Computer Science

ECM3433 - Data Analysis 1 (2019)

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MODULE TITLEData Analysis 1 CREDIT VALUE15
MODULE CODEECM3433 MODULE CONVENERDr Rudy Arthur (Coordinator)
DURATION: TERM 1 2 3
DURATION: WEEKS 0 11 0
Number of Students Taking Module (anticipated) 15
DESCRIPTION - summary of the module content

***DEGREE APPRENTICESHIP STUDENTS ONLY***

The primary role of a data analyst is to collect, organise and study data to provide new business insight. They are responsible for providing up-to-date, accurate and relevant data analysis for the organisation. They are typically involved with managing, cleansing, abstracting and aggregating data across the network infrastructure. They 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. They document and report the results of data analysis activities making recommendations to improve business performance. They need a broad grounding in technology solutions to be effective in their role.

AIMS - intentions of the module

The aim of this module is to give you foundation skills in data analysis, including the fundamentals of data extraction and preparation and an introduction to the the use of a range of analysis techniques that can be used to derive useful information from data.

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. Perform routine statistical analyses and ad-hoc queries

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

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

5. Develop Data Definition Language or Data Manipulation Language software

6. Demonstrate understanding of the processes involved in carrying out data analysis projects.

7. Recognise how to use and apply industry standard tools and methods for data analysis

8. Demonstrate understanding of the fundamentals of data structures, database system design, implementation and maintenance

Discipline Specific Skills and Knowledge

9. Recognise the organisation's data architecture

10. Demonstrate understanding of how to use a range of appropriate data analysis techniques or processes

11. Recognise the importance of clearly defining customer requirements for data analysis

12. Recognise the steps involved in carrying out routine data analysis tasks

13. Recognise the importance of the domain context for data analytics

Personal and Key Transferable / Employment Skills and Knowledge

14. Communicate orally and in writing

15. Solve problems creatively

16. Think analytically and critically

17. Organise your own work

18. Work to a deadline

 

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

Introduction (2 weeks)

•          The steps involved in data analysis projects and tasks

•          The importance of the domain context for data analytics

•          The organisation's data architecture

•          Understanding and drawing conclusions from data

•          Defining customer requirements

•          Case studies (retail; healthcare; finance)

 

Revision: database (2 weeks)

•          Data structures

•          Database and database system design

•          Database system implementation and maintenance

•          Data definition and data manipulation language

•          Ad hoc queries

 

Data preparation (3 weeks)

•          Extracting, transforming and loading data

•          Validating and cleansing data

 

Analysing data to derive inferences and to identify and predict trends and patterns (5 weeks)

•          Common statistical techniques

•          Analysing small and large data sets

•          Structured vs unstructured data

•          Data mining

•          Forecasting

•          Using tools for data analysis e.g., SQL, Microsoft Excel, SPSS, SAS, R

•          Introduction to advanced techniques e.g. cognitive computing, machine learning

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 6-18 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
ETL and data mining exercise 60 3000 words 1-18 Written
Written exam 40 2 hours 1-18 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
ETL and data mining exercise (60%) ETL and data mining exercise 1-18 Completed over summer with a deadline in August
Written exam (40%) Written exam (2 hours) 1-18 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: vle.exeter.ac.uk

 

Reading list for this module:

Type Author Title Edition Publisher Year ISBN Search
Set Rice, J A Mathematical Statistics and Data Analysis 3rd Brooks Cole 2007 978-0495118688 [Library]
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 Luciano Ramalho Fluent Python 1st O'Reilly Media 2015 978-1491946008 [Library]
CREDIT VALUE 15 ECTS VALUE 7.5
PRE-REQUISITE MODULES None
CO-REQUISITE MODULES None
NQF LEVEL (FHEQ) 6 AVAILABLE AS DISTANCE LEARNING No
ORIGIN DATE Tuesday 10 July 2018 LAST REVISION DATE Wednesday 18 September 2019
KEY WORDS SEARCH Data, Analysis