Overview of Data Science Practice

Module code: MA7441

Data science is the process of extracting reliable insights and conclusions from data. Advances in computing power, statistical and computer science techniques and the ability to gather huge amounts of data from sensors and our online digital lives has made data science ubiquitous in all fields of human endeavour.

This module will provide a comprehensive overview of data science. We'll begin by defining data science and understanding its relationship to other disciplines that deal with data collection and analysis. You'll learn who engages in data science and the motivations behind their work. Most weeks we’ll hear from a practitioner in a different field, both from inside the University and beyond.

Beyond the fundamentals, we'll delve into the core techniques used in data science. We'll also address crucial considerations such as the limitations of data science, the concerns of many people about the impact of data science, and the ethical implications of working with large amounts of data.

Throughout the course, you'll develop practical skills. You'll be able to explain data science concepts clearly, design a systematic and creative process for data-driven problem-solving, and appreciate the role of statistics in this field. Additionally, we'll emphasize the importance of reproducibility, explainibility, and transparency within data science.

Furthermore, you'll learn how to identify and evaluate diverse data sources. We'll examine the various risks – including ethical ones – that are inherent in data science. You'll be equipped to recognize these risks in case studies and explore effective strategies for managing them. Finally, we'll address the sustainability challenges arising from data science practices.

Much of your time on this module will be spent on discussion and group work.