Summary of content

The module provides a comprehensive introduction to the principles and practices of business analytics, equipping students with foundational knowledge and applied skills essential for data-driven decision-making in modern organisations. Beginning with an overview of descriptive and diagnostic analytics, students will explore key analytical methodologies and their relevance across business contexts.

The module covers foundation topics such as data management, statistical analysis, probability distributions, hypothesis testing, and regression techniques. It progresses to more advanced areas including time series forecasting, multivariate analysis, and model evaluation. Throughout, students will gain experience with statistical software tools and develop the ability to interpret and communicate analytical findings effectively. The module concludes with a focus on data presentation techniques (storytelling), ethical considerations, and aligning analytics with strategic business goals, ensuring that students are well-prepared to apply analytical insights in real-world decision-making environments.

As stated, the module deals with descriptive and diagnostic analytics:

Descriptive analytics serves as the starting point of the analytics journey, focusing on summarising and interpreting historical data to understand what has happened in the past. It involves techniques such as data aggregation, visualisation, and summary statistics to provide insights into past performance, trends, and patterns. Descriptive analytics helps businesses on a foundational understanding of their operations, customer behaviour, and market dynamics, laying the groundwork for more advanced analytics techniques.

Diagnostic analytics builds upon descriptive analytics by delving deeper into data to understand why certain events or outcomes occurred. It involves analysing relationships, correlations, and causal factors within data to uncover insights into the root causes of specific outcomes or issues. D

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