Principles of Data Science

SOST10031 Principles of Data Science 20 Semester 1 Undergraduate Level 1 Todd Hartman Unit coordinator Social Statistics 1 FHEQ level (Framework for Higher Education Qualifications) ' First part HE study/Bachelors ' European Credit Transfer & Accumulation System Rating : 10.0 This unit introduces the concepts and practices of data science with a focus on applications in economics and the social sciences. Students will learn about the ‘data science lifecycle’, its statistical and computational foundations, and consider ethical issues such as bias, uncertainty, and privacy. Practical training covers programming, data management, linkage, and visualisation. Emphasis is placed on reproducibility and communicating findings through clearly written reports. The unit equips students with essential digital skills and foundations for further study in data science.  This unit introduces the concepts and practices of data science with a focus on applications in economics and the social sciences. Students will learn about the ‘data science lifecycle’, its statistical and computational foundations, and consider ethical issues such as bias, uncertainty, and privacy. Practical training covers programming, data management, linkage, and visualisation. Emphasis is placed on reproducibility and communicating findings through clearly written reports. The unit equips students with essential digital skills and foundations for further study in data science.  This unit introduces students to the core concepts, methods, and practices of data science, with a particular emphasis on applications in economics and the social sciences. Students will explore the historical and intellectual roots of data science, from early statistics to contemporary developments in generative AI, automation, and citizen data science.  The unit develops both conceptual understanding and practical skills. Students will learn about the ‘data science lifecycle’, statistical and computational foundations, and ethical considerations such as uncertainty, bias, and privacy. Practical sessions will provide hands-on experience with data management and linkage, as well as programming tools for cleaning, analysing, and visualising data. Alongside technical training, the unit emphasises data as narrative: students will practice communicating findings through clear visualisations and reports that are tailored to different audiences. By the end of the unit, students will gain knowledge of different data sources, reproducible workflows, and how data can be used to support evidence-based arguments relevant to economic and social questions. This unit equips students with essential digital literacy and transferable skills, preparing them for advanced study in data science, applied economics, and interdisciplinary social research.  Students should be able to:Describe and explain the foundations of (social) data science, including the data science lifecycle, statistical and computational principles, and ethical considerations of using data. Students should be able to:Understand the quality, structure, and appropriateness of different data sources and workflows for answering economic and social questions. Students should be able to:Apply programming tools to manage, analyse, and visualise data in a reproducible manner. Students should be able to:Communicate data-driven insights effectively through clear visualisations and written reports tailored to different audiences. Syllabus (indicative curriculum content):Introduction: What Is (Social) Data Science?  Key concepts and definitions; disciplinary roots; historical development from early statistics to the ‘Big Data’ era and beyondThe Data Science Workflow  Why ‘lifecycle’ thinking matters; workflow mappingStatistical Foundations  Exploratory data analysis (and what it is not); theory testing vs classificationComputational Foundations  Algorithms, automation, and reproducibilityDatabase Management and Data Linkage  Relational databases, SQL basics, and principles of combining data from multiple sourcesProgramming Primer  Object-oriented programming basics; introduction to generative AI tools for coding assistanceReporting, Data as Storytelling  Crafting compelling narratives with data; context and audienceData Visualisation  Principles of effective design; avoiding misleading graphics; accessibility in visualisationEthical Science  Uncertainty, bias, fairness, and GDPRData Science in Practice & Future Directions  Industry applications; trends: simulation, automation, and citizen data science    The unit is delivered through a combination of lectures, seminars and labs, and guided independent study (total of 200 hours).Lectures (10 hours): Weekly lectures introduce core concepts (based on the above lecture schedule), theoretical foundations, and key debates in (social) data science. They provide the intellectual framework and context for applied learning.Seminars/Labs (10 hours): Small-group sessions focus on hands-on practice with data science tools, data management and linkage, visualisation, and ethical and applied case studies. These interactive sessions allow students to apply lecture material, develop programming skills (with new technology like generative AI), and receive formative feedback (from tutors and auto-graded assignments).  Independent Study (180 hours): Students are expected to undertake guided independent study, including background reading, completion of weekly programming exercises not completed in class, preparation for seminars, and work on summative assessments. Independent study consolidates conceptual understanding and develops problem-solving and technical proficiency.Total: 200 hours  5 Portfolio 100% Written feedback will be provided within the standard feedback framework. 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