Assignment Prompt & Instructions
3000 words
**files (1)**
– ## ReportBigDataAnalyticsUsingAIFeb23.pdf.pdf
**## ReportBigDataAnalyticsUsingAIFeb23.pdf.pdf**
1
Learner declaration
I certify that the work submitted for this assignment is my own and research
sources are fully acknowledged.
DEGREE: BSc Computer Science and Digitisation
Module: Big Data Analytics using AI
\_\_\_________________________________________________________
Assignment Title:
Assignment Type: Report
Word Limit: 3000 words (+/- 300)
Weighting: 100%
Issue Date: 4/9/2025
Submission Date: 30/9/2025
Feedback Date: 21/10/2025
Plagiarism:
When submitting work for assessment, students should be aware of the
InterActive/Canvas guidance and regulations in concerning plagiarism. All submissions
should be your own, original work. Please note that you must not submit the same
assignment for two different modules within your course.
You must submit an electronic copy of your work. Your submission will be
electronically checked.
Harvard Referencing:
The Harvard Referencing System must be used. The Wikipedia, UKEssays.com or
similar websites must not be used or referenced in your work.
Student signature: Date:
SmartCity UrbanMobilityAnalysisusingHadoopand
Predictive AI
Introduction
2
Phase 2: Implementation & Analysis (LO 3) – 50% of Total Grade
LO1. Demonstrate the understanding of basic concepts of Big Data, its importance and need
in business context.
LO2. Explain the various components of Hadoop and HFDS along with their role in the Big Data
ecosystem.
LO3. Summarize the learning on Big Data analytics using Yarn, HDFS and MapReduce.
Assessment Criteria: Weighting 100%
3000 words
Task Description: You are a Data Engineer tasked with designing and implementing a proof- of-
concept Big Data analytics solution for a city's transport authority.
Scenario: The city council wants to analyse urban mobility patterns using data from road
sensors, taxi trips, or public transit records. The objective is to identify congestion
hotspots, understand their causes, and predict future traffic patterns to enable proactive
traffic management and better infrastructure planning.
Phase 1: Conceptual Design & Architecture (LO 1, LO 2) – 20% of Total Grade
The goal of this assignment is to provide you with hands-on experience in designing and
implementing a Big Data analytics solution that incorporates a predictive AI component.
You will address a hypothetical smart city challenge by using the Hadoop ecosystem to
process large-scale data and derive actionable insights for urban planning. This
assignment requires you to design a solution using Hadoop, HDFS, YARN, and
MapReduce to analyse transportation data. The final step involves using the processed
data to train a simple predictive model, thereby connecting Big Data processing with AI
applications. This will help you understand the end-to-end pipeline from raw data to
business intelligence in a modern context.
Learning Outcomes:
1. Business Context and Problem Statement (5%)
•
•
Describe the smart city scenario, focusing on the challenges of urban mobility.
Define a clear problem statement. For example: "To analyse historical traffic data to
predict the likelihood of traffic congestion at key intersections based on time of day
and day of the week".
Explain how solving this problem provides tangible value to the city (e.g., reduced
commute times, lower pollution, improved public safety).
•
2. Hadoop Ecosystem and Architecture (15%)
•
•
•
•
Explain why a Big Data approach is necessary for this scenario.
Identify the roles of HDFS, YARN, and MapReduce in your proposed solution.
Justify your choice of these components for the defined problem.
Create a clear architectural diagram illustrating how data flows from source to
HDFS, is processed by MapReduce managed by YARN, and is then used for analysis.
3
Submission Guidelines:
1. Data Acquisition & Preparation (5%)
• Select a suitable public dataset representing urban mobility (e.g., taxi trip records,
traffic sensor data). Platforms like Kaggle or city-specific open data portals are good
sources.
Describe the dataset's structure, size, and key attributes relevant to your problem
statement.
•
2. Hadoop Environment and Data Ingestion (10%)
• Set up a local single-node Hadoop cluster (e.g., using the official Apache Hadoop
binaries or a Docker image).
Document the key steps of your setup process.
Load your chosen dataset into HDFS. Provide the commands and screenshots
showing the data successfully stored in HDFS.
•
•
3. Data Processing with MapReduce (20%)
• Write a MapReduce program in Java or Python to process the data. Your program
must perform data cleaning and feature engineering to prepare it for the predictive
model.
Example tasks: calculate average trip duration per route, count vehicle flow per
hour, or identify other relevant features from the raw data.
Explain the logic of your Mapper and Reducer classes and include the well-
commented source code in your report's appendix.
•
•
4. Predictive Analysis and Visualization (15%)
•
•
Export the processed data from HDFS.
Use the processed data to train a simple predictive model. You can use a library like
Scikit-learn in Python to build a classification or regression model that addresses
your problem statement.
Analyze and interpret the output of your MapReduce job and your predictive model.
Create meaningful visualizations (e.g., graphs showing congestion by time of day, a
confusion matrix for your model) to present your findings.
•
•
Phase 3: Reflection and Documentation (LO 1, LO 2, LO 3) – 30% of Total Grade
1. Critical Reflection (10%)
•
•
Reflect on the key challenges you encountered during implementation (e.g., data
cleaning, debugging MapReduce, model accuracy) and how you addressed them.
Critically discuss the performance and scalability of your MapReduce solution.
Could it be optimized (e.g., by using a Combiner)?
2. Final Report Documentation (20%)
•
•
•
Compile a detailed, professional report of no more than 3000 words documenting
the entire project.
The report must be well-structured with clear headings, proper grammar, and
academic language.
Ensure all phases (Conceptual Design, Implementation, Reflection) are thoroughly
covered, including diagrams, code snippets, commands, and visualizations to
support your work.
Include a bibliography using the Harvard referencing style. •
4
•
•
•
•
•
•
•
•
Document Format: Submit your assignment as a single document following theBSBI
assignment template provided in Canvas.
Writing Quality: Ensure clear and concise writing with proper grammar and spelling.
Use headings and subheadings to organize your work logically according to the tasks
outlined above.
Visuals: Include visuals like diagrams (process flow, conceptual model sketches), tables
(data assumptions, results), and graphs (simulation output) where appropriate to
enhance understanding.
Task Coverage: Address each part thoroughly, demonstrating your understanding of Big
Data concepts and their application to the business scenario.
Implementation Details: Provide relevant examples and details of your model
implementation, including code snippets, commands, and calculations.
Referencing Style: Use Harvard referencing style for your bibliography.
Discussion: Discuss your findings, insights, and the implications of your
recommendations. Reflect on the challenges faced and how you overcame them.
Submission: Submit your assignment electronically (Canvas) by the specified deadline.
5
GUIDANCE ON ASSESSMENT
Allmaterials must be properly referenced under Harvard conventions. The length required is 3000 words with tasks equally weighted. The writing style should be formal academic /
report writing style with in-text referencing to support your comments and observations. Originality, quality of argument and good structure are required. The report should demonstrate
sound understanding and ability to apply knowledge and theory of Simulation Techniques. Additional marks being awarded for juxtaposition and insight of issues.
Grading Criteria
Generic Criteria
Knowledge of contexts,
concepts, technologies and
processes
The extent to which:
relevant contextual or theoretical
issues are identified, defined and
described
historical or contemporary
practices are identified, defined
and described
appropriate technologies,
methods and processes are
identified defined and described
90 – 100 80 – 89 70 – 79 60 – 69 50 – 59 40 – 49 30 – 39 0-29
Le
ve
l 6
Understanding through
application of knowledge
The degree to which research
methods are demonstrated:
relevant knowledge and
information is compared,
contrasted, manipulated,
translated and interpreted
knowledge and information is
selected, analysed, synthesized
and evaluated in order to
generate creative ideas, practices,
solutions, arguments or
hypotheses
Exceptional ability to
produce a range of
creative practices and to
critically evaluate them in
a wider context,
generating sustainable
arguments and highly
effective and individual
results
Exceptional application of
a range of research
methodologies to projects
and problems and
hypotheses, with evidence
of highly focused
independent thought and
some new insights into
the subject
Exceptional breadth and
depth of knowledge of
contextual and theoretical
issues, some of which are
at the forefront of the
discipline, and their
relationship to a range of
historical and
contemporary practices
Exceptional knowledge of
a range of relevant
specialist techniques and
processes
Systematic and thorough
application of a range of
research methodologies
to projects and problems
and hypotheses, with
evidence of highly focused
independent thought and
some new insights into
the subject
Outstanding ability to
produce a range of
creative practices and to
critically evaluate them in
a wider context ,
generating sustainable
arguments and highly
effective and original
results
Outstanding breadth and
depth of knowledge of
contextual and theoretical
issues, some of which are
at the forefront of the
discipline, and their
relationship to a range of
historical and
contemporary practices
Extensive knowledge of a
range of relevant
specialist techniques and
processes
Rigorous application of a
range of research
methodologies to projects
, problems and
hypotheses with evidence
of highly focused
independent thought and
critical analysis
Strong ability to produce a
range of creative practices
and to critically evaluate
them in a wider context,
generating sustainable
arguments and highly
effective results
A breadth and depth of
knowledge of contextual
and theoretical issues,
some of which are at the
forefront of the discipline,
and their relationship to a
range of historical and
contemporary practices
Significant knowledge of a
range of relevant
specialist techniques and
processes
Confident ability to apply
a range of research
methodologies to
projects, problems and
hypotheses with clear
evidence of independent
thought and critical
analysis
Confident knowledge of a
range of relevant
specialist techniques and
processes
Strong ability to produce a
range of creative practices
and to evaluate them in a
wider context , generating
effective results
Confident knowledge of a
range of contextual and
theoretical issues, some of
which are at the forefront
of the discipline, and their
relationship to a range of
historical and
contemporary practices
Sound knowledge of a
range of relevant
specialist techniques
and processes
Sound ability to apply a
range of research
methodologies to
projects, problems and
hypotheses and to
demonstrate
independent thought
and critical analysis
Familiar with a range of
contextual and
theoretical issues, at
least some of which are
at the forefront of the
discipline, and their
relationship to a range
of historical and
contemporary practices
Sound ability to produce
arange of creative
practices and to
evaluate them in a wider
context, generating
effective results
Competent ability to
produce a range of
creative practices and
evaluate them in a
wider context to
generate effective
results
Familiar with a range
of contextual and
theoretical issues and
their relationship to a
range of historical and
contemporary
practices
Adequate knowledge
of a range of relevant
specialist techniques
and processes
Competent ability to
apply a range of
research
methodologies to
projects, problems and
hypotheses with some
element of
independent thought
and critical analysis
Limited knowledge of a range
ofrelevant specialist
techniques and processes
Ability to apply a limited range
of research methodologies to
projects, problems and
hypotheses with little
evidence of independent
thought or critical analysis
Limited ability to produce a
range of creative practices and
to evaluate them in a wider
context to generate effective
results
Some knowledge of a range of
contextual and theoretical
issues and their relationship to
arange of historical and
contemporary practices
Limited knowledge
of contextual and
theoretical issues
and their
relationship to a
range of historical
and contemporary
practices
No significant
knowledge of a range
of relevant specialist
techniques or
processes
No significant ability
to apply research
methodologies to
projects, problems
and hypotheses, and
no evidence of
independent
thought or critical
analysis
No significant ability
to produce a range
of creative practices
or to evaluate them
in a wider context to
generate effective
results
6
Application of technical and
professional skills
The degree to which:
appropriate materials and media
are selected, tested and utilised
to realise and present ideas and
solutions
appropriate technologies,
methods and processes are
demonstrated
transferable, professional skills
are effectively demonstrated
self management and
independent learning are
demonstrated Exceptional ability to
manage own learning in a
sustained manner and to
critically evaluate own
progress, making use of a
wide range of feedback
sources
Exceptional,individual
andfluentapplication ofa
range of specialist
practical and technical
skills
Outstanding
accomplishment of a
range of advanced
transferable and
professional skills applied
to complex situations and
problems
Outstanding ability to
manage own learning in a
sustained manner and to
critically evaluate own
progress, making use of a
wide range of feedback
sources
Accomplished,original
andfluentapplication ofa
range of specialist
practical and technical
skills
Outstanding
accomplishment of a
range of advanced
transferable and
professional skills applied
to complex situations and
problems
Accomplished application
of advanced transferable
and professional skills to
complex situations and
problems
Very high ability to
manage own learning in a
sustained manner and
critically evaluate own
progress making effective
use of feedback
Accomplished and original
applicationof a range of
specialist practical and
technical skills
Strong ability to manage
own learning in a
sustained manner and to
critically evaluate own
progress making effective
use of feedback
Confident application of
advanced transferable
and professional skills to
challenging situations and
problems
Confident and imaginative
application of a range of
specialist practical and
technical skills
Sound application of
advanced transferable
and professional skills
Sound application ofa
range of specialist
practical and technical
skills
Sound ability to manage
own learning in a
sustained manner and
critically evaluate own
progress making
effective use of feedback
Competent application
of advanced
transferable
professional skills
Competent ability to
manage own learning
in a sustained manner
and make effective use
of feedback
Competent application
ofa range of specialist
practical and technical
skills
Limited application of
advanced transferable and
professional skills
Basic application of a range of
specialist practical and
technical skills
Basic ability to manage own
learning in a sustained manner
and make use of feedback
Rudimentary
application of a
range of specialist
practical and
technical skills
Ineffective
application of
advanced
transferable and
professional skills
Evidence of a basic
ability to manage
own learning
– DEGREE: BSc Computer Science and Digitisation Module: Big Data Analytics using AI
– Assignment Title:
– SmartCity UrbanMobilityAnalysisusingHadoopand
– Predictive AI
– Student signature:
– Date:
– Learner declaration I certify that the work submitted for this assignment is my own and research sources are fully acknowledged.
– Introduction
– 3000 words
– Phase 1: Conceptual Design & Architecture (LO 1, LO 2) – 20% of Total Grade
– 1. Business Context and Problem Statement (5%)
– 2. Hadoop Ecosystem and Architecture (15%)
– Phase 2: Implementation & Analysis (LO 3) – 50% of Total Grade
– 1. Data Acquisition & Preparation (5%)
– 2. Hadoop Environment and Data Ingestion (10%)
– 3. Data Processing with MapReduce (20%)
– 4. Predictive Analysis and Visualization (15%)
– Phase 3: Reflection and Documentation (LO 1, LO 2, LO 3) – 30% of Total Grade
– 1. Critical Reflection (10%)
– 2. Final Report Documentation (20%)
– Submission Guidelines:
– Document Format: Submit your assignment as a single document following theBSBI assignment template provided in Canvas. Writing Quality: Ensure clear and concise writing with proper grammar and spelling. Use headings and subheadings to organize your work logically according to the tasks outlined above. Visuals: Include visuals like diagrams (process flow, conceptual model sketches), tables (data assumptions, results), and graphs (simulation output) where appropriate to enhance understanding. Task Coverage: Address each part thoroughly, demonstrating your understanding of Big Data concepts and their application to the business scenario. Implementation Details: Provide relevant examples and details of your model implementation, including code snippets, commands, and calculations. Referencing Style: Use Harvard referencing style for your bibliography. Discussion: Discuss your findings, insights, and the implications of your recommendations. Reflect on the challenges faced and how you overcame them. Submission: Submit your assignment electronically (Canvas) by the specified deadline.
– GUIDANCE ON ASSESSMENT
– Allmaterials must be properly referenced under Harvard conventions. The length required is 3000 words with tasks equally weighted. The writing style should be formal academic /
– report writing style with in-text referencing to support your comments and observations. Originality, quality of argument and good structure are required. The report should demonstrate sound understanding and ability to apply knowledge and theory of Simulation Techniques. Additional marks being awarded for juxtaposition and insight of issues. Grading Criteria
– Level 6
– Understanding through
– 90 – 100
– 80 – 89
– 70 – 79
– 60 – 69
– 50 – 59
– 40 – 49
– 30 – 39
– 0-29
– Application of technical and professional skills The degree to which: appropriate materials and media are selected, tested and utilised to realise and present ideas and solutions appropriate technologies, methods and processes are demonstrated transferable, professional skills are effectively demonstrated self management and independent learning are demonstrated
– Exceptional,individual andfluentapplication ofa range of specialist practical and technical skills
– Outstanding accomplishment of a range of advanced transferable and professional skills applied to complex situations and problems
– Exceptional ability to manage own learning in a sustained manner and to critically evaluate own progress, making use of a wide range of feedback sources
– Accomplished,original andfluentapplication ofa range of specialist practical and technical skills
– Outstanding accomplishment of a range of advanced transferable and professional skills applied to complex situations and problems
– Outstanding ability to manage own learning in a sustained manner and to critically evaluate own progress, making use of a wide range of feedback sources
– Accomplished and original applicationof a range of specialist practical and technical skills
– Accomplished application of advanced transferable and professional skills to complex situations and problems
– Very high ability to manage own learning in a sustained manner and critically evaluate own progress making effective use of feedback
– Confident and imaginative application of a range of specialist practical and technical skills
– Confident application of advanced transferable and professional skills to challenging situations and problems
– Strong ability to manage own learning in a sustained manner and to critically evaluate own progress making effective use of feedback
– Sound application ofa range of specialist practical and technical skills
– Sound application of advanced transferable and professional skills
– Sound ability to manage own learning in a sustained manner and critically evaluate own progress making effective use of feedback
– Competent application ofa range of specialist practical and technical skills
– Competent application of advanced transferable professional skills
– Competent ability to manage own learning in a sustained manner and make effective use of feedback
– Basic application of a range of specialist practical and technical skills
– Limited application of advanced transferable and professional skills
– Basic ability to manage own learning in a sustained manner and make use of feedback
– Rudimentary application of a range of specialist practical and technical skills
– Ineffective application of advanced transferable and professional skills
– Evidence of a basic ability to manage own learning
**## ReportBigDataAnalyticsUsingAIFeb23.pdf.pdf**
1
Learner declaration
I certify that the work submitted for this assignment is my own and research
sources are fully acknowledged.
DEGREE: BSc Computer Science and Digitisation
Module: Big Data Analytics using AI
\_\_\_________________________________________________________
Assignment Title:
Assignment Type: Report
Word Limit: 3000 words (+/- 300)
Weighting: 100%
Issue Date: 4/9/2025
Submission Date: 30/9/2025
Feedback Date: 21/10/2025
Plagiarism:
When submitting work for assessment, students should be aware of the
InterActive/Canvas guidance and regulations in concerning plagiarism. All submissions
should be your own, original work. Please note that you must not submit the same
assignment for two different modules within your course.
You must submit an electronic copy of your work. Your submission will be
electronically checked.
Harvard Referencing:
The Harvard Referencing System must be used. The Wikipedia, UKEssays.com or
similar websites must not be used or referenced in your work.
Student signature: Date:
SmartCity UrbanMobilityAnalysisusingHadoopand
Predictive AI
Introduction
2
Phase 2: Implementation & Analysis (LO 3) – 50% of Total Grade
LO1. Demonstrate the understanding of basic concepts of Big Data, its importance and need
in business context.
LO2. Explain the various components of Hadoop and HFDS along with their role in the Big Data
ecosystem.
LO3. Summarize the learning on Big Data analytics using Yarn, HDFS and MapReduce.
Assessment Criteria: Weighting 100%
3000 words
Task Description: You are a Data Engineer tasked with designing and implementing a proof- of-
concept Big Data analytics solution for a city's transport authority.
Scenario: The city council wants to analyse urban mobility patterns using data from road
sensors, taxi trips, or public transit records. The objective is to identify congestion
hotspots, understand their causes, and predict future traffic patterns to enable proactive
traffic management and better infrastructure planning.
Phase 1: Conceptual Design & Architecture (LO 1, LO 2) – 20% of Total Grade
The goal of this assignment is to provide you with hands-on experience in designing and
implementing a Big Data analytics solution that incorporates a predictive AI component.
You will address a hypothetical smart city challenge by using the Hadoop ecosystem to
process large-scale data and derive actionable insights for urban planning. This
assignment requires you to design a solution using Hadoop, HDFS, YARN, and
MapReduce to analyse transportation data. The final step involves using the processed
data to train a simple predictive model, thereby connecting Big Data processing with AI
applications. This will help you understand the end-to-end pipeline from raw data to
business intelligence in a modern context.
Learning Outcomes:
1. Business Context and Problem Statement (5%)
•
•
Describe the smart city scenario, focusing on the challenges of urban mobility.
Define a clear problem statement. For example: "To analyse historical traffic data to
predict the likelihood of traffic congestion at key intersections based on time of day
and day of the week".
Explain how solving this problem provides tangible value to the city (e.g., reduced
commute times, lower pollution, improved public safety).
•
2. Hadoop Ecosystem and Architecture (15%)
•
•
•
•
Explain why a Big Data approach is necessary for this scenario.
Identify the roles of HDFS, YARN, and MapReduce in your proposed solution.
Justify your choice of these components for the defined problem.
Create a clear architectural diagram illustrating how data flows from source to
HDFS, is processed by MapReduce managed by YARN, and is then used for analysis.
3
Submission Guidelines:
1. Data Acquisition & Preparation (5%)
• Select a suitable public dataset representing urban mobility (e.g., taxi trip records,
traffic sensor data). Platforms like Kaggle or city-specific open data portals are good
sources.
Describe the dataset's structure, size, and key attributes relevant to your problem
statement.
•
2. Hadoop Environment and Data Ingestion (10%)
• Set up a local single-node Hadoop cluster (e.g., using the official Apache Hadoop
binaries or a Docker image).
Document the key steps of your setup process.
Load your chosen dataset into HDFS. Provide the commands and screenshots
showing the data successfully stored in HDFS.
•
•
3. Data Processing with MapReduce (20%)
• Write a MapReduce program in Java or Python to process the data. Your program
must perform data cleaning and feature engineering to prepare it for the predictive
model.
Example tasks: calculate average trip duration per route, count vehicle flow per
hour, or identify other relevant features from the raw data.
Explain the logic of your Mapper and Reducer classes and include the well-
commented source code in your report's appendix.
•
•
4. Predictive Analysis and Visualization (15%)
•
•
Export the processed data from HDFS.
Use the processed data to train a simple predictive model. You can use a library like
Scikit-learn in Python to build a classification or regression model that addresses
your problem statement.
Analyze and interpret the output of your MapReduce job and your predictive model.
Create meaningful visualizations (e.g., graphs showing congestion by time of day, a
confusion matrix for your model) to present your findings.
•
•
Phase 3: Reflection and Documentation (LO 1, LO 2, LO 3) – 30% of Total Grade
1. Critical Reflection (10%)
•
•
Reflect on the key challenges you encountered during implementation (e.g., data
cleaning, debugging MapReduce, model accuracy) and how you addressed them.
Critically discuss the performance and scalability of your MapReduce solution.
Could it be optimized (e.g., by using a Combiner)?
2. Final Report Documentation (20%)
•
•
•
Compile a detailed, professional report of no more than 3000 words documenting
the entire project.
The report must be well-structured with clear headings, proper grammar, and
academic language.
Ensure all phases (Conceptual Design, Implementation, Reflection) are thoroughly
covered, including diagrams, code snippets, commands, and visualizations to
support your work.
Include a bibliography using the Harvard referencing style. •
4
•
•
•
•
•
•
•
•
Document Format: Submit your assignment as a single document following theBSBI
assignment template provided in Canvas.
Writing Quality: Ensure clear and concise writing with proper grammar and spelling.
Use headings and subheadings to organize your work logically according to the tasks
outlined above.
Visuals: Include visuals like diagrams (process flow, conceptual model sketches), tables
(data assumptions, results), and graphs (simulation output) where appropriate to
enhance understanding.
Task Coverage: Address each part thoroughly, demonstrating your understanding of Big
Data concepts and their application to the business scenario.
Implementation Details: Provide relevant examples and details of your model
implementation, including code snippets, commands, and calculations.
Referencing Style: Use Harvard referencing style for your bibliography.
Discussion: Discuss your findings, insights, and the implications of your
recommendations. Reflect on the challenges faced and how you overcame them.
Submission: Submit your assignment electronically (Canvas) by the specified deadline.
5
GUIDANCE ON ASSESSMENT
Allmaterials must be properly referenced under Harvard conventions. The length required is 3000 words with tasks equally weighted. The writing style should be formal academic /
report writing style with in-text referencing to support your comments and observations. Originality, quality of argument and good structure are required. The report should demonstrate
sound understanding and ability to apply knowledge and theory of Simulation Techniques. Additional marks being awarded for juxtaposition and insight of issues.
Grading Criteria
Generic Criteria
Knowledge of contexts,
concepts, technologies and
processes
The extent to which:
relevant contextual or theoretical
issues are identified, defined and
described
historical or contemporary
practices are identified, defined
and described
appropriate technologies,
methods and processes are
identified defined and described
90 – 100 80 – 89 70 – 79 60 – 69 50 – 59 40 – 49 30 – 39 0-29
Le
ve
l 6
Understanding through
application of knowledge
The degree to which research
methods are demonstrated:
relevant knowledge and
information is compared,
contrasted, manipulated,
translated and interpreted
knowledge and information is
selected, analysed, synthesized
and evaluated in order to
generate creative ideas, practices,
solutions, arguments or
hypotheses
Exceptional ability to
produce a range of
creative practices and to
critically evaluate them in
a wider context,
generating sustainable
arguments and highly
effective and individual
results
Exceptional application of
a range of research
methodologies to projects
and problems and
hypotheses, with evidence
of highly focused
independent thought and
some new insights into
the subject
Exceptional breadth and
depth of knowledge of
contextual and theoretical
issues, some of which are
at the forefront of the
discipline, and their
relationship to a range of
historical and
contemporary practices
Exceptional knowledge of
a range of relevant
specialist techniques and
processes
Systematic and thorough
application of a range of
research methodologies
to projects and problems
and hypotheses, with
evidence of highly focused
independent thought and
some new insights into
the subject
Outstanding ability to
produce a range of
creative practices and to
critically evaluate them in
a wider context ,
generating sustainable
arguments and highly
effective and original
results
Outstanding breadth and
depth of knowledge of
contextual and theoretical
issues, some of which are
at the forefront of the
discipline, and their
relationship to a range of
historical and
contemporary practices
Extensive knowledge of a
range of relevant
specialist techniques and
processes
Rigorous application of a
range of research
methodologies to projects
, problems and
hypotheses with evidence
of highly focused
independent thought and
critical analysis
Strong ability to produce a
range of creative practices
and to critically evaluate
them in a wider context,
generating sustainable
arguments and highly
effective results
A breadth and depth of
knowledge of contextual
and theoretical issues,
some of which are at the
forefront of the discipline,
and their relationship to a
range of historical and
contemporary practices
Significant knowledge of a
range of relevant
specialist techniques and
processes
Confident ability to apply
a range of research
methodologies to
projects, problems and
hypotheses with clear
evidence of independent
thought and critical
analysis
Confident knowledge of a
range of relevant
specialist techniques and
processes
Strong ability to produce a
range of creative practices
and to evaluate them in a
wider context , generating
effective results
Confident knowledge of a
range of contextual and
theoretical issues, some of
which are at the forefront
of the discipline, and their
relationship to a range of
historical and
contemporary practices
Sound knowledge of a
range of relevant
specialist techniques
and processes
Sound ability to apply a
range of research
methodologies to
projects, problems and
hypotheses and to
demonstrate
independent thought
and critical analysis
Familiar with a range of
contextual and
theoretical issues, at
least some of which are
at the forefront of the
discipline, and their
relationship to a range
of historical and
contemporary practices
Sound ability to produce
arange of creative
practices and to
evaluate them in a wider
context, generating
effective results
Competent ability to
produce a range of
creative practices and
evaluate them in a
wider context to
generate effective
results
Familiar with a range
of contextual and
theoretical issues and
their relationship to a
range of historical and
contemporary
practices
Adequate knowledge
of a range of relevant
specialist techniques
and processes
Competent ability to
apply a range of
research
methodologies to
projects, problems and
hypotheses with some
element of
independent thought
and critical analysis
Limited knowledge of a range
ofrelevant specialist
techniques and processes
Ability to apply a limited range
of research methodologies to
projects, problems and
hypotheses with little
evidence of independent
thought or critical analysis
Limited ability to produce a
range of creative practices and
to evaluate them in a wider
context to generate effective
results
Some knowledge of a range of
contextual and theoretical
issues and their relationship to
arange of historical and
contemporary practices
Limited knowledge
of contextual and
theoretical issues
and their
relationship to a
range of historical
and contemporary
practices
No significant
knowledge of a range
of relevant specialist
techniques or
processes
No significant ability
to apply research
methodologies to
projects, problems
and hypotheses, and
no evidence of
independent
thought or critical
analysis
No significant ability
to produce a range
of creative practices
or to evaluate them
in a wider context to
generate effective
results
6
Application of technical and
professional skills
The degree to which:
appropriate materials and media
are selected, tested and utilised
to realise and present ideas and
solutions
appropriate technologies,
methods and processes are
demonstrated
transferable, professional skills
are effectively demonstrated
self management and
independent learning are
demonstrated Exceptional ability to
manage own learning in a
sustained manner and to
critically evaluate own
progress, making use of a
wide range of feedback
sources
Exceptional,individual
andfluentapplication ofa
range of specialist
practical and technical
skills
Outstanding
accomplishment of a
range of advanced
transferable and
professional skills applied
to complex situations and
problems
Outstanding ability to
manage own learning in a
sustained manner and to
critically evaluate own
progress, making use of a
wide range of feedback
sources
Accomplished,original
andfluentapplication ofa
range of specialist
practical and technical
skills
Outstanding
accomplishment of a
range of advanced
transferable and
professional skills applied
to complex situations and
problems
Accomplished application
of advanced transferable
and professional skills to
complex situations and
problems
Very high ability to
manage own learning in a
sustained manner and
critically evaluate own
progress making effective
use of feedback
Accomplished and original
applicationof a range of
specialist practical and
technical skills
Strong ability to manage
own learning in a
sustained manner and to
critically evaluate own
progress making effective
use of feedback
Confident application of
advanced transferable
and professional skills to
challenging situations and
problems
Confident and imaginative
application of a range of
specialist practical and
technical skills
Sound application of
advanced transferable
and professional skills
Sound application ofa
range of specialist
practical and technical
skills
Sound ability to manage
own learning in a
sustained manner and
critically evaluate own
progress making
effective use of feedback
Competent application
of advanced
transferable
professional skills
Competent ability to
manage own learning
in a sustained manner
and make effective use
of feedback
Competent application
ofa range of specialist
practical and technical
skills
Limited application of
advanced transferable and
professional skills
Basic application of a range of
specialist practical and
technical skills
Basic ability to manage own
learning in a sustained manner
and make use of feedback
Rudimentary
application of a
range of specialist
practical and
technical skills
Ineffective
application of
advanced
transferable and
professional skills
Evidence of a basic
ability to manage
own learning
– DEGREE: BSc Computer Science and Digitisation Module: Big Data Analytics using AI
– Assignment Title:
– SmartCity UrbanMobilityAnalysisusingHadoopand
– Predictive AI
– Student signature:
– Date:
– Learner declaration I certify that the work submitted for this assignment is my own and research sources are fully acknowledged.
– Introduction
– 3000 words
– Phase 1: Conceptual Design & Architecture (LO 1, LO 2) – 20% of Total Grade
– 1. Business Context and Problem Statement (5%)
– 2. Hadoop Ecosystem and Architecture (15%)
– Phase 2: Implementation & Analysis (LO 3) – 50% of Total Grade
– 1. Data Acquisition & Preparation (5%)
– 2. Hadoop Environment and Data Ingestion (10%)
– 3. Data Processing with MapReduce (20%)
– 4. Predictive Analysis and Visualization (15%)
– Phase 3: Reflection and Documentation (LO 1, LO 2, LO 3) – 30% of Total Grade
– 1. Critical Reflection (10%)
– 2. Final Report Documentation (20%)
– Submission Guidelines:
– Document Format: Submit your assignment as a single document following theBSBI assignment template provided in Canvas. Writing Quality: Ensure clear and concise writing with proper grammar and spelling. Use headings and subheadings to organize your work logically according to the tasks outlined above. Visuals: Include visuals like diagrams (process flow, conceptual model sketches), tables (data assumptions, results), and graphs (simulation output) where appropriate to enhance understanding. Task Coverage: Address each part thoroughly, demonstrating your understanding of Big Data concepts and their application to the business scenario. Implementation Details: Provide relevant examples and details of your model implementation, including code snippets, commands, and calculations. Referencing Style: Use Harvard referencing style for your bibliography. Discussion: Discuss your findings, insights, and the implications of your recommendations. Reflect on the challenges faced and how you overcame them. Submission: Submit your assignment electronically (Canvas) by the specified deadline.
– GUIDANCE ON ASSESSMENT
– Allmaterials must be properly referenced under Harvard conventions. The length required is 3000 words with tasks equally weighted. The writing style should be formal academic /
– report writing style with in-text referencing to support your comments and observations. Originality, quality of argument and good structure are required. The report should demonstrate sound understanding and ability to apply knowledge and theory of Simulation Techniques. Additional marks being awarded for juxtaposition and insight of issues. Grading Criteria
– Level 6
– Understanding through
– 90 – 100
– 80 – 89
– 70 – 79
– 60 – 69
– 50 – 59
– 40 – 49
– 30 – 39
– 0-29
– Application of technical and professional skills The degree to which: appropriate materials and media are selected, tested and utilised to realise and present ideas and solutions appropriate technologies, methods and processes are demonstrated transferable, professional skills are effectively demonstrated self management and independent learning are demonstrated
– Exceptional,individual andfluentapplication ofa range of specialist practical and technical skills
– Outstanding accomplishment of a range of advanced transferable and professional skills applied to complex situations and problems
– Exceptional ability to manage own learning in a sustained manner and to critically evaluate own progress, making use of a wide range of feedback sources
– Accomplished,original andfluentapplication ofa range of specialist practical and technical skills
– Outstanding accomplishment of a range of advanced transferable and professional skills applied to complex situations and problems
– Outstanding ability to manage own learning in a sustained manner and to critically evaluate own progress, making use of a wide range of feedback sources
– Accomplished and original applicationof a range of specialist practical and technical skills
– Accomplished application of advanced transferable and professional skills to complex situations and problems
– Very high ability to manage own learning in a sustained manner and critically evaluate own progress making effective use of feedback
– Confident and imaginative application of a range of specialist practical and technical skills
– Confident application of advanced transferable and professional skills to challenging situations and problems
– Strong ability to manage own learning in a sustained manner and to critically evaluate own progress making effective use of feedback
– Sound application ofa range of specialist practical and technical skills
– Sound application of advanced transferable and professional skills
– Sound ability to manage own learning in a sustained manner and critically evaluate own progress making effective use of feedback
– Competent application ofa range of specialist practical and technical skills
– Competent application of advanced transferable professional skills
– Competent ability to manage own learning in a sustained manner and make effective use of feedback
– Basic application of a range of specialist practical and technical skills
– Limited application of advanced transferable and professional skills
– Basic ability to manage own learning in a sustained manner and make use of feedback
– Rudimentary application of a range of specialist practical and technical skills
– Ineffective application of advanced transferable and professional skills
– Evidence of a basic ability to manage own learning