Unit 26 Big Data Analytics and Visualisation (F/618/5664) Assignment Brief 2026

Unit 26 Big Data Analytics and Visualisation Assignment Brief 2026

Introduction

Raw data can be complicated, confusing and a challenge to understand. But when raw data is organised and structured properly it can reveal patterns and information that can be very powerful in business decision making. Without the ability to organise and visualise data, key information would otherwise remain hidden in raw data. Once a business can understand historic patterns of data sets this information can help predict future trends and behaviours.

Data and visualisation is an area which has seen rapid advancement and there has been considerable challenges for data specialists to develop the skills, experience and growth required to maintain innovation in the sector. As data continues to be the fuel for the digital economy, this area remains a constant topic of conversation for organisations, governments and the public who share an interest in its growing commercial use, manipulation, and presentation.

This unit introduces students to the concepts of big data and visualisation and how this is used for decision making. It explores the industry software solutions available to investigate and present data, before assessing the role and responsibility of data specialists in this current environment. Topics including data driven decision-making, manipulating data and automation, and building ethics into a data-driven culture are examined. Students will demonstrate their use of tools and software to manipulate and prepare a visual presentation for a given data set. They will also assess how data specialists are responsible for adhering to legislation and ensuring data compliance.

On successful completion of this unit students will be able to investigate the value of data for decision making to both end users and organisations, compare how different industry leading tools and software solutions are used to analyse and visualise data, carry-out queries to summarise and group a given data set and analyse the challenges faced when building ethics into a data-driven culture. Students will have the opportunity to progress to a range of roles within the digital sector, and will develop industry-led skills, analysis, and interpretation, which are crucial for developing practical experiences with big data and gaining employment.

Learning Outcomes

By the end of this unit, students will be able to:

LO1 Examine data visualisation for decision making of complex data sets

LO2 Discuss statistical and graphical tools and techniques used to present big data for a given use case

LO3 Demonstrate statistical and graphical techniques used to present big data as a visualisation

LO4 Investigate the challenges faced by data professionals in carrying out their role.

Essential Content

LO1 Examine data visualisation for decision making of complex data sets

Big Data:

Explore common fundamental concepts e.g. Bayesian classification, rule-based classification, The ‘Vs’ of big data (Volume, Velocity, Variety, Variability, Veracity, Visualization, and Value).

Big data lifecycle to include purpose, capturing data, searching and filtering, retrieving data for processing, combining multiple data sources, validation and cleansing, visualisation, analysis and querying, utilisation and storage, obsolete and deleted data.

Visualisation:

Identify the target audience needs, e.g. context, reporting, dissemination, accessibility, breadth of data, depth of analysis.

Explain the phases of data visualisation design process to include formulating the brief, working with data, establishing editorial thinking and developing design solution.

Apply principles of good design to data visualisation e.g. Dieter Rams’ Ten Principles for Good Design, Gestalt principles of visual perception and Pareto Chart.

Evaluate effective visual elements e.g. charts, graphs, plots, tables, points, lines, bars, area, maps, narratives, metaphors, symbols and aesthetics e.g. position, size, shape, colour and transparency.

Data for decision making:

Explore processes of data driven decision making (DDDM) e.g. define objective, establish hypothesis, identify data need, build data process, sampling methods, collect data, analyse data, interpret results and make decision.

The role of the Data Analysis Lifecycle as part of DDDM (e.g. Discovery, Data preparation, Model Planning, Model Building, Operationalise, Communicate results).

Discuss the advantages of data driven decision-making e.g. continuous improvement and planning, collaborative decisions, reduce costs, real-time insights and new opportunities, digital literacy and data-driven cultures.

Challenges e.g. inconsistent and unstandardised data, aligning decision making with business strategy, bias and discrimination, descriptive vs. predictive trends and probabilities.

LO2 Discuss statistical and graphical tools and techniques used to present big data for a given use case

Statistical and graphical techniques for big data analysis and visualisation:

Analyse and apply big data analytics techniques taking account of different data structures and database designs e.g. descriptive, prescriptive, diagnostic and predictive analytics.

Apply principles of mathematics and statistics for analysing data sets.

Explore the various kinds of analysis techniques e.g. anomaly detection, cluster, association by rule, classification and regression analysis.

Examine how to organise semi-structured and unstructured data variety e.g. word-cloud visuals, data catalogue, taxonomies and ontologies.

Forecasting estimates of future values e.g. applied forecasting and decision tree algorithms.

Industry leading tools and software solutions to analyse data:

Apply tools to analyse data e.g. programming or scripting languages such as Python or R and associated libraries, Application Programming Interfaces (APIs).

Industry leading tools and software solutions to visualise data:

Apply leading tools to a solution e.g. Microsoft Excel, Tableau, PowerBI and Azure, AWS, Oracle Visual Analyzer, Qlikview, Canvas, SAS Visual Analytics.

Explore how user experience and domain context influences approaches to data analytics and visualisation.

LO3 Demonstrate statistical and graphical techniques used to present big data as a visualisation

Manipulating data:

Construct activities using industry software to manipulate data e.g. importing datasets, data cleansing, data frame manipulation, testing and training a model, summarising analysis process and steps taken.

Apply query basics e.g. reports, calculate aggregate statistics, use built-in functions summarising and grouping data.

Explore advanced data manipulation and automation concepts e.g. generalised linear models and regression, multilevel modelling and techniques, data pipelines, machine learning and deep reinforcement learning (DRL).

Prepare visual presentations:

Visual presentations to include using insight analysis to understand data in context, selecting visual elements and aesthetic design e.g. find and filter content in dashboards, view and export data from dashboards to create report, presentation or infographic.

Data set requirements:

Understanding the data and its context including summary of data collection, sampling procedures and data type; stakeholder requirements, interests and needs.

LO4 Investigate the challenges faced by data professionals in carrying out their role

Roles and responsibilities:

Explain roles in a data-driven industry e.g. data analyst, data scientist, data engineer, visualisation specialist, data administrator, business analyst, middle-managers and senior management teams.

Explore the responsibilities of a data specialist e.g. preparing, analysing, modelling, managing and visualising data, and storage and access rights.

Strategies to ensure data compliance:

Explain organisational data architecture, policies, standards and rules e.g. how data is stored, managed, used and disseminated.

Assess data protection, informed consent and privacy issues for compliance e.g. personally identifiable information, protected health information, General Data Protection Regulation (GDPR) rights obligations, enforcement and regulatory legal penalties.

Explore and select the most appropriate industry compliance management software tools e.g. Microsoft Compliance Manager, AWS Compliance, IBM DataOps.

Identify and escalate quality risks in data analysis with suggested mitigation or resolutions as appropriate.

Challenges for data specialists:

Understand challenges such as applying data governance framework to ensure value of outcomes, accountability, trust, collaboration, transparency, risks and security, and role of the data steward.

Explain how to guard from poor practice e.g. cherry picking, disclosure of assumptions, conflict of interest, bias from single view and/or choice of technique.

Risks and challenges to combing data from different sources in data analysis activity.

Develop ethics into a data-driven culture and joining community of good practice e.g. Data for Good Exchange (D4GX); Fairness, Accountability and Transparency in Machine Learning group (FAT/ML), Data Ethics Framework (gov.uk).

Learning Outcomes and Assessment Criteria

Recommended Resources

Textbooks

DIETEL, P. (2020) Intro to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and The Cloud. London: Pearson.

FRANKS, B. (2020) 97 Things About Ethics Everyone in Data Science Should Know. USA: O’Reilly Media.

GRAESSER, L. and KENG, W.L. (2020) Foundations of Deep Reinforcement Learning: Theory and Practice in Python. London: Addison-Wesley Professional.

KIRK, A. (2019) Data Visualisation: A Handbook for Data Driven Design. London: Sage Publications.

KNAFLIC, C. N. (2015) Storytelling with Data: A Data Visualization Guide for Business Professionals. USA: John Wiley & Sons.

LOUKIDES, M., MASON, H. and PATIL, D.J. (2018) Ethics and Data Science. USA: O’Reilly Media.

MARR, B. (2017) Data Strategy: How to Profit from a World of Big Data, Analytics and the Internet of Things. London: Kogan Page.

McCORMICK, K., and SALCEDO, J. (2017) SPSS Statistics for Data Analysis and Visualization. USA: John Wiley & Sons.

ROSS, J. (2019) Data Science Foundations Tools and Techniques: Core Skills for Quantitative Analysis with R and Git. London: Addison-Wesley Professional.

VIESCAS, J.L. (2018) SQL Queries for Mere Mortals: A Hands-On Guide to Data Manipulation in SQL. 4th edn. London: Addison-Wesley Professional.

WILKE, C.O. (2019) Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures. USA: O’Reilly Media.

Links

This unit links to the following related units:

Unit 4: Database Design and Development

Unit 8: Data Analytics

Unit 24: Advanced Programming for Data Analysis

Unit 33: Applied Analytical Models.

Answer

Request Answer of this Assignment