We are in the middle of a data revolution. A new laptop can run processes impossible for a supercomputer just a few generations ago. The internet makes data collection and distribution easier and cheaper than ever, with terabytes of information on consumer behaviour, public transport use, crime statistics and election results sourced from across the world now available anywhere in seconds.
These advances in modern computing allow us to begin answering important questions about the world, including when disease outbreaks are expected, why certain choices were made by voters during elections, and whether individuals convicted of serious crimes are likely to reoffend.
Data science provides the tools to take advantage of these possibilities, allowing us to investigate and make sense of the world. The ability to use these tools to conduct high quality data analysis is becoming an increasingly important skill, sought after by employers in industry, government and the not-for-profit sector.
Taught by quantitative political scientists from the United States Studies Centre and data scientists from the Centre for Translational Data Science, Data Analysis in the Social Sciences is one of the only subjects in Australia training students to use data science to address real questions in the social world.
Throughout this course we will be showing you how to program in the open source statistics software R. We will provide you with training in many of the tools of data science, as they are used in politics, economics and finance, criminology and public health. Our focus is on application, not theory. We will give you a critical understanding of the strengths and weaknesses of quantitative research, and help you acquire practical skills using different methods and tools to answer important questions.
Techniques covered range from descriptive statistics, linear and logistic regression, the analysis of data from randomised experiments, model selection for prediction and classification tasks and spatial modelling. We will not only teach you how to use these methods, but how to use them to conduct applied research: how to ask and answer important questions, how to present your data, and ways to communicate your findings.
Seminars will consist of lecture-style discussions on the problems we seek to solve problems in economics, journalism, industry, academia and government, and the methods we can use to do this; and lab sessions built around small-group projects, with students working on real-life problems and data.
If you are interested in taking this unit but unsure whether you have the necessary assumed knowledge, please contact the unit coordinator, Shaun Ratcliff: firstname.lastname@example.org
Learning outcomes are the key abilities and knowledge that will be assessed in this unit. They are listed according to the course goal supported by each.
1. Students will be well-versed in the various ethical issues and professional standards around the gathering of data.
2. During the unit, students will be required to deliver a small scale group project. Students will be proficient in the delivery of a small-scale project, and the management of the project from initial conception to delivery to evaluation.
3. Upon completion, students will be able to present data and reports of a high standard.
4. Students will be trained in the autonomous collection, collation, assessment and comparison of data from multiple sources, such as the Australian Bureau of Statistics and the Australian Data Archive. Students will be able to discern the quality of data to a minute level, and be able to draw a broad range of insights from data of various degrees of statistical significance.
5. Students will be trained in the sophisticated application of established data analytical methodology. Students will be expected to have a medium degree of proficiency in methodological procedures, and will be tasked with complex problems specifically related to the social sciences.
6. Students will be utilising industry-leading concepts and frameworks in their pedagogy. Students will be directing formidable amounts of data for protracted, complex insights into areas such as polling data and demography.
7. Students will be expected to apply their theoretical understanding of statistical methods to practical problems around data gathering methodology, statistical significance and sample sizing. Students will be expected to autonomously create basic design frameworks for statistical modelling problems.
Taught in Semester 1, 2022
1 x 1 hour lecture per week
1 x 2 hour lab per week
3 class tests (weeks 5, 9 and 13; worth 5% of the grade each, 15% in total)
Group work (over six weeks, worth 30%)
Research plan (due week 6, worth 15%)
Research project (due after week 13, worth 40%)
Visit the University of Sydney website for information about fees, cross-institutional and non-award study, and more.