36103 Statistical Thinking for Data Science8cp; block: on-campus; 6 x Monday evenings, on-campus; 1 x Saturday workshops, on-campus; 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.
This subject teaches students the key skills and concepts to apply statistical thinking within an applied data science setting. Students start by being introduced to basic statistical concepts, develp programming skills in R, and start working with real world data. This is followed by learning a family of linear regression models, and then applying what they have learned to go through a full data science research cycle. Working in teams, students learn how to formulate research questions, answer them using formal statistics and real-world datasets, and communicate their findings both verbally and in report format. Students are then given the oppourtinuty to extend their team projects as individuals, using advanced methods to formulate and answer new research questions and submitting their findings in a technical scientific report.
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. However, as the term progresses, students increasingly move towards an individually-driven learning mode, providing first teams, and then individuals, the freedom to develop their statistical thinking and skills further.
By the end of the term students have a strong technical, conceptual, and practical foundation to continue their development as Data Scientists.
Autumn session, City campus
Detailed subject description.