Introductory description

This module is only open to WBS undergraduate students, and some students taking joint degrees with WBS and another department. Students from other departments cannot take this module. To find detailed availability and to apply for this module, log in to my.wbs.ac.uk using your normal IT login details and apply via the my.wbs module application system. Once you’ve secured a place on my.wbs you should apply via your home department’s usual process, which usually takes place via eVision. Note that you do not require the module leader’s permission to study a WBS module, so please do not contact them to request it.

Data has become a ubiquitous in our personal and professional lives. This means that understanding the foundations of data analysis is paramount for developing critical thinking, excelling in a career or navigating the world of social and other media.

This introductory module covers the basic statistical concepts that everyone should muster before progressing to more advanced techniques. All concepts are showcased using real-world examples and implemented using Excel software. The concepts covered in the module start at the very beginning with exploring where data comes from (i.e., measurement and data sampling), progresses to how data is summarised (i.e., summary statistics and visualisation), and finally looks at how data is used to make conclusions about data (i.e., confidence intervals, hypothesis testing, and basic regressions).

The module has several aims: 1) introduce students to the foundational statistical concepts and help them master them both on theoretical and empirical level; 2) help students develop critical thinking skills when it comes to data analytics; 3) develop students’ technical skills using Excel software.

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.

Topics that are covered in the module:

What is data and how do we use it?
Summarizing data: descriptive statistics and data visualisation
Sampling data: sampling biases, uncertainty inherent to data analysis
The concepts of probability and probability distributions
Confidence intervals
Hypothesis testing
Introduction to regression analysis

Learning outcomes

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

• Identify the basic statistical concepts and specific techniques, recognizing the strengths and limitations of these methods and implementing them using real-world data.
• Examine the results of analysis and derive an inference supported by data.
• Examine how data analysis can be used to solve problems within modern business practices.
• Tackle real life quantitative problems and be able to convey results and discuss the issues involved.
• Demonstrate problem solving skills.

Indicative reading list

Siegel, A. (2016), "Practical business statistics", 7th edition, Academic Press. ISBN 9780128042502
Favero, L. P. (2019), "Data science for business and decision making", Academic Press. ISBN 9780128112175
Salkind, N. J. (2017), "Statistics for people who (think they) hate statistics", 6th edition, SAGE. ISBN 9781506361161
Spiegelhalter, D. (2019), “The Art of Statistics: Learning from Data”, Pelican. ISBN: 9780241398630

Subject specific skills

Apply basic quantitative tools, be aware of limitations.

Transferable skills

Use Excel for basic data analysis ranging from descriptive statistics to regression models.
Demonstrate critical and logical thinking.