Prior study of Data Analytics and Techniques (or similar)

You will need access to these resources:

The VLE
An ICT Workplace environment. A Work-Based Learning Agreement will be completed for this module to ensure that the student will have access to the required resources in the workplace.

The Internet and office software
Staffordshire University has subscriptions to electronic book services such as Safari Tech Books, Ebrary and Netlibrary. There are titles in each of the collections that will support students studying this module.

Excel Data analysis Pak
MySQL Community Server edition
Altova XML Suite
PHPmyadmin
Lynda.com video library on Data Analysis Library

Module Launch (30 hours)
There will be a module launch during which 10 hours of face to face contact will be devoted to undertaking tasks which are designed to provide useful insights into the module content and purpose.

Guided Learning (22 hours)
A module tutor who is part of the teaching team of the module will be allocated to you and you will meet them during the launch. Following the launch, there will be some materials on the VLE which are designed to guide your learning. Additionally, there will be at least two hour long sessions per week of contact time for the eleven weeks following the launch. This will be used for learning guided led by your module tutor. It will be a face to face presentation if you are on day release. For online learners it will be flipped classroom approach with group (up to 20) seminars.

Reviews:
• Tutorial reviews for online learners (1 hour per student)
Online learners will have 2 tutorial sessions with their module tutor during the course of the module. These will be individual or small group sessions during which your module tutor will be able to answer any queries that you have regarding module work. The review weeks are listed in the module handbook and mentors will be invited to join the call and provide feedback.

• Tutorial sessions for day release learners (at least 1 hour per student)
There will scheduled tutorial sessions (up to 20 students) during the 11 weeks following the launch which will take the place of the tutorial reviews for day release students

Independent learning (247 hours)
The module leader will provide resources through the virtual learning environment which will include videos and presentations as well as links to useful websites. Other academic learning will be achieved through reading around the subject area. Module tutors will suggest useful texts, though many others will be suitable and can be found in our e-library. If you require help understanding any of the concepts, you may contact your module tutor for assistance.

Part of your independent learning will take place in your workplace under the guidance of your mentor. You will complete a work-based learning agreement to ensure that arrangements are in place at your workplace to facilitate this work-based learning. You are encouraged to endeavour to apply your growing academic knowledge to improve your work practice and to reflect on your work-based experiences to improve your learning.

You will be required to complete assignment work during independent learning time. Assignment work for a 30 credit module at level 6 should take around 140 hours to complete

Additional help with learning
You will have access to the departmental librarian. As a student, you are more than welcome to visit the university at any time and to use the resources. During time at the university, you may arrange to meet your module tutor or academic coach for additional help.

• Big data concepts 1, datasets
• Big data concepts 2 including public datasets
• Hadoop or equivalent
• Hadoop or equivalent
• NoSQL v SQL from level 5
• Data retrieval from NoSQL structures
• Social media and unstructured data
• Data mining concepts and practice
• Datasets 2 – creating your own dataset for the assignment
• Legal and ethical issues of big data
• Project management. Are Big Data projects just like other projects?
• Quality issues of big data. Bad data!
• Stakeholder presentation tools
• Present data visualisation using charts, graphs, tables, and more sophisticated visualisation tools
• The future of big data

Two pieces of Coursework

A portfolio of tasks Weighted at 30% Learning outcomes 2 and 3

An individual assignment Weighted at 70% Learning outcomes 1 and 4

Covering all learning outcomes using the learners work context where possible. A back up scenario will also be provided and the learners will have the choice between the two. However, encouragement to use the workplace as the scenario for the assignment will be given

Dean J. 2014 Big Data, Data Mining and Machine Learning John Wiley & Sons

Ahlemeyer-Stubbe A., Coleman S. 2014 A Practical Guide to Data Mining for Business and Industry John Wiley & Sons

Krishnan K 2013 Data Warehousing in the Age of Big Data Morgan Kaufmann
And the following will be available through the e-books service
Big Data: Using Smart Big Data, Analytics and Metrics to Make Better Decisions and Improve Performance
Bernard Marr 2015 ISBN-10: 1118965833 ISBN-13: 978-1118965832

Big Data for Dummies Wiley and Sons
Hurwitz 2013 ISBN-10: 1118504224 ISBN-13: 978-1118504222

1. RESEARCH, ANALYSE AND CRITICALLY EVALUATE BEST PRACTICES IN BIG DATA ANALYTICS AND ABILITY TO EXTRACT VALUE AND INSIGHT FROM DATA THROUGH PRACTICAL WORK
Analysis
Reflection
Enquiry

2. CRITICALLY DISCUSS AND EVALUATE DOMAIN-SPECIFIC TECHNIQUES FOR APPLYING BIG DATA ANALYTICS TO DIFFERENT DOMAINS
Enquiry
Knowledge & understanding

3. SYSTEMATICALLY UNDERSTAND THE CONCEPTS THAT UNDERPIN DATA MINING AND BE ABLE TO APPLY RELEVANT TOOLS
Application

4. TEST AND DEVELOP SYSTEMATIC ANALYTICAL APPROACHES TO PROBLEM SOLVING AND ABILITY TO PRESENT VISUAL AND COHERENT WRITTEN ARGUMENTS FOR CHOICE OF APPROACH.
Problem solving
Communication