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Programming for Data Science G (11521.1)

Level: Graduate Level
Credit Points: 3
HECS Bands: 2
Faculty: Faculty of Science and Technology
Discipline: Academic Program Area - Technology


Unit Outlines

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  • Semester 2, 2019, ON-CAMPUS, BRUCE (192447) - View

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Beyond the foundational knowledge of using R for data science, data scientists need to be proficient in valuable programming tools and languages that form the basis of advanced analysis and modelling techniques. Using real-world data sets and a problem-solving framework, students in this unit will learn industry-standard programming skills and best practises for data organisation, data manipulation, data interrogation and data modelling relevant to data science. Students will develop a sound understanding of technical programming in one or more languages and related computational modelling and analysis tools. This includes skills in software engineering, such as writing correct code, working with version control tools, testing and debugging. A particular emphasis will be on tackling real-world data science problems as found in industry.

Learning Outcomes

After successful completion of this unit, students will be able to:

1. Learn fundamentals of programming in Python and/or other relevant languages;

2. Develop a sound understanding of the most common libraries and tools;

3. Learn how to connect to large databases;

4. Demonstrate strong skills in good software engineering practices, such as writing correct code, testing and debugging, based on industry standards and best practices;

5. Learn how to use version control tools;

6. Critically reflect on available computational tools for solving data science problems; and

7. Successfully demonstrate practical skills in developing solutions to data science problems.

Assessment Items



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