Module title: Data-Driven Decision Making

SCQF level: 11:
SCQF credit value: 20.00
ECTS credit value: 10

Module code: INF11116
Module leader: Peter Barclay
School School of Computing
Subject area group: Creative and Social Informatics
Prerequisites

n/a

2019/0, Trimester 1, Blended,
Occurrence: 001
Primary mode of delivery: Blended
Location of delivery: MERCHISTON
Partner:
Member of staff responsible for delivering module: Peter Barclay
Module Organiser:


Learning, Teaching and Assessment (LTA) Approach:
You will attend for one day per month during term time for intensive face-to-face lectures, workshops, tutorial and computer-based practical sessions (LO 1-4). Each of these days will typically involve two half day sessions, each consisting of up to 1 hour of lecture followed by up to 2.5 hours of tutorials involving labs/ practical work for the statistics aspects of the module or by tutorials involving seminar/class group work for the organisational aspects of the module. This will be further supported by online material and discussion forums using a variety of communication technologies such as Moodle and Skype. You will be encouraged to develop your learning through peer and tutor interaction, either face to face or through electronic communication. Self-study readings supported by in-class and online discussions hosted through the VLE will develop skills as independent learners (LO 1-4). Formative feedback in online quizzes will encourage independent learning through data collection, analysis, synthesis, as well as skills in developing sound argument (LO 1-4). The lecture programme will be enhanced by material from guest speakers and will be made available online. The material for the lab-based practical sessions will be made available online with a support forum

Formative Assessment:
Formative assessment will be provided during tutorial and lab-based practical sessions at the face to face monthly meetings and/ or online forum. There will also be a series of online quizzes that will give a formative check of progress. The main summative assessment will comprise one practical coursework worth 100% of the final mark (covering LOs 1 - 4). The first elements of this coursework

Summative Assessment:
see above

Student Activity (Notional Equivalent Study Hours (NESH))
Mode of activityLearning & Teaching ActivityNESH (Study Hours)
Face To Face Lecture 6
Face To Face Tutorial 15
Online Tutorial 12
Online Lecture 6
Online Tutorial 5
Independent Learning Guided independent study 116
Independent Learning Guided independent study 40
Total Study Hours200
Expected Total Study Hours for Module200


Assessment
Type of Assessment Weighting % LOs covered Week due Length in Hours/Words
Report 30 1,2,3,4 7 HOURS= 12, WORDS= 0
Report 70 1,2,3,4 15 HOURS= 38, WORDS= 0
Component 1 subtotal: 100
Component 2 subtotal: 0
Module subtotal: 100

Description of module content:

A primary use of data by contemporary organisations is to analyse and explore opportunities for growth or change, either directly or indirectly. The demand for business data, whether operational management, data analytics or data science (such as “big data”, machine learning & predictive analytics) has increased substantially. This has resulted from an organisational need for a more sophisticated approach to analytics and data from both a business and statistical understanding of data and its impacts on the organisation. This raises complex and multifaceted issues.

The aim of the module is to enable you develop a deep understanding of the business context and impact of data, the meaning of the data (including in terms of statistics), and to give you an opportunity to express this in the form of professional written reports. Topics covered include:
* The role of the data scientist
* Data strategy and Key Performance Indicators (KPIs)
* Deployment and implementation
* Governance, ethical and cultural implications
* Exploring and describing data,
* Statistical inference – parametric methods t – tests and Analysis of Variance Statistical presentation of data.
* Multivariate methods – principal component analysis, exploratory factor analysis and segmentation methods (Hierarchical clustering, K means and K modes).
* Statistical modelling – OLS regression, general linear models exemplified by Binary Logistic models
* Diagnosing model fits

The R package for statistics will be used in this module.

The Benchmark Statement for Computing specifies the range of skills and knowledge that should be incorporated in computing courses. This module encompasses cognitive skills in computational thinking and its relevance to everyday life, critical evaluation and professional considerations and practical skills in the deployment and use of tools and critical evaluation of complex problems in addition to providing useful generic skills for employment.

Learning Outcomes for module:

Upon completion of this module you will be able to:

LO1: Critically evaluate the drivers and strategies for advanced analytics and its impact on organisational decision-making
LO2: Critically assess the roles and impact of ethics, governance and professionals in data analysis
LO3: Apply methods of data reduction and of classification to data to identify sub-groups
LO4: Construct and diagnose statistical models to allow prediction of effects and input into strategy development.

Indicative References and Reading List - URL:

Core - JUDAH PHILLIPS (2013) BUILDING A DIGITAL ANALYTICS ORGANIZATION: CREATE VALUE BY INTEGRATING ANALYTICAL PROCESSES, TECHNOLOGY, AND PEOPLE INTO BUSINESS OPERATIONS , ISBN-13: 978-0-13-337278-6: PEARSON, 1st ed.
Core - FOSTER PROVOST & TOM FAWCETT (2014) DATA SCIENCE FOR BUSINESS: WHAT YOU NEED TO KNOW ABOUT DATA MINING AND DATA-ANALYTIC THINKING , ISBN-13: 978-1449361327: O'REILLY, 1st ed.
Core - THOMAS H. DAVENPORT (2014) BIG DATA AT WORK: DISPELLING THE MYTHS, UNCOVERING THE OPPORTUNITIES, ISBN 987-1-4221-6816-5: HARVARD BUSINESS PUBLISHING, 1st ed.
Core - TOOMEY, D (2014) R FOR DATA SCIENCE: PACKT PUBLISHING LTD, BIRMINGHAM, 1st ed.
Core - ZUMEL. N. AND MOUNT, J (2014) PRACTICAL DATA SCIENCE WITH R: MANNING PUBLICATIONS, NY, 1st ed.
Core - GOOD PRACTICE TEAM, EFFECTIVE TABLES AND GRAPHS IN OFFICIAL STATISTICS, GOVERNMENT STATISTICAL SERVICE

- HTTPS://GSS.CIVILSERVICE.GOV.UK/WP-CONTENT/UPLOADS/2014/12/EFFECTIVE-GRAPHS-AND-TABLES-IN-OFFICIAL-STATISTICS-VERSION-1.PDF, 2014



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