staff member Role
Rotimi Ogunsakin Unit coordinator

Additional notes

For Academic Year 2023/24

Updated: March 2023

Approved by: March UG Committee

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