Data Science and Strategic Decision Making
Subject code: MA5840:03
This subject is designed to provide an integrated knowledge for understanding and managing information resources, building basic business intelligent system, interpreting business statistics, and developing practical decision models. It will equip students with decision-making tools and illustrate their applications. Businesses typically collect large volumes of data with relative ease. However, data are often meaningless until they are analysed for trends, patterns and relationships and then become useful information. Acting on this useful information to develop solutions and support decision-making is a key business skill required by data scientists.
Software platform: various
- Critically evaluate data and information as an organizational resource and identify issues in managing data/information/knowledge when faced with uncertainty
- Identify how data is integrated as a strategy in different organisations based on strategic management theory
- Review and assess different information architectures to identify where strategic data resides in organizations
- Apply graphical and numerical tools for organising, analysing, interpreting, and presenting data in a balanced scorecard
- Apply analytical frameworks for organising, analysing, interpreting, and presenting data to formulate strategy and inform strategic decisions
- Run scenario-based models and frameworks for planning, decision making and control of business problems, interpret model results, and make recommendations to stakeholders
- To critically examine different viewpoints and synthesize different materials around trust, leadership and communication to understand how these subjects relate to power and influence in an organisation
- Reflect on knowledge learned of theory and business practices for future learning and ongoing professional development.
Assessment for this course will occur at various times across the seven-week study period. Tasks may include online quizzes, discussion board activity, portfolio development, case studies, reflection, literature reviews presentations and reports. Feedback will be provided to you throughout the study period as well as a final grade at the conclusion of the study period.
This is one of the interdisciplinary subjects studied in the online Master of Data Science.
Please note, course structure and content are subject to change. For information on all course subjects download the course guide.