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Module ASB-1114:
Business Analytics

Module Facts

Run by Bangor Business School

20 Credits or 10 ECTS Credits

Semester 1 & 2

Organiser: Dr Gwion Williams

Overall aims and purpose

This is a foundation course in business analytical skills which includes statistical methods, data collection and interpretation, probability distributions, hypothesis formulation and testing, correlation and regression analysis. It focuses on developing these skills in Microsoft Excel.

Course content

Manipulation of algebraic expressions, collection and presentation of data, producing descriptive statistics, measuring uncertainty using probability, estimating confidence intervals, hypothesis testing and investigating association and causality.

Assessment Criteria

C- to C+

C- to C+ (50-59%): Much of the relevant information and skills mostly accurately deployed. Adequate grasp of theoretical/conceptual/practical elements. Fair integration of theory/practice/information in the pursuit of the assessed work's objectives. Some evidence of the use of creative and reflective skills.


A- to A+ (70%+): Outstanding performance. The relevant information accurately deployed. Excellent grasp of theoretical/conceptual/practice elements. Good integration of theory/practice/information in pursuit of the assessed work's objectives. Strong evidence of the use of creative and reflective skills.


D- to D+ (40-49%): No major omissions or inaccuracies in the deployment of information/skills. Some grasp of theoretical/conceptual/practical elements. Integration of theory/practice/information present intermittently in pursuit of the assessed work's objectives.


B- to B+ (60-69%): Very good performance Most of the relevant information accurately deployed. Good grasp of theoretical/conceptual/practical elements. Good integration of theory/practice/information in pursuit of the assessed work's objectives. Evidence of the use of creative and reflective skills.

Learning outcomes

  1. Derive and manipulate algebraic equations, solve simultaneous linear equations and quadratic equations,

  2. Describe and summarise data using tables, graphs and measures of central tendency and dispersion.

  3. Analyse data, and interpret the results.

  4. Estimate and specify confidence intervals for a mean and variance.

  5. Evaluate probabilities involving the Binomial, Poisson and Normal distributions.

  6. Investigate patterns of association and causality using correlation and regression analysis.

  7. Apply the methods of statistical inference to formulate and test hypotheses.

Assessment Methods

Type Name Description Weight
CLASS TEST S1 On-line tests 17.5
CLASS TEST S2 On-line tests 17.5
EXAM Semester 1 exam 32.5
EXAM Semester 2 Exam 32.5

Teaching and Learning Strategy


One 2-hour lecture per week, which will include practical demonstrations and worked examples to try in class.


Two-hour drop-in workshop every fortnight. 10-hours per semester. This session provides further support for students. Attendance is non-compulsory.

Private study

Review and reflect upon the course material and practice the applications of techniques in Excel


Transferable skills

  • Numeracy - Proficiency in using numbers at appropriate levels of accuracy
  • Computer Literacy - Proficiency in using a varied range of computer software
  • Self-Management - Able to work unsupervised in an efficient, punctual and structured manner. To examine the outcomes of tasks and events, and judge levels of quality and importance
  • Exploring - Able to investigate, research and consider alternatives
  • Information retrieval - Able to access different and multiple sources of information
  • Inter-personal - Able to question, actively listen, examine given answers and interact sensitevely with others
  • Critical analysis & Problem Solving - Able to deconstruct and analyse problems or complex situations. To find solutions to problems through analyses and exploration of all possibilities using appropriate methods, rescources and creativity.
  • Self-awareness & Reflectivity - Having an awareness of your own strengths, weaknesses, aims and objectives. Able to regularly review, evaluate and reflect upon the performance of yourself and others

Subject specific skills

  • Quantification and design. Data, and their effective organisation, presentation and analysis, are important in economics. The typical student will have some familiarity with the principal sources of economic information and data relevant to industry, commerce, society and government, and have had practice in organising it and presenting it informatively. This skill is important at all stages in the decision-making process.
  • Numeracy: the use of quantitative skills to manipulate data, evaluate, estimate and model business problems, functions and phenomena.


Resource implications for students

No resource implications

Talis Reading list

Reading list

  • Curwin, J., Slater, R. and Eadson, D. (2015). Quantitative methods for business decisions, seventh edition. Cengage Learning.
  • Newbold, P., Carlson, W.L. and Thorne, B. (2013). Statistics for Business and Economics, 8th ed. Harlow: Pearson.

Courses including this module

Compulsory in courses:

Optional in courses: