PRINCIPLES OF ANALYTICS - 2026/7

Module Overview

This module provides an introduction to data analytics within a business context, focusing on the foundations of data-driven decision-making and the analytics lifecycle. Students will develop practical skills in data management, business intelligence, visualisation, and introductory machine learning techniques.

The module introduces key concepts and frameworks used in business analytics, including the Cross-Industry Standard Process for Data Mining (CRISP-DM), relational databases, structured query language (SQL), exploratory data analysis, and dashboard development. Students will work with real-world datasets to develop analytical solutions that support managerial and operational decision-making.

A strong emphasis is placed on transforming data into actionable insights through effective visualisation, storytelling, and business intelligence techniques. The module also introduces fundamental concepts in statistical learning and predictive analytics, providing a foundation for more advanced modules in machine learning and artificial intelligence.

Module provider

Surrey Business School

Module Leader

GROVER Vikas (SBS)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

Module cap (Maximum number of students): N/A

Module Availability

Semester 1

Prerequisites / Co-requisites

MANM530 is a co-requisite for MANM547.

Module content

The module includes knowledge, techniques and tools relevant to the area of Data Analytics for Business. Please find below an indicative set of topics:

• Introduction to Data Analytics: definitions, applications, ethics, analytics lifecycle, business value of analytics.
• Analytics Frameworks: CRISP-DM (or KDD), knowledge discovery processes, analytics project workflows.
• Data Engineering and Relational Databases: structured data, schemas, keys, relationships, database design concepts.
• SQL for Analytics: querying, joins, aggregation functions, and construction of analytical datasets.
• Business Intelligence and Visualisation: dashboard design, storytelling with data, visual perception principles, key performance indicators (KPIs), business intelligence software and dashboarding environments.
• Data Science Language (DSL): introduction to programming for data analytics, including data structures, packages, and exploratory analysis using modern data science languages.
• Exploratory Data Analysis: descriptive analytics, correlations, feature exploration, data interpretation.
• Introduction to Machine Learning: classification concepts, confusion matrix, performance metrics
• Business Decision Support: communicating insights, managerial interpretation, data-driven recommendations

Assessment pattern

Alternative Assessment

Not applicable

Assessment Strategy

Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate achievement of all learning outcomes through both applied coursework and individual analytical problem-solving.

Thus, the summative assessment for this module consists of:

• One coursework assignment ¿ testing Learning Outcomes 1¿6
• One examination ¿ testing Learning Outcomes 1¿5

The coursework is designed to assess students¿ ability to apply the data analytics lifecycle to a realistic business problem using structured datasets. Students are required to demonstrate competencies in data understanding, database querying, exploratory analysis, business intelligence, visualisation, and introductory predictive analytics. The assessment emphasises the integration of technical implementation, analytical reasoning, and professional communication to transform data into actionable business insights and recommendations.

The examination is designed to assess students¿ understanding of core analytical concepts, frameworks, and quantitative techniques introduced throughout the module. Students are expected to demonstrate their ability to interpret analytical scenarios, apply appropriate methods, evaluate results, and solve structured business analytics problems within a time-constrained environment. These analytical and problem-solving skills are central to the programme and aligned with industry expectations.

Students will work with numerous datasets during lectures and computer laboratory sessions. Guided exercises and additional analytical tasks included within the laboratory materials will support independent study and skills development throughout the semester.

Formative feedback will be provided through:

• laboratory discussions and practical exercises;
• guided solutions to analytical tasks;
• opportunities for discussion during office hours and support sessions; and
• coursework guidance activities integrated throughout the module.

These activities are designed to support students in developing technical, analytical, and communication skills progressively throughout the module.

Students will receive ongoing verbal and written feedback during laboratory sessions, formative activities, and coursework support discussions. Opportunities will be provided for students to discuss analytical approaches, technical implementation, interpretation of results, and assessment strategies throughout the semester.

A dedicated session towards the end of the module will be used to discuss assessment preparation, common challenges, and overall module feedback.

Module aims

• Develop a systematic understanding of data analytics concepts, frameworks, and business intelligence techniques for decision-making.
• Enable students to manage, explore, visualise, and analyse structured data using appropriate database, business intelligence, and programming tools.
• Equip students with the ability to apply analytical techniques to real-world business problems and communicate insights effectively.

Learning outcomes

Methods of Teaching / Learning

The module is delivered through a combination of:

• Lectures introducing key concepts, frameworks, and analytical methods.
• Computer laboratory sessions focusing on SQL, business intelligence tools, and programming for analytics.
• Guided independent study using datasets, coding exercises, and online materials.
• Formative activities based on practical business analytics scenarios.

Students are expected to engage in independent study, including reading academic and practitioner literature, completing analytical exercises, and applying data analytics techniques using appropriate software tools.

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list

https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: MANM530

Other information

Employability: The module is highly applied and directly teaches the key skills needed by Business Analytics graduates entering analytical careers.

Digital capabilities: Students develop digital capabilities by using problem-solving and analytical skills to analyse data and make decisions on how to transform it into useful insights, understanding how data is turned into knowledge and eventually into insights.

Programmes this module appears in

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2026/7 academic year.