158755

Data Science - Making Sense of Data

A study of the science of drawing knowledge and insights from data, including the concepts and techniques of data mining, machine learning and natural language processing. The course covers both theoretical and practical aspects using a range of software tools and algorithms.

Course code

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

158755

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).

700-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

Students must achieve at least 50% of the assessment to pass the course.

Expected prior learning

Previous level-300 study in Information Technology or a related area.

General progression requirements

You may enrol in a postgraduate course (that is a 700-, 800- or 900-level course) if you meet the prerequisites for that course and have been admitted to a qualification which lists the course in its schedule.

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 Perform data wrangling and data visualization skills using an open-source programming library.
  • 2 Demonstrate the ability to formulate a problem from data, ask questions of data, devise a solution and present findings from real-world problems.
  • 3 Apply several machine learning and data mining algorithms both programmatically and from software toolkits to solve classification problems.
  • 4 Reason about which machine learning/data mining algorithms to apply on given problems.
  • 5 Compare the generalizability of different machine learning/data mining algorithms for a given problem.

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 15%
Computer programmes 1 2 3 25%
Computer programmes 1 2 3 4 25%
Computer programmes 1 2 3 4 5 35%

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.