MSc - Data Exploration and Analysis

Course Outline and Assessment 2018

Business School Ali Copepland and Dr Andrew Clegg

Data Exploration and Analysis

MSCDSA04 Data Exploration and Analysis

Data Exploration and Analysis

Data Exploration and Analysis

Introduction

Building upon the concepts outlined in the Data Science Foundation module, this module will take a deeper dive in to the methods and in particular the statistical techniques typically used when analysing and presenting data. There are challenges around gathering external data, as well as in analysing and interpreting hundreds of thousands, or millions, of data points. The module encourages the use of open access data sources to bring in added value to an organisation.

This will enrich student’s understanding and appreciation of in sustainability being the driver for bringing suppliers and customers along for the journey. Central to the work of this module is Python.

This module gives the student the opportunity for hands on data analysis using Python on an open public data set.

Learning Outcomes

Knowledge and Understanding: On successful completion of this module students will be able to: •

Demonstrate ability to apply Python, and other data analysis tools, such as SPSS, to manipulate, analyse and present data effectively.

•

Demonstrate a critical understanding of core statistical techniques and how to apply them.

•

Select and apply the correct statistical techniques to specific data analysis tasks

•

Demonstrate critical understanding of typical use cases for other statistical language tools

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Data Exploration and Analysis

Module Content

Week 2 - 21/29/18: Introduction Week 3 - 28/9/18:

Python Workshop 1

Week 4 - 5/10/18:

Python Workshop 2

Week 5 - 12/10/18:

Python Workshop 3

Week 6 - 19/10/18:

SPSS Workshop 1: Introduction to SPSS and basic descriptive analysis

Week 7 - 26/10/18:

READING WEEK - SELF-DIRECTED STUDY

Week 8 - 2/11/18:

Presenting Your Data

Week 9 - 9/11/18:

SPSS Workshop 2: Exploratory Data Analysis 1 - Understanding Your Data

Week 10 - 16/11/18: SPSS Workshop 3: Exploratory Data Analysis 1 - Looking for Difference 1 Week 11 - 23/11/18: SPSS Workshop 4: Exploratory Data Analysis 1 - Looking for Difference 2 Week 12 - 30/11/18: SPSS Workshop 5: Exploratory Data Analysis 1 - Looking for Association

Module Resources

Sessions for MSCDSA04 will be on Fridays. Specific learning

Self-Directed Activities

Sessions will involve the use of resource files that can be downloaded

outcomes for each session are detailed on the MSCDSA04 homepage on Moodle.

from the MSCDSA04 homepage on Moodle. Please download these files into your own file space ready for use in the session.

As part of the course, you will be asked to complete short tasks as part of the lecture session.

Specific tasks will be allocated on a

weekly basis. It is essential that these tasks are completed, in order to demonstrate your competency in the statistical methods that are being employed during the module. Please ensure that you read through the handouts provided thoroughly.

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Data Exploration and Analysis

SPSS Software

This module will involve the use of two specific software programmes.

Python and SPSS. While we will be running Python on University computers, you can download Python for free at: www.python.org.

The module will also be using SPSS. You can get a copy of SPSS to install on your own PC/Mac from the library (free of charge!). This software will be licensed to you as long as you are at student at the University of Chichester. You are free to install SPSS on your own laptop and bring that to the weekly sessions.

Assessment

The assessment for this module will focus on: •

Data analysis scenario/case study (1,600 words)

•

Lab based project development, modelling, and testing using Python and other statistical tools. (2,400 words equivalent)

Additional guidance on all aspects of the assessment will be provided during the module.

Assessment Criteria •

Quality of data analysis

•

Quality of Python and statistical tools techniques and application

•

Quality of data insight/output generated.

•

Originality, as evidenced by creative aspects of the work, independence of judgment, and striking insights

•

Reasoned recommendations/conclusions based on adequate data analysis, problem solving techniques, and clear developmental approaches

•

Clarity and coherence of writing

Reassessment Portfolio of elements highlighted by examiner/assessor relating to original assessment task. (4,000 words) Assessment Criteria •

Quality of data analysis

•

Quality of Python and statistical tools techniques and application

•

Quality of data insight/output generated.

•

Originality, as evidenced by creative aspects of the work, independence of judgment, and striking insights

•

Reasoned recommendations/conclusions based on adequate data analysis, problem solving techniques, and clear developmental approaches

•

Clarity and coherence of writing

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Data Exploration and Analysis

Submission Dates

Key dates for your diary: •

Lab Reports must be submitted by 1pm on Friday 2nd November

•

Data analysis scenario/case study must be submitted by 1pm on Friday 14th December.

Student Support

Ali and I can be found on the top of floor of the Dome on the Bognor Regis campus. If you have any problems please do not hesitate to come see us. While you are welcome to pop in informally, please email myself or Ali to make an appointment (a.copeland@chi.ac.uk / a.clegg@chi.ac.uk) to guarantee that we are in to see you.

You will be introduced to a number of new concepts and techniques in this module - notably an introduction to basic statistics. Statistics is not ‘everybody’s cup of tea’ and I am very conscious about what is called ‘statistics anxiety’.

If at any time you are unclear about what we are doing, please do not hesitate to come and see us. The support materials for this module have been designed to make the everything as self-explanatory as possible. Please make time to read through the materials provided, and use the online simulations to enhance your familiarity with the different statistical techniques we will be using. The materials have been expanded and developed as a result of feedback from student evaluations. It is imperative that you read through all the materials provided and take responsibility for your own learning. Failure to do so could result in you failing this module.

Evaluation

At the end of the module, you will have the opportunity to complete a module evaluation form to comment on the overall structure, content and quality of the programme.

The module evaluation for the previous year can be found on the MSCDSA04 homepage. If you have any immediate concerns about the quality of the module then please do not hesitate to come and talk to me directly. You can also make comments throughout the course of the module by using the comment and suggestion wall that has been embedded into the MSCDSA04 homepage.

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Data Exploration and Analysis

Student Conduct

The University’s Commitment Charter (Section C) sets out the codes of behaviour that staff and students can expect from one another.

Every member of the University community is expected to uphold the Charter commitments and to help to maintain a respectful and constructive learning environment for themselves and for others.

In contact (class) time, and outside of it, the University expects you to show consideration towards other students and the staff of the University. In lectures, seminars and workshops it is your responsibility to avoid behaviour which distracts the learning process for yourself and others. Behaviours which may seem insignificant to you, such as whispering to friends, or texting during a seminar, are almost always noticed! They can have an accumulative, negative impact on the group and the tutor. Such behaviours signal lack of respect for others - even if this was not your intention.

To help illustrate these points, here are some behaviours that students and tutors have found distracting: •

Talking or whispering in lectures, outside times set aside for group discussion

•

Talking or whispering while other students are making points

•

Interrupting other students or the tutor while they are talking

•

Habitually arriving late or leaving early (without forewarning the tutor)

•

Sending and receiving texts

•

Mobile phones ringing

•

Using MP3 players

•

Playing electronic games

•

Surfing the net in class

Students whose behaviour disrupts a class persistently may be asked to leave the session. However we are sure that as adult learners you’ll use common sense and be willing to help create the best possible learning environment for everyone.

p. 6

Data Exploration and Analysis Students often find elements of statistical analysis rather difficult.

Therefore considerable time and effort has gone into the design of learning and teaching materials. The sessions will be tutor-led to start, therefore students are asked to pay close attention to the instructions that are given. Please note: [1] I will not expect to see students using software other than that being used in the session - no emailing, checking Facebook or equivalent. Students infringing this request will be asked to leave the session.

[2] Please be punctual as there is considerable ground to cover in each of the weekly sessions. Evidence indicates that students who have a poor attendance record fail this module.

[3] Mobile phones should be switched off before the session.

[4] This is a challenging module and you will need to concentrate.

I will need to try and help everybody through the session.

This is not helped by constant chatting, as such any students persistently talking or causing a distraction will be asked to leave the session.

[5] It is critical that any self-directed activities or quizzes are completed satisfactorily. Failure to complete weekly tasks will result in you being excluded from sessions until these activities are completed. Please note that I can monitor completion via Moodle, and I will contact students that are not engaging with resources. Failure to engage with the module resources will ultimately result in you failing the module.

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Data Exploration and Analysis

Attendance

Students are reminded that attendance at all modules is compulsory.

On arrival at each scheduled/timetabled session you will be expected to ‘tap in’ to the SAM reader located near the entrance to the teaching room. It is your responsibility to register your attendance. Failure to register your attendance will be treated as non-attendance. If you miss a session, for whatever reason, you should complete and submit a student absence via ChiView - guidance on how to do this will be provided at the start of the module. It is also courteous to let the module tutor know of any absence in advance or immediately after the session that was missed. You are reminded that persistent absence can potentially result in your de-registration from the module.

The full University regulations regarding attendance can be found in your student handbook. You are also asked to arrive punctually for your lectures. Students that are persistently late will be marked as absent. A record of your punctuality will also be captured via the SAM system.

For reference, students that tend to fail this module do so because of a lack of attendance and engagement. You are also advised that student engagement with tasks is monitored closely via Moodle, and any student persistently shown to be not engaging will be asked to attend a meeting with the module tutor to explain their lack of academic endeavour.

p. 8

Relevance

Non-submission of work

Contains little of relevance to the objectives of the assessment task. Fails to answer and address the set topic

Contains limited relevance to the objectives of the assessment task. May address the topic but not the assignment brief. May be scanty and brief.

Inconsistency of relevance to the objectives of the assessment task. Addresses topic but not always [OL HZZPNUTLU[ IYPLM 4H` IL ZPNUPÄJHU[S` ZOVY[ VM required length/ time.

May be some deviation from objectives of the assessment task. May not consistently address set question or assignment brief. May be short of required length/time.

Satisfactorily addresses most objectives of the assessment task Completed to acceptable tolerance, limits of time/length.

Competently addresses objectives of the assessment task, but may contain minor errors or omissions at the lower end, where treatment of issues may be Z\WLYÄJPHS *VTWSL[LK [V YLX\PYLK [PTL SLUN[O L[J

Clearly addresses the objectives of the assessment task, especially those elements requiring critical analysis. At the higher end the work will not contain errors or omissions.

Authoritatively addresses the objectives of the assessment task, especially those components requiring critical analysis, synthesis and evaluation.

Innovatively addresses objectives of the assessment task, especially those components requiring sophistication of critical analysis, synthesis and evaluation.

Professionally addresses the objectives of the assessment task, especially those components requiring originality of critical analysis, synthesis and evaluation.

Class Marks/Overall Quality

Fail

Fail 1-9% Minimal quality

Fail 10-19% Very poor quality

Fail 20-34% Poor quality

Fail/PP 35-29% Weak quality

3rd 40-49% Acceptable quality

2(ii) 50-59% Sound quality, competent with some limitation

2(i) 60-69% High quality, skilled work

1st 70-79% Outstanding quality

1st 80-89% Outstanding quality

1st 90-100% Exceptional or distinguised quality

Undergraduate Assessment Criteria

Consistent line of profound critical and evaluative argument, displaying the ability to develop original ideas from an innovative synthesis of the work of V[OLYZ *YLH[P]L ÅHPY PU HK]HUJLK [OLVYL[PJHS HUK conceptual analysis.

A clear and consistent line of highly critical and evaluative argument, displaying the ability to develop VUL Z PUUV]H[P]L PKLHZ MYVT [OL ^VYR VM V[OLYZ *YLH[P]L ÅHPY PU [OLVYL[PJHS HUK JVUJLW[\HS HUHS`ZPZ

A clear and consistent line of critical and evaluative HYN\TLU[ KPZWSH`PUN [OL HIPSP[` [V KL]LSVW VUL Z V^U insightful ideas from the work of others. Excellent engagement in theoretical and conceptual analysis.

Generally clear line of critical and evaluative argument, with ability to develop own ideas from the work of others. Ability to engage in theoretical and conceptual analysis.

Some limited critical discussion, but argument is unconvincing, particularly at the lower end where the work is more descriptive. More reliance on work of others rather than developing own arguments. Limited theoretical and conceptual analysis.

Work is descriptive with minimal critical discussion and limited theoretical engagement. Too much reliance on the work of others rather than developing own understanding and application of the material

Descriptive or anecdotal with little or no critical discussion and theoretical engagement. Unconvincing or minimal line of argument. Mostly reliant on the work of others, displaying little understanding or ability to apply the material.

Descriptive or anecdotal work with scanty or no argument. Reliant on the work of others and does not use this to develop own arguments. No critical discussion or theoretical engagement. Little practical and intellectual application.

Work is descriptive and anecdotal. Minimal or no argument. May be entirely reliant on the work of others, with no practical and /or academic application to demonstrate understanding of the material.

No practical, academic or intellectual application.

Argument (Reasoning)

Wide range of relevant and recommended sources used in a profound and consistent way as supporting evidence. Use of cutting-edge sources beyond the recommended texts, including in-depth use of complex material demonstrating advanced independent research.

Wide range of recommended and relevant sources used in an innovative and consistent way to support arguments. In depth use of sources beyond YLJVTTLUKLK [L_[Z KLTVUZ[YH[LZ JYLH[P]L ÅHPY PU independent research.

Wide range of relevant and recommended sources used in an insightful and consistent way as supporting evidence. Some in depth use of sources beyond recommended texts, to demonstrate independent research.

Good range of relevant and recommended sources used in an imaginative and largely consistent way as supportingevidence. Use of some sources beyond recommended texts including more complex materials.

Range of relevant and recommended sources are used, but this may be in an unimaginative or literal manner, particularly at the lower end of the range. Limited use of sources beyond the standard recommended materials.

Limited range of relevant and recommended sources are used, but with some inadequacies in their use and employment as supporting evidence. There may be some reliance on dated or unreliable sources.

Very limited range, use and application of relevant and recommended sources. Demonstrates lack of real understanding. Too much reliance may be placed on dated, unreliable or non-academic sources.

Minimal and inadequate knowledge of relevant and recommended sources. Their use as supporting evidence may be inaccurate, inappropriate or negligible. Reliance on dated, unreliable or nonacademic sources.

Irrelevant or minimal use of recommended sources, resulting in a lack of understanding and inadequate supporting evidence. Non-academic sources that lack intellectual integrity are relied upon.

Based on little or no evidence. Lacks academic and intellectual integrity and quality. Use of non-academic sources limits intellectual understanding.

Evidence

Distinguished visual and written presentation. Highly sophisticated yet clear and accessible style.

Extremely good standards of vocabulary, syntax, spelling and punctuation. Innovative yet logical HUK Å\LU[ VYNHUPZH[PVU HUK KL]LSVWTLU[ VM TH[LYPHSZ /PNOS` HY[PJ\SH[L JVOLYLU[ HUK Z\JJPUJ[ Relationships between statement and sections are precisely made with great clarity. Referencing PZ HJJ\YH[L HUK HWWYVWYPH[L PUUV]H[P]L `L[ SVNPJHS HUK Å\LU[ VYNHUPZH[PVU HUK KL]LSVWTLU[ VM materials. Articulate, coherent and succinct. Relationships between statements and sections are clear and precise. Referencing is accurate and, appropriate.

Outstanding visual and written presentation. Sophisticated yet clear and accessible style. Very good standards of vocabulary, syntax, spelling and punctuation. Possibly Possibly innovative yet SVNPJHS HUK Å\LU[ VYNHUPZH[PVU HUK KL]LSVWTLU[ VM TH[LYPHSZ (Y[PJ\SH[L JVOLYLU[ HUK Z\JJPUJ[ Relationships between statements and sections are clear and precise. Referencing is accurate and, appropriate.

Excellent visual and written presentation. Very clear and accessible style. Good standards of ]VJHI\SHY` Z`U[H_ ZWLSSPUN HUK W\UJ[\H[PVU 3VNPJHS HUK Å\LU[ VYNHUPZH[PVU HUK KL]LSVWTLU[ VM materials. Coherent and succinct. Relationship between statements and sections are very clear.

Referencing is accurate, appropriate and extensive.

Good visual and written presentation. Clear and accessible style. Generally good standards of vocabulary, syntax, spelling and punctuation. Logical organisation and development of materials.

Coherent. Relationship between statements and sections are easy to follow. Referencing is accurate and appropriate.

Generally sound presentation. Style is largely clear and accessible. There may be minor errors of vocabulary, syntax, spelling and punctuation but these should not detract from the overall meaning. There may be inconsistencies in the organisation and development of materials. The relationship between some statements and sections may not be easy to follow. Some points may not be made coherently or succinctly. Work is referenced accurately with few errors.

Acceptable presentation. Some aspects of the style may be unclear. Points may not be made coherently or succinctly. Some errors of vocabulary, syntax, spelling and punctuation but these are not serious distractions from the overall meaning. Some lack of logical development and organisation of the materials. The relationship between some statements and sections may be hard to follow. Work is referenced accurately with some errors.

Weak presentation. Some aspects of the style may be inappropriate, unclear and inaccessible.

Some points will not be made coherently or succinctly. Errors of vocabulary, syntax, spelling and punctuation may seriously detract from the overall meaning. The materials may lack logical development and organisation. The relationship between some statements and sections may be KPMÄJ\S[ [V YLJVNUPZL 3PTP[LK \ZL VM YLMLYLUJLZ HUK ZVTL TH` IL PUHJJ\YH[L

Poor visual and written presentation. The style may be inappropriate, unclear and inaccessible.

Points may not be made coherently or succinctly. Errors of vocabulary, syntax,spelling and punctuation may seriously detract from the overall meaning. The materials may lack logical KL]LSVWTLU[ HUK VYNHUPZH[PVU 9LSH[PVUZOPW IL[^LLU Z[H[LTLU[Z HUK ZLJ[PVUZ TH` IL KPMÄJ\S[ [V recognise. References may be absent, inaccurate or incorrect.

Presentation is inappropriate, unclear and inaccessible. Points are not made coherently or succinctly. Compound errors of vocabulary, syntax, spelling and punctuation seriously detract from the overall meaning. Materials lack logical development. Relationship between statements and sections are hard to recognise. References may be absent or incorrect.

Presentation is inappropriate, unclear and inaccessible. Work is not coherent or succinct. Serious errors of vocabulary, syntax, spelling and punctuation obscure the overall meaning. No logical development or organisation of the materials with few links between statements and sections.

References are absent, incorrect or inaccurate.

Structure and Presentation

Data Exploration and Analysis

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Data Exploration and Analysis

Suggested Reading

There are plenty of business research books in the library, here are some titles to point you in the right direction:

Bryman, A. and Bell, E. (2015) Business Research Methods, 4th Edition, Oxford University Press, Oxford.

Clark, M., Riley, M., Wilkie, E. and Wood, R. (1998) Researching and Writing Dissertations in Hospitality and Tourism, Thomson Business Press, London.

Denscombe, M. (2012) Research Proposals: A Practical Guide, Open University Press, Maidenhead (available as an e-book)

Easterby-Smith, M., Thorpe, R. and Jackson, P. (2015) Management and Business Research, Fifth Edition, Sage, Los Angeles.

Field, A. (2013) Discovering Statistics Using SPSS, Fourth Edition, Sage, London

Jarman, K. (2013) The Art of Data Analysis – How to Answer any Question Using Basic Statistics, Wiley, Chichester.

Morgan, G.,Leech, N., Loeckner, G. and Barrett, K. (2010) SPSS for Introductory Statistics: Use and Interpretation, Routledge, London.

Ng, W. and Coakes, E. (2014) Business Research: Enjoy Creating, Developing and Writing your Business Project, Kogan Page, London (available as an e-book).

Saunders, M. and Lewis, P. (2016) Research Methods for Business Students, Pearson Education, Harlow.

Wilson, N. (2014), Business Research – Enjoying Creating, Developing and Writing Your Business Project, Kogan Page, London.

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Data Exploration and Analysis

Notes

p. 11

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Data Exploration and Analysis

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p. 12



p=0.755 2 x =1.896

Total

Count

Expected Count

Residual ual Std. Resid Count

Expected Count

Residual

Std. Resid

ual

Count

Expected Count

p>0.05

associa