Programming for Data Science G (11521.1)
|Faculty:||Faculty of Science and Technology|
|Discipline:||Academic Program Area - Technology|
UC - Canberra, Bruce
Year Teaching Period Convener Mode of Delivery 2020 Semester 2 DR Dat TRAN (Ph: +61 2 62012394 ) ON-CAMPUS
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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.
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.
UC - Canberra, Bruce
Semester 2, 2019
On-Campus Attendance (Expected) and Online Content (ON-CAMPUS)
- Week 2 Tutorial/Lab (2% )
- Week 3 Tutorial/Lab (2% )
- Online Test (5% )
- Week 5 Tutorial/Lab (2% )
- Week 6 Tutorial/Lab (2% )
- Week 7 Tutorial/Lab (2% )
- Data Science Programming Assignment - Part A (15% )
- Week 9 Tutorial/Lab (2% )
- Week 10 Tutorial/Lab (2% )
- Week 11 Tutorial/Lab (2% )
- Week 12 Tutorial/Lab (2% )
- Week 13 Tutorial/Lab (2% )
- Data Science Programming Assignment - Part B (15% )
- Final Exam (45% )
- On-Campus Attendance (Expected) and Online Content (ON-CAMPUS)
- Semester 2, 2019
Four hours of problem-based learning activities, interactive workshops and practical work in laboratory classes on campus per week.
Working knowledge of discrete mathematics, algebra and numerical analysis.