Foundation degrees

Course Information

Big Data

Module summary

Module code: COMP1702
Level: 7
Credits: 15
School: Liberal Arts and Sciences
Department: Computing and Information Sys.
Module Coordinator(s): Mohammad Majid Al-Rifaie / Mohammed Hassouna

Specification

Aims

This course aims to provide analytical skills to study big data and to provide a solid foundation for developing solutions and applications that need to manipulate big data. Students will be introduced to a range of tools and techniques to manipulate and manage big data sets which will be used to develop a range of big data applications. Specific aims are: To provide an overview of statistical and machine learning techniques often used in big data processing. To develop a fundamental toolbox of techniques and skills from which to construct solutions to gain insight from large data sets. To develop the ability to manipulate abstracted data into meaningful information.

Learning outcomes

On successful completion of this course a student will be able to:

1 Demonstrate a clear understanding of the common statistical analysis and machine learning techniques used to manipulate data.
2 Utilise current big data technologies to create applications for a range of purposes.
3 Critically evaluate, select and employ appropriate tools and technologies for the development of big data applications
4 Demonstrate a critical awareness of the current limitations of the tools used to manipulate big
data.

Indicative content

i. Examination and comparison of statistical and analytical tools to manipulating and understanding big data.
ii. Analysis and exploration of data.
iii. Development of statistical models using industry standard tools.
iv. Examination and comparison of technologies to support big data and build big data applications.

Teaching and learning activity

Each week students will attend a 1-hour lecture (33%) and a 2-hour lab (67%). In the lectures, student will be introduced to the theoretical and technical concepts needed for the development of big data applications. In the lab sessions, students will learn new languages and tools to develop big data applications by completing a set of tutorials and lab exercises.

Assessment

Method of SUMMATIVE assessment: Practical coursework
Outcomes assessed:1-4
Grading Mode (e.g. pass/ fail; %): %
Weighting % :100%
Passmark: 50%
Word Length: 2000 words
Outline Details: Analysis of a large dataset by the application of appropriate tools and methods.Critically evaluate the approach and results.

Nature of FORMATIVE assessment supporting student learning:
Feedback from lab tutors on lab-based tutorial exercises