IST2334: Web and Network Analytics SCHOOL OF ENGINEERING AND TECHNOLOGY

INSTRUCTIONS TO CANDIDATES

ANSWER ALL QUESTIONS

You are required to select and analyze a dataset from https://www.freecodecamp.org/news/https-medium-freecodecamp-org-bestfree-open-data-sources-anyone-can-use-a65b514b0f2d/. Based on your own interests, select THREE distinctive areas of analysis..

For each area, explain the rationale of the selection. Apply suitable techniques to process and analyse the data using R. Take a deep look at the obtained results and explain them critically.

(Hint: you can explain them from a different perspective, such as observable trends, the significance of the result, meaningful anomalies etc.)

 

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__________________________________________________________________________

SCHOOL OF ENGINEERING AND TECHNOLOGY

FINAL ASSESSMENT FOR THE BSC (HONS) INFORMATION SYSTEMS (BUSINESS

ANALYTICS); YEAR 3

ACADEMIC SESSION AUGUST 2021; SEMESTER 7, 8, 9

IST2334: WEB AND NETWORK ANALYTICS

DEADLINE:3

rd

 DECEMBER 2021 4:00PM

GROUP: __________________________________________________________

INSTRUCTIONS TO CANDIDATES

 

 This project will contribute 50% to your final grade.

 This is a group project. Each group consists of 4-5 members.

IMPORTANT

The University requires students to adhere to submission deadlines for any form of assessment.

Penalties are applied in relation to unauthorized late submission of work.

- Coursework submitted after the deadline but within 1 week will be accepted for a

maximum mark of 40%.

- Work handed in following the extension of 1 week after the original deadline will be

regarded as a non-submission and marked zero.

Students’ declaration:

 (Name) (ID) (Signature)

We 1)

2)

3)

4)

5)

received the assignment and read the comments.

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Academic Honesty Acknowledgement

“We (names stated above) verify that this paper contains entirely my own work. I have not

consulted with any outside person or materials other than what was specified (an interviewee,

for example) in the assignment or the syllabus requirements. Further, I have not copied or

inadvertently copied ideas, sentences, or paragraphs from another student. I realize the

penalties (refer to page 16, 5.5, Appendix 2, page 44 of the student handbook diploma and

undergraduate programme) for any kind of copying or collaboration on any assignment.”

 1)

 2)

 3)

 4)

 5)

….................................. (Student’s signature / Date)

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Description of the project (100 marks)

You are required to select and analyse a dataset from

https://www.freecodecamp.org/news/https-medium-freecodecamp-org-bestfree-open-data-sources-anyone-can-use-a65b514b0f2d/.

Based on your own interests, select THREE distinctive areas of analysis. For each

area, explain the rationale of the selection. Apply suitable techniques to process

and analyse the data using R. Take a deep look at the obtained results and explain

them critically. (Hint: you can explain them from different perspective, such as

observable trends, significance of the result, meaningful anomalies etc.)

Each group must submit a report which includes the following sections:

1) Introduction and motivation of the work. (10 marks)

2) Elaboration of the data sets. (10 marks)

3) Presentation of the three analyses – techniques used, rationale, results and

explanation. (15 marks each total 45 marks)

4) Coding in R. (10 marks)

5) Lessons learned and Conclusion. (5 marks)

6) An individual reflection for each student on the learning and appreciation

of the techniques used and analysis carried out in this project. (10 marks)

On top of the above, 10 marks will be graded based on the language, formatting

and structure of the report.

Note: If you wish to use any dataset from Kaggle or similar and carry out analysis

as described in the Kaggle page, you must not use the analysis presented in these

tutorials/guides as your own. If analysis performed in your work is found identical

with any resources, marks will not be awarded. Please give reference to the 

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Kaggle page you have selected, and state CLEARLY the differences your work has

compared to the Kaggle page where your data and inspiration came from. Failure

to cite the source of your data and references will be considered as a case of

plagiarism.

What is expected from each section of report?

1) Introduction and motivation of the work

How well you introduce the idea and motivation why this dataset is interesting

to you. You can elaborate what you expect to see, why is it significant, what

caused the curiosity etc.

2) Elaboration of the data sets

In this section, you should elaborate the dataset based on the information

provided by the data provider, and also your initiative observation. You can

include snapshot of the data, especially if that snapshot hints something that

can be linked to your motivation and interests. Graphs and summary of the

data can be used but there should not be too many.

3) Presentation of the analysis – techniques used, rationale, results and

explanation

This section should begin with the introduction of the THREE selected areas of

analysis. You can explain what outcome you expect to find/present. The

analysis should be meaningful and lead to useful findings.

This will be followed by the elaboration of each analysis. You can begin with

the rationale and aim and then straight to the analysis. Visualizing using any

kind of plots/charts/graphs are required, but do not forget about elaboration

of these visualizations. 

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4) Coding in R

You should include the scripts you have coded to perform the analyses on the

data set together with a description of them highlighting the consistency of

the code with the analysis part. The submitted code will be marked based on

correctness, originality, clarity, and appropriate comments to describe

different functions and purpose of the code. The code is expected to work on

any computer. In other words, the lecturer/examiner should be able to

execute the submitted code without making any modification.

5) Lessons learned

In this section, you should conclude lesson learned through this assignment.

Questions to ask yourself (and help to write this section): What have I learned?

What mistakes I have made and how will I do things differently? Did you see

something that should be improved? Have you faced with any major bugs in

your program? How did you manage to fix those bugs?

6) An individual reflections on the learning and appreciation of the techniques

used and analysis done

Reflections will be similar to lessons learned but focus more on “How is your

life changed” kind of answer. Did you see something throughout the process

you didn’t expect? Did you understand the hidden power/potential of a certain

function/ tool/ process/ analysis? How will these lessons from you and your

group mates (stated in (5)) will help you to be a better analyst?

7) Formatting, grammar, and style of writing

This requirement is to judge how well has the report been written in terms of

writing and structure. An overall impression on formatting, grammar and

writing style will be considered for grading.

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Submission

Marks will be generously deducted for not following submission instructions:

1. Submit a single zip file via eLearn. The name of the zip file must be the

subject code along with your group ID separated by an underscore “_”.

Ex: IST233 - G1.zip

2. Your zip file must include

a) R file which includes your source code

b) PDF report which consists of SIX sections as stated in the description of

the project

Note: Each group must submit the report to the eLearn. Individual submission is

NOT required. Email submissions will not be marked.

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Marking rubrics

Excellent Good Adequate Unsatisfactory

9 - 10 6 - 8.99 4 - 5.99 0 – 3.99

Introduction and motivation of

the work [10%]

9 - 10 6 - 8.99 4 - 5.99 0 – 3.99

Elaboration of the data sets

[10%]

13.5 - 15 9 – 13.49 6 – 8.99 0 – 5.99

Presentation of the analysis –

techniques used, rationale,

results and explanation

[15% x 3]

9 - 10 6 - 8.99 4 - 5.99 0 – 3.99

Coding in R [10%]

4.5 - 5 3 – 4.49 2 – 2.99 0 – 1.99

Lesson learned

[5%]

9 - 10 6 - 8.99 4 - 5.99 0 – 3.99

Individual Reflections [10%]

9 - 10 6 - 8.99 4 - 5.99 0 – 3.99

Formatting, grammar, and style

of writing [10%]

Attachments:

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