FIT1043 · Introduction to data science

Overview

This unit looks at processes and case studies to understand the many facets of working with data, and the significant effort in Data Science over and above the core task of Data Analysis. Working with data as part of a business model and the lifecycle in an organisation is considered, as well as business processes and case studies. Data and its handling is also introduced: characteristic kinds of data and its collection, data storage and basic kinds of data preparation, data cleaning and data stream processing. Curation and management are reviewed: archival and architectural practice, policy, legal and ethical issues. Styles of data analysis and outcomes of successful data exploration and analysis are reviewed. Standards, tools and resources are also reviewed.

Areas of study: Data science

Offerings

Assessment

The Handbook lists an examination for this unit.

This unit has threshold mark hurdles. You must achieve at least 45% of the available marks in the final scheduled assessment, at least 45% in total for in-semester assessments, and an overall unit mark of 50% or more to be able to pass the unit. If you do not achieve the threshold mark, you will receive a fail grade (NH) and a maximum mark of 45 for the unit.

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

Learning outcomes

1. Detail the phases of the data science lifecycle and differentiate the roles involved in a data science project.
2. Implement strategies for acquiring, cleaning, and organising data prior to analysis.
3. Utilise basic data analysis models to extract insights and critique their effectiveness.
4. Understand fundamental properties of Big Data and their influence on storage and processing and evaluate the strengths and weaknesses of Big Data tools for specific contexts.
5. Examine data science projects by identifying and discussing inherent ethical, privacy, and data management issues, including their broader impacts.
6. Apply commonly used data science software and programming languages to interpret results across a diverse range of scenarios.

Workload

Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled online and face to face learning activities and independent study. Independent study may include associated reading and preparation for scheduled teaching activities.

Student experience, not official rules. Nothing here changes what the Handbook says.