The aim of the module is to give students of business administration a conceptual introduction to the field of business analytics, its many applications, and the role statistics plays in it. The module is designed to prepare students for the study of more advanced analytical materials in the future.

(LO1) Students will be able to analyse and interpret data, including variability, attribute etc., using graphs and summary statistics

(LO2) Students will be able to explain basic principles of sampling, including sampling error, and apply them to management contexts

(LO3) Students will be able to model data using standard probability distributions

(LO4) Students will be able to calculate and interpret a margin of error to place confidence limits on estimates

(LO5) Students will be able to analyse the relationship between quantitative variables using simple regression and correlation techniques

(LO6) Students will be able to calculate and interpret hypothesis tests for differences in means and proportions of sample data

(LO7) Students will be able to perform basic statistical functions in excel spreadsheets using standard commands and analysis tool

(S1) Team player

(S2) Numerate

(S3) Excellent written and verbal communicator

(S4) Problem solver

(S5) IT literate

Teaching Method: Lecture
Scheduled Directed Student Hours: 24
Attendance Recorded: Yes

Teaching Method - Seminar
Description: The seminar for this module will take place face-to-face. Students will be given real-world case studies to solve. The teaching team will provide the solutions to the case studies. These will generally be based on, and aligned with, the lectures
Scheduled Directed Student Hours: 6
Attendance Recorded: Yes

Teaching Method : Group Study
Description: Bi-weekly 1 hour session to foster student community and engagement by working with others on their ‘active learning’ activities
Scheduled Student Hours: 6
Attendance Recorded: No

Self-Directed Learning Hours: 114
Description: Participation in recordings and seminars is insufficient in order to maximize performance on this module. Students are expected to engage in regular self-directed learning. This should include attempting seminar exercises, data gathering and analysis, and good use of the suggested reading material.

Skills/Other Attributes Mapping

Skills / attributes: A problem solver
How this is developed: During the lectures, tutorials , and in the group project, students will solve problems such as how to construct a financial portfolio that provides a good balance of risk and return.
Mode of assessment (if applicable): In-semester tests and group project

Skills / attributes: Numerate
How this is developed: In the lectures, tutorials, and group project, students will be assigned tasks such as summarize quantitative and qualitative data of a company and prepare a managerial report describing the relationships between key variables.
Mode of assessment (if applicable): In-semester tests and group project

Skills / attributes: A team player
How this is developed: In the tutorials, students will work in teams to complete assigned tasks. They will also work together t o complete the group project. In doing so, they will understand the importance of teamwork, manage the interaction and relationships with other group members, gain experience in negotiation, persuasion, influencing and managing conflict.
Mode of assessment (if applicable): Group project

Skills / attributes: An excellent verbal and written communicator
How this is developed: By contributing to in-class discussions (lectures and seminars), and by preparing the group project, where they will need to communicate technical aspects of the subject in terms easily understandable by the non-specialist.
Mode of assessment (if applicable): In-semester tests and group project

Skills / attributes: IT literate
How this is developed: In tutorials and group project, students will use analytical software to study datasets and recommend managerial actions supported by the data. By using digital tools and specialist software to engage with the course material, to undertake additional research, and to communicate.
Mode of assessment (if applicable) In-semester tests and group project

This module will cover:
- Data and Statistics; visualizing data, categorical and quantitative data; descriptive measures, probability.
- The normal and other continuous distributions.
- Surveys and sampling; sampling distributions; confidence intervals for proportions.
- Confidence intervals and hypothesis tests for means.
- Correlation and linear regression.
- Inference for regression; understanding residuals.
- Control charts and improvement strategies