Developing Data Science Projects

SOST30041 Developing Data Science Projects 20 Semester 1 Undergraduate Level 3 Simon Thomas Rudkin Unit coordinator Social Statistics 1 FHEQ level (Framework for Higher Education Qualifications) ' Last part of a Bachelors ' European Credit Transfer & Accumulation System Rating : 10.0 This module equips students with the ability to develop research questions, identify appropriate data science methodologies to address research questions, and to acquire data to support their analysis. By the end of the module, a toolkit will be in place to enable the conducting of a larger research project. An introduction will be provided to cutting edge data science techniques. Throughout the module, emphasis will be placed on the importance of explainability and reproducibility in data science projects.  This module equips students with the ability to develop research questions, identify appropriate data science methodologies to address research questions, and to acquire data to support their analysis. By the end of the module, a toolkit will be in place to enable the conducting of a larger research project. An introduction will be provided to cutting edge data science techniques. Throughout the module, emphasis will be placed on the importance of explainability and reproducibility in data science projects.  The unit aims to:Prepare students to conduct an independent data science research project. Preparation comes through the design of research, the development of a research proposal, and exposure to further tools that can enable the conduct of a data science project. Students should be able to:Recognise the importance of reproducibility in data science research.  Evaluate appropriate data science methodologies for research projects.  Appreciate the advantages and disadvantages of tools for exploratory data analysis. Students should be able to:Critically evaluate academic literature in supporting research project design. Design data pre-processing for data science projects. Students should be able to:Produce a GitHub site for replication resources. Develop data science project proposals. Source appropriate data from multiple repositories. Students should be able to:Formulate research designs to address data science problems. Communicate data science research to non-specialist audiences. Syllabus (indicative curriculum content):The following topics will be covered:Developing research questions  Conducting literature reviews  Research philosophies  Exploratory data analysis techniques  Data visualisation  Producing research proposals  Topics in contemporary data science (2-weeks)The topics in contemporary data science will reflect the latest research being done by social scientists using data science in their work.   Lectures are used to introduce students to the process of conducting research projects in Data Science. Sessions will include methodologies, presentation skills, reproducibility in research, and the requisite information for the successful completion of the project in the 2nd Semester. Each weekly lecture will be 2-hours.Workshops provide opportunities to put the content of the lectures into practice. Sessions will be divided between the implementation of codes associated with the research methods, development of research proposals, presentations and group discussions on research topics. 2-hour workshops will be held every week.Drop-in sessions offer support to students as they develop their individual research proposals. There will be a total of 4 2-hour sessions.  0 Other 100% GitHub site for exploratory data analysis - 500 words (10%)Exploratory Data Analysis for non-specialist audience - 1,000 words (30%)Individual Research Proposal - 2,500 words (60%) Via Turnitin after submission. BA(Hons) Econ & Soc Std BAEcon (DS & Econ) Third Year Optional N Axtell, R. L., & Farmer, J. D. (2025). Agent-based modeling in economics and finance: Past, present, and future. Journal of Economic Literature, 63(1), 197-287.  Bail, C. A. (2024). Can Generative AI improve social science?. Proceedings of the National Academy of Sciences, 121(21), e2314021121.  D'ignazio, C., & Klein, L. F. (2023). Data feminism. MIT press.Foster, I., Ghani, R., Jarmin, R. S., Kreuter, F., & Lane, J. (Eds.). (2020). Big data and social science: Data science methods and tools for research and practice. CRC Press.Grimmer, J., Roberts, M. E., & Stewart, B. M. (2021). Machine learning for social science: An agnostic approach. Annual Review of Political Science, 24(1), 395-419.  Lectures 16 Practical classes & workshops 16 150