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This course will introduce students to core topics in scalable data science based on distributed-computing techniques. This is a very practical course, with students learning by experimenting on a computer cluster.
This course will introduce students to topics in scalable data science based on distributed computing techniques. We will look at principles of distributed computing, topics in statistical modelling, and applications of distributed machine learning to find scalable solutions for real problems. This is a very practical course, and students will learn by experimenting on both university and cloud based clusters with large datasets. All computing resources are available online using remote desktop and there will be interactive computer labs online for students who are not on campus. Enrolled students who take this course will have ongoing access to computing resources to pursue additional projects.The intent of the course is to provide an environment that is very similar to what you will experience in a data science position in industry. You will need to understand the theory underlying common solutions to data science problems and how to implement these using a distributed computing framework such as Spark.
Concrete learning outcomes will include: Demonstrate your knowledge of the principles of distributed computing by using open source frameworks to perform distributed data processing.Perform distributed data analysis on data using Spark, including visualisation and reporting.Connect concepts of statistical modeling and machine learning and identify how these can be applied to scalable data science problems.Develop practical solutions for real world data science problems that require data analysis, statistical modeling, and machine learning.
This course will provide students with an opportunity to develop the Graduate Attributes specified below:
Critically competent in a core academic discipline of their award
Students know and can critically evaluate and, where applicable, apply this knowledge to topics/issues within their majoring subject.
Employable, innovative and enterprising
Students will develop key skills and attributes sought by employers that can be used in a range of applications.
Biculturally competent and confident
Students will be aware of and understand the nature of biculturalism in Aotearoa New Zealand, and its relevance to their area of study and/or their degree.
Engaged with the community
Students will have observed and understood a culture within a community by reflecting on their own performance and experiences within that community.
Globally aware
Students will comprehend the influence of global conditions on their discipline and will be competent in engaging with global and multi-cultural contexts.
Subject to approval of the Head of Department of Mathematics and Statistics.
James Williams
No textbook required.
LEARN Postgraduate
• Labs / exercises 10%• Quizzes 10%• Assignment 1 40%• Assignment 2 40%
Domestic fee $1,247.00
International Postgraduate fees
* All fees are inclusive of NZ GST or any equivalent overseas tax, and do not include any programme level discount or additional course-related expenses.
For further information see Mathematics and Statistics .