Download Unit Descriptor - Solent Online Learning

UNIT TITLE: Data Analytics
CREDIT POINTS:15
FHEQ LEVEL: 7
UNIT DESIGNATION: Traditional
UNIT CODE: COM719
ACADEMIC SCHOOL: Media
Technology
Delivering School: Media
Technology
Date validated: September 2016
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
Effective and efficient data analysis techniques help business organisations understand and
report on their past, measure their current market state and predict the future. In today
business world, data analysis is increasingly becoming an indispensable tool for business
corporates to understand their priorities. Students taking this unit will be provided with the
theoretical and practical skills necessary to understand and implement the most current
data analysis principles in the business context. Data analysis aims at defining business
success measurements, introduce new improvements, identify risks related to stakeholders
and technological assets such as online data, financial data, stock data and all other data
driven business aspects.
This unit introduces the key principles, theories and techniques that relate to Business and
Marketing Data Analysis. This unit will be focused on the structured process of data
collection, analysis and forecast business and marketing data in order to generate useful
insights about both business and customer data. It will provide student with the knowledge
needed to work with modern analytical tools currently used to analyse business data. The
unit will also provide students with the ability to report on business data and present
valuable information to stakeholders, it is aimed directly at web data analysis.
This unit will draw upon some of the existing web data analysis platforms, including and not
restricted to, Google Analytics, Clicky, Mint, Church Analytics, Open Web Analytics.
LEARNING OUTCOMES
On successful completion of the unit, students should be able to:
Knowledge and Understanding
K1
Understand the main statistical frameworks and core analysis techniques and their
relation to business data analytics.
K2
Synthesise theory from data mining and data warehousing, reporting and data
visualisation in order to achieve a critical perspective on inform critical business and
marketing decisions.
Cognitive Skills
C1
Independently analyse online business data statistics and report about online
consumer behaviour and design customer acquisition journey,
C2
Design the most effective digital marketing initiatives and evaluate business website
traffic patterns and trends which give insights into what customer segments should
be valuable.
Practical and Professional Skills
P1
Demonstrate a competency in using multiple web analytics platforms, practical
methods to collect actionable business and marketing data.
P2
Create different types of analytical reports, including but not restricted to, Audience
Reports, Acquisition Reports, Behaviour Reports and Customer reports and
Dashboards.
Transferable and Key Skills
T1
Apply business and marketing data analytics to improve business drivers and solve
business practical problems
T2
Work collaboratively with other to produce a presentation on a given research topic
AREAS OF STUDY
The unit will cover the business digital data analytics and the principles of web data mining
and management.
Web Analytics
It is imperative for online business to assess and review web content issues they may have,
web analytics if the examination of online website patterns and trends. This particular area
will be looking at techniques that should be employed in order to collect, analyse, measure
and report website data, this will be achieved by using tactical approach to data collection
and report generations.
Actionable Data Collection with multiple platforms and dashboards
The main focus of this area will be placed on introducing practical data analytics
frameworks, where students will familiarise themselves with different dashboards and learn
how to collect, read and analyse data. This is considered the first step towards an intensive
hand-on experience to google analytics. Student journey will go through creating accounts
to review structures and setting up filters and goals.
Web Data Analysis Techniques
Exploring the web analytics process is vital for this unit, and therefore, the focus will be
shifted to understand the common professional techniques required to process collected
data to information, this should also focus on how to develop a KPI to infuse business
strategies.
Reporting
This will help student to develop the necessary skills to be able to create different types of
analytical reports, including but not restricted to, Audience Reports, Acquisition Reports,
Behaviour Reports and Customer reports and Dashboards
LEARNING AND TEACHING STRATEGY
This unit will be delivered by a mixture of class-room based lecture and seminar sessions
and more practical work, using the usability lab for data collection and analysis, where
students will be provided with a hands-on experience of the standard and commercial tools
for data collection and analysis. Formal tutor-led presentations will cover the essential
topics and will provide an opportunity for students to encounter and explore a wide range
of theoretical and practical content. They will then be expected to apply their learning
for specific client and end user requirements, both independently and in seminars. Working
in groups students will be assigned to a particular platform/context and will seek to apply
the theories and practical skills that they have learned to a specific interface design
problem. Regular student-led presentations will provide students with opportunities to see
and review the work of other groups and will allow the tutor to monitor and feedback on
student work.
This approach is intended to promote positive outcomes in terms of employability and an
inclusive approach, by encouraging professional approaches to team work and by ensuring
regular, formative interaction between students and between students and their tutor. It is
expected that students will actively seek to establish effective collaboration and to use
diversity within the group as a stimulus for creativity and effective problem solving.
ASSESSMENT STRATEGY
Assessment for this unit consists of an exam and a presentation. The exam is designed to
test the student knowledge of digital data analytics, through which students are expected
to critically analyse and answer a combination of multiple choice and true/false questions.
The exam will cover all areas of studies in this unit, it will designed to not only achieve the
unit objectives but also to prepare students for the other accredited professional certificate
such as Google Analytics IQ Certificate Exam.
The presentation should capture the data patterns and trends applied to a certain case study
selected by the student. As part of the assessment strategy of this unit, student should
demonstrate a competency in applying data analytics techniques to certain business
context, where an online business case study will be selected, an account need to be created
and a successful implementation of what the process of web data analytics entail should be
evidenced. This activity will be performed in group context, where student gets a real world
hand on experience in working collaboratively with groups. Through engagement with the
formative group-based development work, students should have contributed to and be able
to access a group case study containing detailed research, evidence of planning, models and
actionable data, testing data and a fully detailed report. They will also have had ample
opportunity to have received feedback on this and will have seen the work of other groups.
Formal assessment on this unit requires each group to perform a group presentation using
their collected data, where they showcase their how the collected data will improve the
business and potentially will contribute to create an online business strategy. This
presentation should be delivered as if it were a pitch to a client. Each member of the team
is expected to contribute to the presentation equally and will be assessed on their individual
contribution. In addition to this each student should submit a copy of the presentation in
which they provide a detailed assessment of their contribution. In doing so they should,
justify key analytical decisions, with reference to theory and relevant secondary literature,
provide an assessment of the strengths and weaknesses of the collected data and how data
analysis will inform more effective business strategy.
AE1
weighting:
assessment type:
length/duration:
online submission:
grade marking:
anonymous marking:
40%
Group Presentation
Up to 10 mins per member including questions
No
Yes
No
AE2
weighting:
assessment type:
length/duration:
online submission:
grade marking:
anonymous marking:
60%
Time Constrained Assignment
2 Hours
No
Yes
No
Aggregation of marks
The marks for each element of assessment will be aggregated to give an overall mark for
the unit.
Re-assessment Arrangements
Students will undertake re-assessment in the University re-assessment period.
Re-assessment in AE1 will require the student to present their element of the group
presentation to meet the required standard.
In the event of referral in AE2, students will be required to revise and resubmit their original
submission in light of tutor feedback.
Unit Author: Dr.Mohammed Al-Husban
Date of version: September 2016