36103 Statistical Thinking for Data Science8cp; block: on-campus; 6 x Monday evenings, on-campus; 1 x Saturday workshops, online; 2 x optional assessment support sessions, online; independent learning activities, online; availability: Master of Data Science and Innovation and Master of Business Analytics students
There are course requisites for this subject. See access conditions.
Statistical thinking is the foundational mindset in data science, emphasizing the use of statistical principles and methods to understand, analyze, and derive meaningful insights from data. It serves as the core of data science. This subject equips students with essential skills and concepts for applying statistical thinking in the context of applied data science. Initially, students are introduced to fundamental statistical principles, developing a simultaneous understanding of modern methods for statistical inference, and gaining valuable hands-on experience with real-world data. Subsequently, they delve into a range of parametric and non-parametric models and estimation techniques, applying their acquired knowledge to engage in a complete data science research cycle. Collaborating in teams, students learn how to formulate research inquiries, employ formal statistics and real-world datasets to address them, and effectively communicate their findings through both oral presentations and written reports.
The progression of this subject starts with more teaching-intensive methods such as workshops and lectures to give students the technical and conceptual know-how to work as practicing data scientists. As the subject progresses, students increasingly move towards an individually-driven learning mode, allowing both teams and individuals the flexibility to enhance their statistical thinking and skills.
Upon completion of the subject, students possess a robust foundation in technical, conceptual, and practical aspects, empowering them to continue their development as Data Scientists.
Autumn session, City campus
Detailed subject description.