36103 Statistical Thinking for Data Science

SUBJECT OUTLINE

36103 Statistical Thinking for Data Science

Subject description

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 statistical 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.

Subject learning objectives (SLOs)

Upon successful completion of this subject students should be able to:

1. Manage the complexity of real data science projects and their inevitable compromises
2. Formulate authentic data science questions precise enough to be answered by valid statistical techniques
3. Justify the use of different statistical concepts and tools to audiences from a wide range of backgrounds
4. Find, clean, and merge datasets from a range of sources to answer real world data science problems
5. Apply statistical methods that are appropriate to a dataset and stakeholder requirements
6. Interpret the results of a statistical analysis correctly, visualizing and reporting upon them in ways that create value for, and are sensitive to the needs of, a wide range of stakeholders
7. Collaborate with and contribute to the professional community of data scientists, both local and global

Course intended learning outcomes (CILOs)

This subject also contributes specifically to the development of the following course outcomes:

Exploring and testing models and describing behaviours of complex systems

Explore and test models and generalisations for describing the behaviour of sociotechnical systems and selecting data sources, taking into account the needs and values of different contexts and stakeholders (1.2)

Making the invisible visible

Use transdisciplinary approaches to seeing and doing to uncover underrepresented, or misrepresented, elements of a system (1.4)

● Exploring, interpreting and visualising data

Explore, analyse, manipulate, interpret and visualise data using data science techniques, software and technologies to make sense of data rich environments (2.2)

. Designing and managing data investigations

Apply and assess data science concepts, theories, practices and tools for designing and managing data discovery investigations in professional environments that draw upon diverse data sources, including efforts to shed light on  underrepresented components (2.4)

Developing strategies for innovation

Explore, interrogate, generate, apply, test and evaluate problem-solving strategies to extract economic, business, social, strategic or other value from data (3.1)

Working together

Develop a collaborative and team-oriented mindset to harness value for stakeholders to produce innovative solutions to challenges (3.3)

Engaging audience

Explore and craft interpretative narratives that engage key audiences with data analytics and potential significance for action, at a societal, industrial, organisational, group or individual levels (4.2)

· Informing decision making

Develop, test, justify and deliver data project propositions, methodologies, analytics outcomes and recommendations for informing decision-making, both to specialist and non-specialist audiences (4.3)

Contribution to the development of graduate attributes

Your experiences as a student in this subject support you to develop the following graduate attributes (GA):

GA 1 Sociotechnical systems thinking

GA 2 Creative, analytical and rigorous sense making

GA 3 Create value in problem solving and inquiry

GA 4 Persuasive and robust communication

Teaching and learning strategies

Authentic problem based learning: This subject relies heavily upon the principle that students learn best by doing. It offers a range of authentic data science problems to solve that help to develop students’ statistical thinking about complex problems. Students work on real world data analysis problems using datasets that they create using modern data harvesting techniques. These are used to answer realistic data science questions in broad areas of topical interest. This exposes them to the true ambiguities, constraints, and complexities of working as a data scientist for a variety of different stakeholders.

Blend of online and face to face activities: This subject is offered through a series of block sessions blending online with face-to-face learning. Students interact face-to-face with each other and the teaching team in three intensive modules that require the completion of both preparation and after class activities. They concurrently use a range of complementary online resources to develop their statistical thinking according to identified weaknesses in their background knowledge. They are expected to engage in online discussion and to actively participate in other blended activities.

Collaborative work: We place a strong emphasis on group activities and collaboration in diverse teams. As a data science professional you need to approach professional projects and challenges by working with people from different  backgrounds, expectations, and expertise. This course simulates that environment by requiring students to work with a team of peers who come from many different backgrounds. Group assessments help students to develop effective strategies for working as a part of a data science team, as well as an appreciation that there are diverse perspectives on many different topics in data science and innovation.

Self paced evaluation and improvement: This subject takes students from an exceptionally wide range of backgrounds, some of who are better versed in statistical methods, and Python, than others. We help all students to self-diagnose their weaknesses and strengths, and to work to improve in areas that they identify as a priority for the professional niche that they would like to occupy as a practicing data scientist. Students choose their own path through a wide variety of curated resources as needed.

Embedding English Language: An aim of this subject is to help you develop academic and professional language   and communication skills in order to succeed at university and in the workplace. To determine your current academic language proficiency, you are required to complete an online language screening task, OPELA (information available at https://www.edu.au/research-and-teaching/learning-and-teaching/enhancing/language-and-learning/about-opela-student If you receive a Basic grade for OPELA, you must attend additional Language Development Tutorials (each week from    week [3/4] to week [11/12] in order to pass the subject. These tutorials are designed to support you to develop your language and communication skills. Students who do not complete the OPELA and/or do not attend 80% of the Language Development Tutorials will receive a Fail X grade