Introductory description

With the ever-growing mass of digital data available in every business, large amount of information remains hidden in the data. This information can now be extracted with the help of data analysis (descriptive analytics) to provide a sound and objective support for forecasting predictions (predictive analytics) and related decisions (prescriptive analytics) in business. Business analytics & visualization is the art and science of extracting information from business data and presenting it with suitable graphical tools to provide a sound and objective basis to the decision process.

Module web page

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

DESCRIPTIVE ANALYTICS - Introduction to basic business analytics with Excel; Data manipulation and analysis, statistical analysis
PREDICTIVE ANALYTICS - Forecasting Methods
PRESCRIPTIVE ANALYTICS - Linear programming

Learning outcomes

By the end of the module, students should be able to:

• Demonstrate a comprehensive understanding of the different forms of analytics and their respective use-cases. [CITP 2.1.2, 2.1.9]
• Critically evaluate business scenarios and determine the appropriate analytical solution [C3].
• Implement sophisticated analytical solutions - statistical analysis, time series analysis, optimisation, to solve a range of problems. [CITP 2.1.10]
• Communicate solutions effectively and efficiently to a critical audience of non-specialists. [CITP 2.3.2]

Indicative reading list

Sharda, R. (2018) Business intelligence: managerial perspective. Fourth, Global edition. Harlow, England: Pearson. Cox, D. R. and Donnelly, C. A. (2011) Principles of applied statistics. Cambridge: Cambridge University Press.
Ross, S. M. (2021) Introduction to probability and statistics for engineers and scientists. Sixth edition. London, United Kingdom: Academic Press.
Albright, S.C. & Winston, W.L. (2020) Business analytics: data analysis and decision making, Seventh edn, Cengage, Boston, MA.

View reading list on Talis Aspire

Subject specific skills

Students will be able to demonstrate a high competency level in:

K13: Principles of data analysis for digital and technology solutions.
K17: Reporting techniques, including how to synthesize information and present concisely, as appropriate to the target audience.
K34: Approaches to analytical and critical thinking to define business problems objectively and create value for the client.
K53: The barriers that exist to effective data analysis between analysts and their stakeholders and how to avoid or resolve these.
K58: How Data Analytics operates within the context of data governance, data security, and communications.
K59: How Data Analytics can be applied to improve an organisation’s processes, operations and outputs.
S13: Report effectively to colleagues and stakeholders using the appropriate language and style, to meet the needs of the audience concerned.
S48: Define Data Requirements and perform Data Collection, Data Processing and Data Cleansing.
S49: Apply different types of Data Analysis, as appropriate, to drive improvements for specific business problems.
S50: Find, present, communicate and disseminate data analysis outputs effectively and with high impact through creative storytelling, tailoring the message for the audience. Visualise data to tell compelling and actionable narratives by using the best medium for each audience, such as charts, graphs and dashboards.
S51: Identify barriers to effective analysis encountered both by analysts and their stakeholders within data analysis projects.
S54: Extract data from a range of sources. For example, databases, web services, open data.

Transferable skills

K54: How to critically analyse, interpret and evaluate complex information from diverse datasets.
K56: Sources of data such as files, operational systems, databases, web services, open data, government data, news and social media.
K57: Approaches to data processing and storage, database systems, data warehousing and online analytical processing, data-driven decision making and the good use of evidence and analytics in making choices and decisions.
S11: Determine and use appropriate data analysis techniques. For example, Text, Statistical, Diagnostic or Predictive Analysis to assess a digital and technology solutions.
S39: Recommend and use appropriate software tools to implement Business Analysis tasks and outcomes.
S52: Apply a range of techniques for analysing quantitative data such as data mining, time series forecasting, algorithms, statistics and modelling techniques to identify and predict trends and patterns in data.
S53: Apply exploratory or confirmatory approaches to analysing data. Validate and and test stability of the results.
S55: Analyse in detail large data sets, using a range of industry standard tools and data analysis methods.