Computer Science

ECM3441 - Data Analysis 2 (2019)

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MODULE TITLEData Analysis 2 CREDIT VALUE15
MODULE CODEECM3441 MODULE CONVENERUnknown
DURATION: TERM 1 2 3
DURATION: WEEKS 12 0 0
Number of Students Taking Module (anticipated) 15
DESCRIPTION - summary of the module content

The primary role of a data analyst is to collect, organise and study data to provide new business insight. You are responsible for providing up-to-date, accurate and relevant data analysis for the organisation. You are typically involved with managing, cleansing, abstracting and aggregating data across the network infrastructure. You will have a good understanding of data structures, software development procedures and the range of analytical tools used to undertake a wide range of standard and custom analytical studies, providing data solutions to a range of business issues. You will document and report the results of data analysis activities making recommendations to improve business performance. You need a broad grounding in technology solutions to be effective in your role.

Pre-requisite ECM3433 Data Analysis 1

AIMS - intentions of the module

The aim of this module is to extend your skills in data analysis, encompassing more advanced statistical and modelling techniques to derive insights from large and small datasets, ways of communicating results to stakeholders, and practical knowledge of data quality and control issues.

INTENDED LEARNING OUTCOMES (ILOs) (see assessment section below for how ILOs will be assessed)

On successful completion of this module you should be able to:

Module Specific Skills and Knowledge

1. Import, cleanse, transform, and validate data with the purpose of understanding or making conclusions from the data for business decision making purposes

2. Present data visualisation using charts, graphs, tables, and more sophisticated visualisation tools

3. Perform routine statistical analyses and ad-hoc queries

4. Use a range of analytical techniques such as data mining, time series forecasting and modelling techniques to identify and predict trends and patterns in data

5. Report on conclusions gained from analysing data using a range of statistical software tools

6. Summarise and present results to a range of stakeholders making recommendations

7. Design and develop relational databases for collecting data and influencing data input screens

8. Develop Data Definition Language or Data Manipulation Language software

9. Analyse large datasets, to derive inferences

10. Interpret and apply the organisations data and information security standards, policies and procedures to data management activities

Discipline Specific Skills and Knowledge

11. The quality issues that can arise with data and how to avoid and/or resolve these

12. The processes involved in carrying out data analysis projects.

13. How to use and apply industry standard tools and methods for data analysis

14. The range of data protection and legal issues

15. The fundamentals of data structures, database system design, implementation and maintenance

16. The organisation's data architecture

17. How to use a range of appropriate data analysis techniques or processes

18. The importance of clearly defining customer requirements for data analysis

19. The steps involved in carrying out routine data analysis tasks

20. The importance of the domain context for data analytics

Personal and Key Transferable / Employment Skills and Knowledge

21. Communicate orally and in writing

22. Solve problems creatively

23. Think analytically and critically

24. Organise your own work

25. Work to a deadline

26. Make decisions

27. Conduct independent research

SYLLABUS PLAN - summary of the structure and academic content of the module

Data storage (2 weeks)

•          NoSQL databases e.g., Hadoop; MongoDB

•          Unstructured data

 

Analysing data to derive inferences and to identify and predict trends and patterns (6 weeks)

•          Advanced statistical techniques

•          Machine learning and cognitive computing; natural language processing

•          Modelling techniques

•          Network analysis

•          Analysing large datasets

•          Advanced use of data analysis tools

•          Data visualisation; tables, charts and graphs; more sophisticated visualisation tools

 

Communicating results (2 weeks)

•          Reporting on conclusions gained from analysing data

•          Summarise and present results to a range of stakeholders

•          Making recommendations

 

Quality and controls (2 weeks)

•          Quality issues that can arise with data; how to avoid and/or resolve

•          Security; applying the organisation’s data and information security standards, policies and procedures

•          Data protection

•          Other legal issues

LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 22.00 Guided Independent Study 128.00 Placement / Study Abroad 0.00
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
Category Hours of study time Description
Scheduled learning and teaching activities 18 Online learning activity, including virtual workshops, synchronous and asynchronous virtual lectures and other e-learning.
Scheduled learning and teaching activities 2 Lectures
Scheduled learning and teaching activities 2 Group workshops
Guided independent study 128 Coursework, exam preparation and self-study

 

ASSESSMENT
FORMATIVE ASSESSMENT - for feedback and development purposes; does not count towards module grade
Form of Assessment Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Contribution to class discussion N/A 1-27 Verbal
       
       
       
       

 

SUMMATIVE ASSESSMENT (% of credit)
Coursework 60 Written Exams 40 Practical Exams 0
DETAILS OF SUMMATIVE ASSESSMENT
Form of Assessment % of Credit Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Modelling and visualisation exercise 60 3,000 words 1-27 Written
Written exam 40 2 hours 1-25, 26 Written
         
         
         

 

DETAILS OF RE-ASSESSMENT (where required by referral or deferral)
Original Form of Assessment Form of Re-assessment ILOs Re-assessed Time Scale for Re-assessment
Modelling and visualisation exercise (60%) Modelling and visualisation exercise 1-27 Completed over summer with a deadline in August
Written exam (40%) Written exam 1-25, 26 August assessment period
       

 

RE-ASSESSMENT NOTES

Deferral – if you miss an assessment for certificated reasons judged acceptable by the Mitigation Committee, you will normally be deferred in the assessment. The mark given for a re-assessment taken as a result of deferral will not be capped and will be treated as it would be if it were your first attempt at the assessment.

Referral – if you have failed the module overall (i.e. a final overall module mark of less than 40%) you may be required to sit a referral. The mark given for a re-assessment taken as a result of referral will be capped at 40%.

RESOURCES
INDICATIVE LEARNING RESOURCES - The following list is offered as an indication of the type & level of
information that you are expected to consult. Further guidance will be provided by the Module Convener

ELE: http://vle.exeter.ac.uk

 

Reading list for this module:

Type Author Title Edition Publisher Year ISBN Search
Set Witten, I. H., Frank, E., Hall, M. A. Data Mining: Practical Machine Learning Tools and Techniques 3rd Morgan Kaufmann 2011 978-0123748560 [Library]
Set Few S Now You See it: Simple Visualization Techniques for Quantitative Analysis 1st Analytics Press 2009 978-0970601988 [Library]
Set Rice, J A Mathematical Statistics and Data Analysis 3rd Brooks Cole 2007 978-0495118688 [Library]
Set Luciano Ramalho Fluent Python 1st O'Reilly Media 2015 978-1491946008 [Library]
CREDIT VALUE 15 ECTS VALUE 7.5
PRE-REQUISITE MODULES ECM3433
CO-REQUISITE MODULES
NQF LEVEL (FHEQ) 6 AVAILABLE AS DISTANCE LEARNING No
ORIGIN DATE Friday 22 January 2016 LAST REVISION DATE Tuesday 10 July 2018
KEY WORDS SEARCH Data Analysis