Download Unit Descriptor - Solent Online Learning

UNIT TITLE: Data Management
CREDIT POINTS: 15
FHEQ LEVEL: 7
UNIT DESIGNATION: Traditional
UNIT CODE: COM718
ACADEMIC SCHOOL: Media
Technology
Delivering School: Media
Technology
Date validated: September
Date last modified: N/A
Unit delivery model: CD
Max & Min Student No: N/A
Arts
and
Arts
and
TOTAL STUDENT WORKLOAD
Students are required to attend and participate in all the formal scheduled sessions for the
unit. Students are also expected to manage their directed learning and independent study
in support of the unit.
PRE-REQUISITES AND CO-REQUISITES: None
UNIT DESCRIPTION
The concept of Data Science has now become a driving force within the Digital Economy
across many strands of both business and social life.
This unit explores a wide variety of the concepts and methodologies associated with the
management of databases, how data is explored and mined, and the role of predictive
analytics.
The aim of the unit is to equip both graduates and professionals with the knowledge and
skills required for the global marketplace. It will provide enterprise-level training in
Database Management, Data Modelling, Data Warehousing and their applications through
research-informed teaching and industry case studies.
LEARNING OUTCOMES
On successful completion of the unit, students should be able to:
Knowledge and Understanding
K1
Analyse and evaluate situations and theories and to an appropriate audience.
Cognitive Skills
C1
Critically evaluate a range of appropriate tools and methodologies for their
suitability and justify choices made.
Practical and Professional Skills
P1
Undertake research into advanced data mining and analytics tools and techniques.
Transferable and Key Skills
T1
Critically evaluate choices and decisions made.
AREAS OF STUDY
Data Mining and Knowledge Discovery
Concepts and theory of Data Mining (DM) and Knowledge Discovery in Databases (KDD)
KDD Process including data exploration, cleansing, pre-processing, mining and evaluation
Various data mining methods and algorithms including predictive analytics
Practical experience using data mining/machine learning software packages
Implementation and testing of a DM application using sample, synthetic and real datasets
Data Warehousing and OLAP
Theoretical underpinning of data warehousing concepts and architecture
ETL (Extraction, Transformation and Loading) process in data warehousing
Data warehouse design – star/snowflake schema, fact/dimension tables and data cubes
Multi-Dimensional Modelling and On-Line Analytic Processing (OLAP)
Querying, reporting and using tools to summarise and discover new patterns
Big Data and NoSQL
Develop an understanding of the theoretical and technical concepts of big data
NoSQL databases and data modelling
Techniques to manipulate and manage large-scale datasets
Big data processing and analytics
Evaluation and selection of appropriate tools to develop big data applications
LEARNING AND TEACHING STRATEGY
Students will be expected to perform additional individual student led research on each
topic. This essential work will inform the basis of the work required for the assignments.
The learning approach is based on a series of small activities used to explore the key
concepts associated with the unit content, and to place these in the context of the
assignment.
Presentation of core theoretical concepts will take place in the early stages of delivery, but
students will move quickly to an exploration of how these are applied in independent work
with later stages focused more on development of the transferable skills.
This will include regular reviews to ensure that students and tutor are able to monitor the
effectiveness of the work and overall progress. Meanwhile, class exercises will develop
confidence in applying key techniques and the soft skills associated with stakeholder
relationship management throughout.
ASSESSMENT STRATEGY
The unit will be assessed by one individual written assignment.
The assignment will focus on the critical evaluation of the tools used and researched
throughout the unit and their application. The report will be underpinned by the inclusion
of a software product developed during the unit. The software product and report also offer
the potential for the student to show coherent and substantive evidence to an employer of
the modelling, analytical and professional skills acquired.
ASSESSMENT
AE1
weighting:
assessment type:
length/duration:
online submission:
grade marking:
anonymous marking:
100%
Report
3000 words
Yes
Yes
No
Aggregation of marks
No departure from standard University regulations.
Re-assessment Arrangements
Students referred in AE1 will be required to revise and resubmit the Report in light of tutor
feedback.
Unit Author:
Date of version: September 2016