Project report and video on data processing and analytics topics

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ASSESSMENT TASK

This is a group assignment with individual elements where you are asked to design and implement databases for selected use cases and perform data analytics on given datasets.

A. The first part of the assignment focuses on your understanding and implementation of various database technologies.

Choose one of the given database development scenarios and:

1. Develop an Entity-Relationship model of the information requirements for this scenario.

2. Translate your model into an equivalent set of relations. Specify all relation headings, indicating primary and foreign keys.

3. Implement at least three related entities from your database in SQL, MongoDB and Neo4J (could be different three entities for each technology) and generate sample data for all of the implementations. Show samples of generated data.

4. Explain why you chose the particular entities to implement with each technology.

5. Come up with 5 use cases for each database technology and implement those using queries. Show the queries code and the output.

Include the answers to each of the five points above in your technical report.

B. In an era of growing data complexity and volume, feature selection and construction techniques play a key role in understanding our data in helping reduce the dimensionality and improve learnability in data analytics problems. Both for data and big data processing and analytics feature selection techniques are important for reducing the time required to build machine learning models and improving the performance of these algorithms. Moreover, principal component analysis is an important algorithm used in data and big data processing for the purpose of data visualisation, as well as for dimensionality reduction and for gaining insight in the knowledge hidden in the data.

For the submission, you are given 3 datasets and you are asked to define the classification problem of your choice (you can group classes if you think it is appropriate), select one dataset and perform the following tasks:

1. Define the training and testing set for your dataset.

2. Apply two classification algorithms of your choice and compare the performance of them for the dataset your choice.

3. Apply Principal Component Analysis to the dataset and explain its outcome. How does the number of principal components affect the percentage of variance covered for this dataset?

4. Apply a feature selection algorithm of your choice and compare the performance of one algorithm of your choice (it can be one of the two you used in question 2) before and after applying the feature selection algorithm.

Faculty of Science and Technology - Department of Computing and Informatics

Unit Title: Data Processing and Analytics

Assessment Title: Project report and video on data processing and analytics topics

Unit Level: 7

Assessment Number: 1 of 1

Credit Value of Unit: 20

Date Issued: 13/01/2020

Marker(s): Theodoros Kostoulas and Rashid Bakirov

Quality Assessor: Hamid Bouchachia

Submission Location: Turnitin

Feedback method: Turnitin

This is group assignment with individual elements which carries 100% of the final unit mark

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5. Discuss the challenges and implications regarding the time required to build the required

models. Compare those times when using feature selection algorithms and when no such

algorithms are used.

You must use significance test on the test set, to evaluate the results. Use the AUC (area under

curve) as a metric for comparing the performance. Include the answers to each of the five points

above in your technical report.

Groups

• It is your responsibility to decide whom you would like to work with. You also need to be prepared

for any unforeseen circumstances that may arise when working as a team. You should inform the

unit leader via email about your group members within three weeks of the issue date of this

coursework. Otherwise, the remaining students will be randomly assigned into groups.

• Each group should consist of four members. If you would like to form a group of a different size

(minimum: 3, maximum: 5), you can do this in exceptional circumstances only, and you must

explain these circumstances in your report. In that case, you will be still marked as if you worked

in a group of four. That is, there will be no compensation considered during marking.

• We assume that all members in the group will receive equal marks for the technical report (80%)

of the assignment. If group members do not agree with this default mark distribution and raise an

issue (e.g. somebody not-engaged or made a poor contribution), the unit leader will contact the

group members via email to resolve the issue and marks can be reallocated.

SUBMISSION FORMAT

There will be two deliverables for this assignment:

1. Technical report (80%, equivalent of 6000 words) - Group submission. The technical report needs

address both tasks A and B.

2. Individual video (20%, equivalent of 600 words) – Individual submission. Prepare a five minute

video, where you discuss your role in the group, your contribution to the final submission and the

steps that you followed for completing the tasks. The links to the video of every group member

should be included in the project report.

MARKING CRITERIA

The following criteria will be used to assess the assignment:

Technical report:

Task A: 40%

Subtask HIGH MARK MEDIUM MARK LOW MARK

1. Entity

Relationship

Model (10%)

All or most of the

required entities,

attributes, relations

and cardinalities

(including the

optional participation)

are correctly

identified and

described.

Many of the required entities,

attributes, relations and

cardinalities (including the

optional participation) are

correctly identified and

described. ERD generally

makes sense.

Few of the required entities,

attributes, relations and

cardinalities (including the

optional participation) are

correctly identified and

described. ERD does not

address the scenario.

2. Relational

Schema

(5%)

ERD is mostly

correctly translated

into relational

schema, including

the specification of

primary and foreign

keys.

ERD is translated into

relational schema, with some

mistakes and some missing

keys.

ERD is translated into

relational schema, with

many mistakes and missing

keys.

3. Databases

and sample

Designed databases

mostly correspond to

Designed databases

generally correspond to the

Designed databases mostly

do not correspond to the

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data (10%) the scenario and the

proposed ERD.

All of the entities

have sufficient

sample data, and it is

clearly shown.

scenario and the proposed

ERD.

All of the entities have

sample data with some

omissions and ambiguities.

scenario and the proposed

ERD.

There are significant

omissions in sample data,

some or all of the entities do

not have any sample data.

4. Design

choices

explanation

(5%)

Explanation of the

choice of entities for

database

technologies is clear

and fits the

characteristics of

these technologies.

Explanation of the choice of

entities for database

technologies is generally

clear and the characteristics

of these technologies are

considered.

Explanation of the choice of

entities for database

technologies is unclear and

contradicts the

characteristics of these

technologies.

5. Use cases

and queries

(10%)

Use cases are clear

and the queries

address them well.

Queries are correct

and deliver valid

results, which are

clearly shown.

Use cases are generally

clear and the queries

address them appropriately

for most of the cases. Many

queries are correct and

deliver some results, which

are shown.

Use cases are unclear and

the existing queries do not

address them appropriately

for most of the cases.

Queries are

incorrect/missing and the

results are not shown well.

Task B: 40%

Subtask HIGH MARK MEDIUM MARK LOW MARK

Introduction

(5%)

Detailed and

concise to the

problem.

Generally clear but not to

the point.

Not relevant or inadequate.

System

description

(10%)

Detailed and

elaborated.

Vague and not described

in detail.

Not detailed, with mistakes

or parts missing.

Experimental

setup (10%)

Detailed and

elaborated.

Vague and not described

in detail.

Not detailed, with mistakes

or parts missing.

Experimental

results (5%)

Detailed and

elaborated.

Vague and not described

in detail.

Not detailed, with mistakes

or parts missing.

Discussion on

the results

(10%)

Thorough

elaboration on the

results with

meaningful

explanation.

Vague and not discussed

in detail.

Not discussed or analysed

enough.

Individual video 20%:

Subtask HIGH MARK MEDIUM MARK LOW MARK

Clarity of

explanation (10%)

Detailed and

concise to the

problem.

Generally clear but not to

the point.

Not relevant or inadequate.

Connection to

the tech report

( (5%)

Detailed and

elaborated.

Vague and not described

in detail.

Not detailed, with mistakes

or parts missing.

Summary and

c conclusions (5%)

Thorough

elaboration on the

results with

meaningful

explanation.

Vague and not discussed

in detail.

Not discussed or analysed

enough.

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LEARNING OUTCOMES

This assignment tests your ability to:

1. Perform and critically analyse data modelling.

2. Understand the underlying technology of various database systems.

3. Gain critical understanding of Data analytics’ challenges.

4. Gain critical understanding of the most significant pattern recognition algorithms for dealing with Data

and Big Data.

5. Be able to interpret the results from Data and Big Data analytics’ algorithms and use the appropriate

methods for reporting the results.

QUESTIONS ABOUT THE BRIEF

Any issues about the assignment can be raised with the lecturer during lectures/seminars or by

appointment. Email will be used for handling questions about the brief when no seminar/lab session is

scheduled between the time the questions arise and the submission deadline.

Signature Marker Rashid Bakirov and Theodoros Kostoulas

HELP AND SUPPORT

• If a piece of coursework is not submitted by the required deadline, the following will apply:

1. If coursework is submitted within 72 hours after the deadline, the maximum mark that can be

awarded is 50%. If the assessment achieves a pass mark and subject to the overall performance

of the unit and the student’s profile for the level, it will be accepted by the Assessment Board as

the reassessment piece. The unit will count towards the reassessment allowance for the level;

This ruling will apply to written coursework and artefacts only; This ruling will apply to the first

attempt only (including any subsequent attempt taken as a first attempt due to exceptional

circumstances).

2. If a first attempt coursework is submitted more than 72 hours after the deadline, a mark of zero

(0%) will be awarded.

3. Failure to submit/complete any other types of coursework (which includes resubmission

coursework without exceptional circumstances) by the required deadline will result in a mark of

zero (0%) being awarded.

The Standard Assessment Regulations can be found on Brightspace.

• If you have any valid exceptional circumstances which mean that you cannot meet an assignment

submission deadline and you wish to request an extension, you will need to complete and submit the

Exceptional Circumstances Form for consideration to your Programme Support Officer (based in

C114) together with appropriate supporting evidence (e.g, GP note) normally before the

coursework deadline. Further details on the procedure and the exceptional circumstances form can

be found on Brightspace. Please make sure that you read these documents carefully before

submitting anything for consideration. For further guidance on exceptional circumstances please see

your Programme Leader.

• You must acknowledge your source every time you refer to others’ work, using the BU Harvard

Referencing system (Author Date Method). Failure to do so amounts to plagiarism which is against

University regulations. Please refer to http://libguides.bournemouth.ac.uk/bu-referencing-harvardstyle

for the University’s guide to citation in the Harvard style. Also be aware of Self-plagiarism, this

primarily occurs when a student submits a piece of work to fulfill the assessment requirement for a

particular unit and all or part of the content has been previously submitted by that student for formal

assessment on the same/a different unit. Further information on academic offences can be found on

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Brightspace and from https://www1.bournemouth.ac.uk/discover/library/using-library/howguides/

how-avoid-academic-offences

• Students with Additional Learning Needs may contact Learning Support on

www.bournemouth.ac.uk/als

Disclaimer: The information provided in this assignment brief is correct at time of publication. In the

unlikely event that any changes are deemed necessary, they will be communicated clearly via e-mail and

Brightspace and a new version of this assignment brief will be circulated.