Overview
Course overview
Statistics for Business gives managers, executives and aspiring analysts the practical statistical knowledge needed to interpret data, identify trends and make informed decisions. The course avoids unnecessary mathematical complexity and focuses on understanding and applying statistical concepts in realistic business scenarios, from reading dashboards to interpreting research findings and financial data.
What you will study
Build a working understanding of descriptive statistics, including measures of central tendency and dispersion, frequency distributions and the graphical tools used to summarise and communicate data. Learn how to apply these techniques to real business data sets.
Develop a practical understanding of probability as a tool for business decision-making under uncertainty. Covers basic probability rules, conditional probability, expected value calculations and how probability concepts underpin risk assessment and scenario planning.
Learn how to select and create charts, graphs and dashboards that communicate data accurately and compellingly to a business audience. Covers the principles of effective data visualisation, the most common visualisation mistakes and how to choose the right chart type for each analytical purpose.
Develop the ability to build simple regression models and use them to forecast business outcomes. Covers the logic of linear regression, how to interpret regression outputs, the assumptions that must hold for the model to be valid and how to communicate regression-based forecasts to a non-technical audience.
Learn the principles of statistical hypothesis testing and how to apply them to business questions. Covers the null and alternative hypothesis, significance levels, p-values, t-tests and chi-square tests, with a focus on interpreting results rather than manual calculation.
Apply statistical methods to real business problems across a range of functional areas, including marketing analytics, operations management, financial analysis and HR metrics. Develop the confidence to identify when statistical analysis is the right tool and to interpret the outputs correctly.
Develop critical skills for interpreting data in business contexts, including how to identify misleading statistics, how to assess the quality and reliability of data sources, how to distinguish correlation from causation and how to communicate analytical findings with appropriate confidence and caveats.
Learn how to build and use simple statistical models for business decision support, including how to select the right model for each analytical purpose, how to validate model assumptions and how to use model outputs to inform rather than replace managerial judgement.
Develop a working understanding of business intelligence tools and approaches, covering how BI systems collect and store data, how dashboards and reporting tools work and how to design BI outputs that give decision-makers the information they need in a format they can use.
Bring together statistical and analytical skills in the context of structured business decision-making. Learn how to frame decisions analytically, select and apply the right analytical methods, interpret outputs in context and present data-driven recommendations clearly and persuasively to a business audience.
Who is this for?
Business professionals, managers and students who need to work with data but have little or no statistics background. Also suited to learners preparing for diploma or degree-level study that includes quantitative modules.
Learning outcome
Learners will be able to read and interpret statistical information, apply basic analytical tools to business problems and communicate data-driven insights clearly. They will understand how statistics underpins business strategy, risk and performance measurement.
Assessment and delivery style
Sessions combine explanation with applied exercises using business data sets. Learners work through realistic problems and build a practical toolkit they can apply immediately. Assessment is through in-class exercises and a short analytical project.
