158333

Applied Machine Learning and Big Data Processing

An in-depth exploration of methods for developing intuition and insights about data that enables effective problem formulation and its solution through data-driven methods. A broad range of advanced machine learning and data mining algorithms will be used to build predictive models from real-world contexts. A particular emphasis will be on developing data-products, rapid prototyping and effectively communicating their value through visual storytelling and interpretable summaries. Practical skills for processing large datasets will be taught.

Course code

Qualifications are made up of courses. Some universities call these papers. Each course is numbered using six digits.

158333

Level

The fourth number of the course code shows the level of the course. For example, in course 219206, the fourth number is a 2, so it is a 200-level course (usually studied in the second year of full-time study).

300-level

Credits

Each course is worth a number of credits. You combine courses (credits) to meet the total number of credits needed for your qualification.

15

Subject

Information Technology

Course planning information

Course notes

The final examination will be an online supervised examination using remote invigilation.

To pass the course students must obtain a mark of at least 35% in the Exam.

Prerequisite courses

Complete first

You need to complete the above course or courses before moving onto this one.

General progression requirements

You must complete at least 45 credits from 200-level before enrolling in 300-level courses.

Learning outcomes

What you will learn. Knowledge, skills and attitudes you’ll be able to show as a result of successfully finishing this course.

  • 1 Use a broad variety of sophisticated machine learning and data mining techniques to extract patterns in data.
  • 2 Assess the usefulness of predictive models.
  • 3 Formulate problems for real-world datasets from various contexts.
  • 4 Present data-driven solutions to real world problems.
  • 5 Devise strategies for Big Data problems.

Learning outcomes can change before the start of the semester you are studying the course in.

Assessments

Assessment Learning outcomes assessed Weighting
Computer programmes 1 2 3 4 10%
Computer programmes 1 2 3 4 15%
Computer programmes 1 2 3 4 5 35%
Exam (centrally scheduled) 1 2 3 5 40%

Assessment weightings can change up to the start of the semester the course is delivered in.

You may need to take more assessments depending on where, how, and when you choose to take this course.

Explanation of assessment types

Computer programmes
Computer animation and screening, design, programming, models and other computer work.
Creative compositions
Animations, films, models, textiles, websites, and other compositions.
Exam College or GRS-based (not centrally scheduled)
An exam scheduled by a college or the Graduate Research School (GRS). The exam could be online, oral, field, practical skills, written exams or another format.
Exam (centrally scheduled)
An exam scheduled by Assessment Services (centrally) – you’ll usually be told when and where the exam is through the student portal.
Oral or performance or presentation
Debates, demonstrations, exhibitions, interviews, oral proposals, role play, speech and other performances or presentations.
Participation
You may be assessed on your participation in activities such as online fora, laboratories, debates, tutorials, exercises, seminars, and so on.
Portfolio
Creative, learning, online, narrative, photographic, written, and other portfolios.
Practical or placement
Field trips, field work, placements, seminars, workshops, voluntary work, and other activities.
Simulation
Technology-based or experience-based simulations.
Test
Laboratory, online, multi-choice, short answer, spoken, and other tests – arranged by the school.
Written assignment
Essays, group or individual projects, proposals, reports, reviews, writing exercises, and other written assignments.

Textbooks needed

There are no set texts for this course.

Course delivery details

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