Introduction to Data Science: Big Data Analytics in Business
| Code | School | Level | Credits | Semesters |
| BUSI3122 | Nottingham University Business School China | 3 | 10 | Autumn China |
- Code
- BUSI3122
- School
- Nottingham University Business School China
- Level
- 3
- Credits
- 10
- Semesters
- Autumn China
Summary
PRE-REQUISITE
Please note that for this module, the following pre-requisites apply:
Pre-requisite: BUSI2189 Introduction to Business Programming, or equivalent modules
(Please ignore Requisites: N/A below)
This module contains:
• Introduction: Data-Analytic Thinking
• Business Problems and Data Science solutions
• Predictive modelling
• Fitting models to data, and overfitting issues
• Similarity, Neighbours and Clusters
• Decision Analytics Thinking
• Big Data Visualization
• Data Science and Business Strategy
• Launching your Big data journey and Action
Re-assessment format is decided by the school.
Target Students
Part II (Year 4) students with the required pre-requisites.Available to JYA/Erasmus students.
Classes
- One 1-hour-30-minute lecture each week for 11 weeks
- One 1-hour computing each week for 3 weeks
Assessment
- 30% Coursework 1: Group Coursework (1500 words)
- 70% Exam 1 (1-hour-30-minute): one 1.5 hour examination
Assessed by end of autumn semester
Educational Aims
Approach business problems data-analytically. Think carefully and systematically about whether & how data can improve a particular application (e.g. Google Adwords), to understand a phenomenon (e.g. Why TARGET set up a data team?) better and especially to make better-informed decisions and automated decisions.Learning Outcomes
•The dynamic and changing nature of business and the consideration of the future of organisations within the global business environment, including the management of risk
•The development, management, application and implementation of information systems and their impact upon organisations
•The ability to analyse and evaluate a range of business data, sources of information and appropriate methodologies, which includes the need for strong digital literacy, and to use that research for evidence-based decision-making
•Numeracy and quantitative skills to manipulate data, evaluate, estimate and model business problems, functions and phenomena
•Commercial acumen based on an awareness of the key drivers for business success, causes of failure and the importance of providing customer satisfaction and building customer loyalty
•Self-management and a readiness to accept responsibility and flexibility, to be resilient, self-starting and appropriately assertive, to plan, organise and manage time
•Articulating and effectively explaining information
Conveners
- Dr Joseph Yu