Large-Scale Computing for Data Analytics (ODL) STATS5083

  • Academic Session: 2024-25
  • School: School of Mathematics and Statistics
  • Credits: 10
  • Level: Level 5 (SCQF level 11)
  • Typically Offered: Summer
  • Available to Visiting Students: No
  • Taught Wholly by Distance Learning: Yes
  • Collaborative Online International Learning: No

Short Description

The course introduces students to deep learning and convolutional neural networks and presents an overview over systems for large-scale computing and big data.

Timetable

The course mostly consists of asynchronous teaching material.

Requirements of Entry

The course is only available to online-distance learning students on the PGCert/PGDip/MSc in Data Analytics and Data Analytics for Government.

Excluded Courses

 -/-

Co-requisites

-/-

Assessment

100% Continuous Assessment

This will typically be made up of a project, assessed in terms of code and a report, (40%) and three homework exercises, including online quizzes (60%). Full details are provided in the programme handbook.

Course Aims

The aims of this course are:

■ to train students in the efficient implementation of computationally expensive data-analytic methods and/or data-analytic methods for big data;

■ to introduce students to deep learning and convolutional neural networks, both in terms of applications and implementation in frameworks such as Tensorflow or Keras; and 

■ to introduce students to enterprise-level technology relevant to big data analytics such as Spark, Hadoop or NoSQL databases.

Intended Learning Outcomes of Course

By the end of this course students will be able to:

■ assess and compare the complexity of an algorithm and implementation both in terms of computational time and memory, as well as suggest strategies for reducing those;

■ distinguish between different types of deep and/or convolutional neural networks and choose an appropriate network for a given problem;

■ fit a neural network using specialised frameworks such as Tensorflow or Keras and assess the result;

■ discuss important methodological aspects underpinning deep learning; 

■ explain the differences between SQL and NoSQL databases and assess their suitability in different real-life settings; and

■ explain the basic concepts underpinning big data systems such as Spark or Hadoop and discuss their suitability and use in different scenarios.

Minimum Requirement for Award of Credits

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.