You are required to write a 3,500 word (with 10% over/under) research paper based on your practical project for this module. The paper should contain rigorous evidence and references from the primary and secondary data collection you undertook and the analysis and findings of your practical project proposed from Part 1 – PRES1.
Issue with a flights on Make My Trip they
were giving access to an intermediate page
which create problem for users to wait till
the time they fetch flight details.
Goibibo take the issues overcome the
problem and chase by ensuring a much speed ,
transparent and realistic from search to
booking faster for users
Currently Goibibo having remain issue
around the all services like flights, hotels etc.
because of other online website like Oyo its
dominance in market for goibibo business
• Maintain the complete project data and analyse the
requirement
• Estimate the Cost and Time for Project scope
• Receiving the initial payments
• Sending invoice for total payments
• Improve the project development sources
• development URL, fixable with clients can observe the
ongoing work
• Migration from our development server to users online
server
• Giving overview on add/remove , maintain less cancellation
in backend.
Work-in-progress
AY: 2019/20
CIS7029 Social Media Analytics for Business
Credit Value: 40
Term / Semester:2
Module Leader: Dr Esyin Chew
Assessment Brief
Assessment Title:
The Social Media Analytics for Business Research
PRES1 and WRIT1
Page 1 of 11
Contents
Learning Outcomes …………………………………………………………………………………………………….. 3
EDGE…………………………………………………………………………………………………………………………. 3
Assessment Overview ……………………………………………………….. Error! Bookmark not defined.
Assessment Component / Weighting: e.g. WRIT1 50% ………………………………………………… 3
Title: ……………………………………………………………………………. Error! Bookmark not defined.
Assessment Requirements / Tasks ……………………………………………………………………………….. 3
Assessment Criteria ……………………………………………………………………………………………………. 6
Guidance Notes ………………………………………………………………… Error! Bookmark not defined.
Submission Details ……………………………………………………………………………………………………… 6
Feedback …………………………………………………………………………. Error! Bookmark not defined.
Marking Criteria ……………………………………………………………….. Error! Bookmark not defined.
Additional Information ……………………………………………………………………………………………….. 8
Referencing Requirements (Harvard) ………………………………. Error! Bookmark not defined.
Mitigating Circumstances ………………………………………………………………………………………… 8
Unfair Practice ……………………………………………………………………………………………………… 10
Page 2 of 11
Learning Outcomes
This assessment is designed to demonstrate a student’s completion of the following
Learning Outcomes:
1. Demonstrate an understanding of concepts underlying social media analytics and be
able to apply them appropriately in business settings (Presentation & Written
Paper);
2. Critically evaluate and implement specialist technologies to harvest, analyse and
visualise “social data” from individuals to corporate perspectives (Presentation &
Written Paper);
3. Synthesise and apply social analytics and appropriate techniques on social
information (Presentation & Written Paper);
4. Critically evaluate, design, prototype and implement social media applications and
visualization for business story telling (Written Paper).
EDGE
The Cardiff Met EDGE supports students in graduating with the knowledge, skills, and
attributes that allow them to contribute positively and effectively to the communities in
which they live and work.
This module assessment provides opportunities for students to demonstrate development
of the following EDGE Competencies:
ETHICAL
Critical understanding of the importance of ethical, social, cultural and
legal agendas in social media analytics.
DIGITAL
Advanced digital skills for the fourth industrial revolution: social big
data harvesting, visualisation and business story telling.
GLOBAL
International horizon and benchmarking for social media analytics
corporate specialist software (Tableau) and Python programming
knowledge. The assessment involves creating online Tableau
visualisations on Tableau’s cloud system to be shared with the world
and as a continued portfolio for students.
ENTREPRENEURIAL Use students’ creativity to solve business and/ or public problems and
spot social media entrepreneurship opportunities. The Dragons’ Den
assessment is aimed to develop entrepreneurial characteristics.
Assessment Requirements / Tasks (include all guidance notes)
Assessment Components
There are TWO assessment components you must complete for this module with a
combined total of 100% weighing. The separate components weigh as follows:
1. Part One: Presentation (PRES1) – 20% weighting
2. Part Two: Written paper on practical project (WRIT1) – 80% weighting
Page 3 of 11
Part One
Presentation (PRES1) – 20% weighting (500 word equivalent)
You will be required to deliver a three minute Dragons’ Den in Week 8-10 in your seminar
sessions with your tutor and dragons (selected peers by tutor).
You will play a role of a “social media entrepreneur” with only three minutes to pitch your
“Social Media Analytics for Business” ideas to the “dragons” (selected peers by your tutor),
pretending multimillionaires who are willing to invest their own cash to kick-start the
business. After each pitch, the Dragons have the opportunity to ask questions about your
social media analytics proposal. The times will be strictly managed: A 3 minute presentation
followed by 2 minutes Q&A.
You will specifically be required to present the:
•
•
•
•
•
•
Aims and objectives of your proposed social media analytics for business
The project background (current problem statements, project motivation and etc.)
Your initial design of how your social media analytics research project for business
will be conducted
Discussion on the initial results and visualisation obtained (if applicable).
The proposed project scope, initial designs and any work-in-progress (i.e. third-party
tools / APIs / code demo can also be presented)
Importantly, the “so what” insights of the business story telling must be presented i.e. the value for money of your business pitch!
The subject of the social data scrapping and the results analysed are left for the student to
choose. For examples (and not limited to):
▪
▪
▪
▪
▪
You could scrap for keywords related to some corporate data, e.g. KFC, Tableau or
any company of your choice on its corporate review website and look for customers’
experiences or customers’ views analytics.
You could scrap for keywords related to some public data on Twitter, i.e. McDonald,
Starbucks or any company of your choice and look for public perceptions, market
trends or brand monitoring.
You could scrap customers’ reviews for products data from Asda, Tesco, Amazon, or
any online shop for competitor analysis or product review comparison.
You could harvest overall sentiment towards an international brand or a global
company and provide data analytics and visualisations for a business story telling.
Any social media analytics topic of your own choice, e.g. the company you worked
for.
Please ensure you discuss your topic with your tutor in the studio/seminar as early as
possible before you begin working on it.
Page 4 of 11
Part Two
Written paper on practical project (WRIT1) – 80% weighting due in Week 11
You are required to write a 3,500 word (with 10% over/under) research paper based on
your practical project for this module. The paper should contain rigorous evidence and
references from the primary and secondary data collection you undertook and the analysis
and findings of your practical project proposed from Part 1 – PRES1.
Rationale
As part of practical project in this module and learnt from the weekly studios, you will be
harvesting a suitable dataset using the relevant tools, i.e. Tableau, Python, Facebook or
Twitter API or third party Tool(s) and extracting relevant information from the results, as
proposed in your Part 1 Business Pitch.
Therefore, you should start your paper with the motivation or rationale of your research,
especially the business aim of your project. The design and approach of your project such as
the reasons for the choice of social web harvesting, scope for strategic or tactical decision
making, business values, public interest, marketing campaign, product reviews, branding
and marketing, customers’ preferences or other etc.
Research Tools and Methods
You need to discuss your research into suitable tools and/ or APIs and the justification for
your choice. Based on this, you should then document the design of your project and show
clearly how your research project communicates with any third-party service or API.
Results and Visualisation
You should discuss the results with necessary business or social implications and relate that
back to the motivation or rationale of your project. Visualisation is very important, so the
report should also contain suitable visualisations for your business storytelling out of the
social data you collected.
Limitations and Implications
In addition, project limitations and recommendations are desirable.
Conclusion and Appendices
Finally, draw a conclusion with key results to nicely conclude your web harvesting research.
You are encouraged to compare various technical tools and techniques to demonstrate the
social media analytics skills you have learnt.
You can implement your social web harvesting research project using any suitable language
/ technology / third-party tools you see fit. You can hard-code queries or you can provide a
suitable front-end where users can enter search keywords. In addition, you can show the
results in any suitable form, e.g. tables, various forms of innovative graphs or information
overlaid on a map. The final visualisation needs to be published on Tableau Public and
include the link and evidences in the paper as Appendixes.
Page 5 of 11
Additional Information
Referencing Requirements (Harvard)
The Harvard (or author-date) format should be used for all references (including images).
Further information on Referencing can be found at Cardiff Met’s Academic Skills website.
Assessment Criteria
Part 1 (PRES1): Three Minutes Dragons’ Den as a Proposal for the Part 2
Innovation, creative and clear presentation
Understanding of the social media analytics for business design and
anticipated implementation, APIs and techniques used
“So What?” aspects for the business value / anticipated research impact
Part 2 (WRIT1): Written Research Paper of the Practical Project
▪ Title Page: A title of not more than eight words should be provided
and the student’s name and student ID.
▪ Biography: a brief professional biography of not more than 100
words about you.
▪ Abstract (less than 200 words): students must supply a structured
abstract in their submission, set out under 4-7 sub-headings
o Purpose (mandatory)
o Design/methodology/approach (mandatory)
o Findings (mandatory)
o Social or Business implications: the original value of the
research (mandatory)
o Research limitations/implications (if applicable)
Introduction: Motivation or Rationale of the project
Design and discussion of data sources / tools/ APIs and justification of
choice
Key Results, Visualisation and Business Story Telling
Conclusions, Project Limitation and Recommendation
References and Report Format
Appendixes, e.g. A link to Tableau Public of your visualisation work; Screen
shots of data source file, interfaces or third party tools you used or Source
code. Please zip everything and submit via a separate submission link on
Moodle. Feel free to put the key screen shots in the main report as well.
Total:
Submission Details
Please see Moodle for confirmation of the Assessment submission date.
Submission will be by 4:00pm on the deadline day (Week 11).
Page 6 of 11
20%
5%
10%
5%
80%
10%
5%
10%
15%
10%
10%
10%
100%
Any assessments submitted after the deadline will not be marked and will be recorded as
a Non-Attempt.
Submit the assignment online through Moodle, including
(i)
a research paper (include the Tableau public link, a link to the Youtube
Presentation, references and appendixes);
(ii)
a zip folder including final presentation slides and appendixes, such as the
source files, e.g. Excel sheets, interfaces or third party tools you used or
Python source code.
The assessment must be submitted as a zip file / pdf / word document through the Turnitin
submission point in Moodle
Submit the assignment online through Moodle, including
(i)
a research paper (include the Tableau public link, a link to the Youtube
Presentation, references and appendixes);
(ii)
a zip folder including final presentation slides and appendixes, such as the
source files, e.g. Excel sheets, interfaces or third party tools you used or
Python source code.
The assessment must be submitted as a zip file / pdf / word document through the Turnitin
submission point in Moodle
Summative feedback for the Part 2 assessment will be provided electronically via Moodle,
and will normally be available 4 working weeks after initial submission. The feedback return
date will be confirmed on Moodle.
Feedback will be provided in the form of a Tunitin-GradeMark rubric and supported with
comments on your strengths and the areas in which you could improve.
All marks are preliminary and are subject to quality assurance processes and confirmation at
the Examination Board.
Further information on the Academic and Feedback Policy in available in the Academic
Handbook (Vol 1, Section 4.0)
Page 7 of 11
Marking Criteria for PRES1
Marking Criteria for WRIT1
Mark Range
Criteria
80 – 100
An excellent research paper is given that shows evidence of extensive
research: brilliant motivation/rationale of the project with an
excellent design, discussion of data sources / APIs and justification of
choice. A well written, efficient, functional project for Social Media
Analytics for Business Research Project has been developed and
thorough findings with visualisations and storytelling are evident.
Excellent key results with limitations and conclusions are depicted. An
excellent presentation is also given and the student demonstrates a
brilliant understanding of the implementation, APIs and techniques
used. The format and references are at the top end of this band – the
work is of publishable quality.
70 – 79
An excellent academic paper is given that shows evidence of detailed
research: brilliant motivation/rationale of the project with an
excellent design, discussion of data sources / APIs and justification of
choice. A well written, efficient, functional Social Media Analytics for
Page 8 of 11
Business Research Project has been developed with clear evidence of
findings with visualisations and storytelling. A clear presentation is
also given and the student demonstrates an excellent understanding
of the implementation, APIs and techniques used. The format and
references are excellent with minor typos and errors.
60 – 69
A very good report is given that shows evidence of detailed research:
good motivation/rationale of the project with a detailed design,
discussion of data sources / APIs and justification of choice. A well
written, functional Media Analytics for Business Research Project has
been developed with good evidence of findings with visualisations
and storytelling. A clear presentation is also given and the student
demonstrates a very good understanding of the implementation, APIs
and techniques used. The format and references are very good with
some typos and errors.
50 – 59
A good research paper is given that shows evidence of research: some
motivation/rationale of the project with a good design, discussion of
data sources / APIs and justification of choice, though these could be
expanded upon. A functional Social Media Analytics for Business
Research Project has been developed with some evidence of findings
with visualisations and storytelling. However, the visualisations and
discussion could be enhanced. A good presentation is given and the
student demonstrates a good understanding of the implementation,
APIs and techniques used. The format and references are good with
some typos and errors.
40 – 49
A basic research paper is given that shows some evidence of research:
limited or no motivation/rationale of the project with a basic design,
limited discussion of data sources / APIs and justification of choice these need to be expanded upon. A functional Social Media Analytics
for Business Research Project has been developed but there is little to
no evidence of visualisations. Only a basic presentation is given and
the student only demonstrates a basic understanding of the
implementation, APIs and techniques used. The format and
references are basic with major typos and errors.
35 – 39
A very basic research paperis given that shows little to no evidence of
research: no motivation/rationale of the project with only a very basic
design, data sources / APIs and justification of choice. A limited Social
Media Analytics for Business Research Project has been developed
that does not meet all functional requirements and little to no
evidence of testing and visualisations is evident. Only a basic
presentation is given and the student demonstrates little to no
Page 9 of 11
understanding of the implementation, APIs and techniques used. The
format and references are poor with major typos and errors.
Under 35
Limited or no evidence of research: no motivation/rationale of the
project with no design, data sources / APIs and justification of choice
is given. No functional Social Media Analytics for Business Research
Project has been submitted with no testing and visualisations and no
meaningful results produced. No presentation is given. The format is
very poor and no or little references with major errors.
Mitigating Circumstances
If you have experienced changes or events which have adversely affected your academic
performance on the assessment, you may be eligible for Mitigating Circumstances (MCs).
You should contact your Module Leader, Personal Tutor or Year Tutor in the first instance.
An application for MCs, along with appropriate supporting evidence, can be submitted via
the following link to the MCs Dashboard
Applications for MCs should ideally be submitted as soon as possible after circumstances
occur & at the time of the assessment. Applications must be submitted before the relevant
Examination Board.
Further information on the Mitigating Circumstances procedure is available in the Academic
Handbook (Volume 1, Section 5)
Unfair Practice
Cardiff Metropolitan University takes issues of unfair practice extremely seriously. The
University has distinct procedures and penalties for dealing with unfair practice in
examination or non-examination conditions. These are explained in full in the University’s
Unfair Practice Procedure (Academic Handbook: Vol 1, Section 8)
Types of Unfair Practice, include:
Plagiarism, which can be defined as using without acknowledgement another person’s
words or ideas and submitting them for assessment as though it were one’s own work, for
instance by copying, translating from one language to another or unacknowledged
paraphrasing. Further examples include:
•
•
Use of any quotation(s) from the published or unpublished work of other persons,
whether published in textbooks, articles, the Web, or in any other format, which
quotations have not been clearly identified as such by being placed in quotation
marks and acknowledged.
Use of another person’s words or ideas that have been slightly changed or
paraphrased to make it look different from the original.
Page 10 of 11
•
•
•
•
Summarising another person’s ideas, judgments, diagrams, figures, or computer
programmes without reference to that person in the text and the source in a
bibliography or reference list.
Use of services of essay banks and/or any other agencies.
Use of unacknowledged material downloaded from the Internet.
Re-use of one’s own material except as authorised by the department.
Collusion, which can be defined as when work that that has been undertaken with others is
submitted and passed off as solely the work of one person. An example of this would be
where several students work together on an assessment and individually submit work which
contains sections which are the same. Assessments briefs will clearly identify where joint
preparation and joint submission is specifically permitted, in all other cases it is not.
Fabrication of data, making false claims to have carried out experiments, observations,
interviews or other forms of data collection and analysis, or acting dishonestly in any other
way.
Page 11 of 11
AY: 2019/20
CIS7031 – Programming for Data Analysis
20 Credit Hours
Semester 2
Module Leader: Imtiaz Khan
Assessment Brief
Assessment Title:
Employment in Wales
WRIT1 100 %
HAND-OUT DATE:
HAND-IN DATE: 24 May 2020
Page 1 of 8
Contents
Learning Outcomes ………………………………………………………………………………………. 3
EDGE …………………………………………………………………………………………………………. 3
Assessment Requirements / Tasks (include all guidance notes) …………………………. 4
Assessment Criteria ……………………………………………………………………………………… 5
Submission Details ……………………………………………………………………………………….. 5
Feedback ……………………………………………………………………………………………………. 6
Marking Criteria ……………………………………………………………………………………………. 6
Additional Information……………………………………………………………………………………. 7
Referencing Requirements (Harvard) …………………………………………………………… 7
Mitigating Circumstances……………………………………………………………………………. 7
Unfair Practice ………………………………………………………………………………………….. 7
Page 2 of 8
Learning Outcomes
This assessment is designed to demonstrate a student’s completion of the following
Learning Outcomes:
•
•
•
•
Critically analyse and evaluate various statistical and computational
techniques for analysing datasets and determine the most appropriate
technique for a business problem;
Critically evaluate, develop and implement solutions for processing datasets
and solving complex problems in various environments using relevant
programming paradigms;
Evaluate and apply key steps and issues involved in data preparation,
cleaning, exploring, creating, optimizing and evaluating models;
Evaluate and apply aspects of data science applications and their use.
EDGE
The Cardiff Met EDGE supports students in graduating with the knowledge, skills, and
attributes that allow them to contribute positively and effectively to the communities in
which they live and work.
This module assessment provides opportunities for students to demonstrate
development of the following EDGE Competencies:
ETHICAL
Students will be required to consider Ethical implication of
their analysis and follow the necessary ethical approval
processes while addressing problems associated with the
assessment.
DIGITAL
Students will be required to demonstrate digital skills in the
collation of data and analysis for their project.
GLOBAL
Students will demonstrate an awareness of the global
context and apply this to their assessment
ENTREPRENEURIAL Students will also demonstrate their developed
entrepreneurial through working under their own initiative,
formulating and presenting recommendations in order to
solve an authentic and complex problem associated with
the module.
Page 3 of 8
Assessment Requirements / Tasks (include all guidance notes)
This assignment will use employment data of Wales from the StatsWales data source.
This dataset provides workplace employment estimates, or estimates of total jobs, for
Wales and its NUTS2 areas, along with comparable UK data disaggregated by
industry section.
For this assignment students will undertake a data analysis and machine learning
approach to reveal the workplace employment landscape of Wales.
1. Data processing
1.1. Download the dataset for the period 2009 – 2018 and create a dataframe that
concatenates Wales (total) employment value only.
1.2. Check for any null value or outlier. If found replace that with mean value.
1.3. Change the name of the industries as bellow
The final dataframe should look like following
Industry
Agriculture
Production
Construction
Retail
ICT
Finance
Real_Estate
Professional_Service
Public_Adminstration
Other_Service
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2. Data analysis
For each question provide graph/chart along with your own interpretation (~ 50 words)
2.1. Which industry employed highest and lowest workers over the period?
2.2. Which industry has the highest and lowest overall growth over the period?
2.3. Which years are the best and worst performing year in relation to number of
employment. (highest and lowest employment)
3. Visual analysis
Create a dynamic scatter/bubble plot showing the change of workforce number over
the period using Plotly express.
4. PCA/Correlation
4.1. Undertake a PCA (PC=2; columns should be like PC1, PC2, Industry) and
produce a scatter plot. Write your interpretation about the plot and in relation
to the analysis of section 2 & 3 (for example which industries are correlated
over the years as well as in PCA etc.)
4.2. Make a year wise correlation for each industry. Does the aforementioned
industries are also correlated over the years? Explain your answer.
5. Clustering (k means & hierarchical)
5.1. Using the best and worst performing year column’s employment data (2.3)
undertake a K means clustering analysis (K=2 & 3) and identify industries
cluster together. Write your own interpretation (~100 words).
Page 4 of 8
5.2. Using the same dataset (best & worst performing) create a hierarchical cluster.
Compare the cluster with k means clusters.
6. Discussion
Provide a brief discussion (~ 300 words) on employment landscape of Wales based
on the employment data analysis results.
Assessment Criteria
1.1 Data preparation
1.2 Data preparation
1.3 Data preparation
2.1 Data analysis
2.2 Data analysis
2.3 Data analysis
3 Visual analysis
4.1 PCA
4.1 Correlation
5.1 Clustering
5.2 Clustering
6 Discussion
05
05
05
05
05
05
20
10
10
10
10
10
Submission Details
Please see Moodle for confirmation of the Assessment submission date.
Presentation will be on 4:00 PM of submission date.
Any assessments submitted after the deadline will not be marked and will be
recorded as a Non-Attempt.
The assessment must be submitted as a zip file / pdf / word document through the
Turnitin submission point in Moodle
Your assessment should be titled with your Student ID Number, module code and
assessment id, e.g. st12345678 CIS4000 WRIT1
Page 5 of 8
Feedback
Feedback for the assessment will be provided electronically via Moodle, and will
normally be available 4 working weeks after initial submission. The feedback return
date will be confirmed on Moodle.
Feedback will be provided in the form of a rubric and supported with comments on
your strengths and the areas which you improve.
All marks are preliminary and are subject to quality assurance processes and
confirmation at the Examination Board.
Further information on the Academic and Feedback Policy in available in the Academic
Handbook (Vol 1, Section 4.0)
Marking Criteria
70 – 100%
(1st)
60-69%
(2:1)
50-59%
(2:2)
40-49%
(3rd)
35-39%
(Narrow
Fail)