INFORMATICS FOR DECISION MAKING - 2020/1

Module Overview

The module introduces knowledge management practice in the context of informatics and data science. The module covers topics and problems associated in the use of ’Big data’ platforms and frameworks within and utilised by organisations. The role of Cloud architectures and the Internet of Things (IoT) is also detailed within this module. Common data sources used to identify, capture, create, and distribute information within an organisation for optimal utilisation by decision makers are identified and examined along with new sources provided by IoT connected sensors. Through the additional use of Mixed Reality technologies such as Augmented Reality the visualisation of data for structured decision making is demonstrated. Exploring the latest visions for data use in industry, such as Industry 4.0 and the Cloud Manufacturing paradigm, this module encourages students to form their own perceptions of existing and future business structure and operations. The module emphasises the building and evaluation of data driven models as an alternative decision-making approach to the established and widely used traditional key performance metrics from econometric and financial analyses. The potential and future use of Artificial Intelligence within decision making is examined and further independent exploration of highlighted concepts is encouraged throughout the course of this module.

In terms of module assessment the 2-hour examination (50% of the total mark) will be a standard closed book examination, with material coming directly from the module content. The broader topics for the questions will be covered during the lectures and laboratory sessions. The problems will be similar to those discussed in the lectures, computer laboratory or indicated in the essential reading material. The answers will mainly be in form of written text responses, and will be in alignment with the criteria related to learning outcomes (1).

 

For the coursework (50% of the total mark), the students will apply and deepen their knowledge with the criteria related to learning outcomes (1) to (3) mentioned above by producing a group assignment of 5000 words in length. The analysis methods and frameworks utilised by informatics practice will be assessed along with appropriate use of related analytics tools. Furthermore, evidence of background reading has to be provided. Students will be informed about the coursework topic at the beginning of the semester.

Module provider

Surrey Business School

Module Leader

TURNER Christopher (SBS)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

Module cap (Maximum number of students): N/A

Module Availability

Semester 2

Prerequisites / Co-requisites

MANM301 Data Analytics

Module content

The module content will focus on a selected set of critical areas in Informatics. As an indication of the kind of issues that will be covered, please find below an indicative set of topics.

• Big Data Analytics Frameworks


• Cloud Computing and Cloud Manufacturing


• Augmented Reality and Virtual Reality for Informatics


• The Impact of Industry 4.0 and IoT technology on Decision Making


• Data Visualisation with Tableau


• Mining Big Data Sets with Apache Spark and TensorFlow


• Programming with Python

Assessment pattern

Alternative Assessment

N/A

Assessment Strategy

In order to achieve the threshold standard for the award of credits for this module, the student must meet the following criteria related to the learning outcomes:

1. Apply the theories, conceptual frameworks and methodologies that underpin informatics for decision making;


2. Prove the ability to visualise data and describe the underlying constituent technologies used in its provision;


3. Demonstrate evidence of background reading and research of the academic and practitioner literature relevant to the field of Informatics.

 

Thus, the summative assessment for this module consists of an exam and a coursework.

 

The 2-hour examination (50% of the total mark) will be a standard closed book examination, with material coming directly from the module content. The broader topics for the questions will be covered during the lectures and laboratory sessions. The problems will be similar to those discussed in the lectures, computer laboratory or indicated in the essential reading material. The answers will mainly be in form of written text responses, and will be in alignment with the criteria related to learning outcomes (1).

 

For the coursework (50% of the total mark), the students will apply and deepen their knowledge with the criteria related to learning outcomes (1) to (3) mentioned above by producing a group assignment of 5000 words in length. The analysis methods and frameworks utilised by informatics practice will be assessed along with appropriate use of related analytics tools. Furthermore, evidence of background reading has to be provided. Students will be informed about the coursework topic at the beginning of the semester.

 

Formative assessment will be provided throughout the course. Students will demonstrate the ability to analyse informatics provided data. Formative feedback on the student’s approach will be given during the computer laboratory sessions.

Module aims

• To gain insights into informatics use within industry and understand the future form of data driven decision making.

Learning outcomes

Methods of Teaching / Learning

The teaching and learning strategy is designed to: cultivate an understanding of the main issues concerning data driven decision making, made possible through a new generation of informatics hardware and software systems along with usage initiative such as Industry 4.0.

The teaching and learning methods include: a lecture every week as well as several student exercises. Web-based learning support and electronic resources will be provided.

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list

https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: MANM302

Programmes this module appears in

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2020/1 academic year.