Data Science for Business

Overview

This unit provides students with a practical foundation in contemporary data science techniques used in industry, using Python. It offers a hands-on introduction to programming paradigms and fundamental techniques for financial data analysis. Key topics include data cleaning and validation, data transformation, algorithm design, text analytics, and data visualisation. Real-world case studies and datasets from finance and economics will be used to illustrate how data science supports decision-making and market analysis. Students will gain hands-on experience with widely used Python libraries such as Pandas, Matplotlib, and the Natural Language Toolkit (NLTK).

Unit learning outcomes

Students who successfully complete this unit will be able to:

1. Apply coherent and advanced knowledge of how to read, clean, and manipulate data sets.
2. Critically evaluate existing toolkits, and learn how to construct custom algorithms when necessary.
3. Identify research questions and create project outlines that use data science to support decision making process
4. Analyse data sets using statistical techniques, visualisations, regression analysis, and text analytics to derive insights and support data-driven financial decision-making

Teaching methods

Hawthorn

Assessment

Content

• Basic programming theory
• Data science best practices
• Data structures, access and usage
• Data cleaning and validation
• Data Visualisation
• How to validate results
• Working with Text data (Text Analysis)
• Data science tools