数据分析作业代写 MANM301代写

Data Analytics

MANM301

Individual Assignment: Report and Video Presentation

数据分析作业代写 Description: You are a strategic consultant to a well-known company which has a diversified business with a presence in different industries

100% of the module mark

Task ‘A’ – Caravan Insurance Report [mark 70%]

Deadline: Wednesday 9^th of December 2020 by 4:00 pm

Individual report 3000 words

Task ‘B’ – Bus Services Video Presentation [mark 30%]

Deadline: Monday 18^th January 2021 by 4:00 pm

Video length less than <= 20 minutes

Please see University guidelines for late submission.

Module learning outcomes assessed 数据分析作业代写

In completing this assessment, you should demonstrate:

The efficiency and productivity of business firms by using data analytics techniques
The application of state-of-the-art techniques to build analytical and predictive models
The use of data analytics and a methodical approach to analyse data
To communicate and provide results to the management and stake-holders for decision making and implementation

Task ‘A’ – Caravan Insurance Report

Description: You are a strategic consultant to a well-known company which has a diversified business with a presence in different industries and geographical markets. You are asked to analyse the Caravan customer dataset of an insurance company. The Dataset was supplied by a Dutch Data Mining Company Sentinel Machine research. Problem is based on a real-world business problem. The feature of interest is whether a customer buys a caravan insurance.

Information about customers consists of 86 variables:

43 socio-demographic variables derived via the customer’s ZIP area code,
43 variables about ownership of other insurance policies.

Data Size:

9822 records: 5822 training records and 4000 test records
86 attributes

Challenges: The data analytical task is to predict whether a customer is interested in a caravan insurance policy from the data. You should first do some exploratory data analysis. Visualising the data should give you some insight into certain particularities of this dataset. Then prepare the data for data mining. It will be important to select the right features, and to construct new features from existing ones. Try out at least three different data mining algorithms in SPSS Modeller and R.

Second challenge is to derive information about the profile of a typical caravan insurance buyer to give a clear insight to why customers have a caravan policy and how these customers are different from other customers.

The Data and associated data dictionary can be downloaded from the SurreyLearn.

Feature selection and extraction will be very important. You need to find out if data is noisy or unbalanced.数据分析作业代写

Ensure that the analysis, the critical arguments and conclusions contained in the report are supported by the evidence. The descriptions and interpretation must be comprehensible, useful and actionable for a marketing professional with no prior knowledge of technical jargon. The value of a description is inherently subjective.

Guidance and assessment of report

Assessment format:

The assignment should be a MAXIMUM of no more than 3,000 words (excluding the table of contents, tables, figures, references, and appendix).

Use the Times New Roman font, 12 font size, 2 line spacing, margins of at least one-inch and upper right page numbers.
Tables and figures could be included in the text.

How to write a report?

The report should at least include the following:

Title Page:Attach the cover sheet.
Introduction:Briefly introduce the topic; include the parameters of the assessment, i.e. what you will include and why, what you will not include, and why not.数据分析作业代写
Development:Develop your assessment, with the most important points first, least important last. Use heading and paragraphing to show new topics/ideas, etc.
Conclusion:Summarise the content of your assessment; proposing solutions where possible; do not introduce new points / ideas at this stage.
References:This must include all references in your research paper.
Appendix:This must include your syntax, code, screenshots, etc.

Task ‘B’ – Bus Service Video-Presentation 数据分析作业代写

Description: In June 2015, a Knowledge Transfer Partnership between Surrey County Council and the University of Surrey begun to increase patronage of bus services in the county.

The United Kingdom consists out of former sovereign states England, Wales, Scotland and Ireland. England is divided into 48 counties having an estimated population of about 56.0 million in 2020. In the south-east of England is the county Surrey with an area of 1,663 km² with a population of 1,189,934 (estimate in 2018, in our database 1.16M ≈ 1,161,256). This population can be found in about 29.1k postcode areas. This increases to 50.4k when a buffer is included, and all delivery points are considered. There are 500.6k delivery points (out of which are 481.4k domestic ones within the 29.1k postcode geometries).数据分析作业代写

Including the buffer there are 800.8k delivery points (out of which are 760k domestic ones with 43.9k geometries). Delivery points, addresses and other general geographical data can be obtained from Royal Mail’s Postcode Address File (PAF) and Ordnance Survey’s portal. Please see BusAnalytics.uk for more details. The population used in the database is 1.16M and 1.86M including the buffer. The number of domestic delivery points which will be called households is 481.4k, i.e. there are about 2.41 people per household on average.

Our bus service information shows 37 bus operators (status 2018) in the county of Surrey.

Several bus operators in Surrey participated in the subsequent case study. However, due to non-disclosure agreements (NDA) details about them cannot be mentioned. There are 270 routes with distinct bus service numbers operating in Surrey. Sometimes a bus service number is assigned to several slightly differing routes depending on the time or whether there were route deviations. In this study the most frequent (usually the longest) hat to be used.

Furthermore, a subset of 87 routes (i.e. 32.2% of all routes) with distinct service number were analysed. Again, due to the NDA these routes will not be identified. That means displayed routes to illustrate concepts are not necessarily related to data provided by bus operators. Information about bus stops and train stations can be obtained from the UK’s government page by accessing the National public transport access node (NaPTAN) data (see BusAnalytics.uk).数据分析作业代写

Figure 1 gives a geographic overview of the area of interest. The underlying heatmap was derived for the domestic households in Surrey (excluding buffer zone) using Kernel Density Estimators (KDE).

The tables to plot the figure above are:

Bus routes: bus_route;
Bus stops: bus_stop;
Train stations: train_station;
Hospitals: hospital;
Delivery points: household(business, household (=domestic properties) and others);
household: household_domestic_surrey(used for heatmap);
Town and village names: town;
Border of Surrey: surrey_boundary.

Essential passenger and model data are in aggregated form in two tables:

Route information over entire time period: routes_aggr;
Daily route information: routes_daily.

A database backup and related information can be found on SurreyLearn.

Challenges: The aim is to predict passengers for new and existing routes.

Review related literature; SQL queries include aggregation functions (e.g. count, min, max, …), visualisations created by using Power BI and R (e.g. density/histogram plots, correlation plots, PBI should include header, filters and interactive graphs and calls to R code visualisation, methods briefly explained, feature creation/selection/map, three accuracy measures for several machine learning methods (compared in tabular form), R-code (appropriate variable types chosen, control statements, loops and functions, usage of dplyr, ggplot, etc.).数据分析作业代写

Ensure that the slides in the presentation are supported by the evidence. The descriptions and interpretation must be comprehensible, useful and actionable for a marketing professional.

Students must use a database system, Power Business Intelligence and R.

Presentation Format: individual presentation

The completed presentation with recorded narrations will take the following form:

Title – 1 slide
Executive summary – 1 slide
Literature review ≤ 3 slides
Data explanation ≤ 3 slides
Visualisation ≤ 3 slides
Prediction/classification methods ≤ 3 slides
SQL queries, R-code (appendix – no narrations required) ≤ 6 slides

Presentation limits: ≤ 20 slides (including appendices, tables and references) and ≤ 20 minutes.数据分析作业代写

Submission type: electronic copy (via SurreyLearn website).

Guidance: Please consult lecture material and computer laboratory sessions.

Marking Scheme: 数据分析作业代写

Marks will be awarded on the basis of the following marking grid.

Please note that despite the detail this list provides, the final mark relies on the experience and judgement of the marker and the moderator, and marks are not negotiable. If you require further discussion about your assignment for future work please contact the teaching staff.

Marking Criteria

Marking Grid

	Report	Basis of Marking	Maximum
Task A	Data Visualisation	See marking grid below.	10
	Feature selection and construction		10
	Prediction of who would be interested in buying a caravan insurance	数据分析作业代写	25
	Profile of a caravan insurance buyer		15
	Using SPSS and R for data modelling		10
Task B	Video presentation	Executive Summary and Literature Review; Database and SQL;Visualisation and Analytics (Business Intelligence); Methods	30
	Overall		100

Task A – Data Visualisation (10 marks)

	Mark range	MARK
Exceptional, comprehensive, deep and advanced, meaningful and information-rich visualisation. The work is highly critical and analytical and draws robust recommendations/conclusions from the data.	(10)
Excellent comprehensive, advanced and information-rich visulisation. Recommendations/Conclusions drawn from the data are supported and the concept is critical and analytical.	(8-9)
Very good use of visualisation techniques for data exploration but lacks development in some areas. Recommendations/Conclusions drawn from the data are supported and the concept is critical and analytical.	(6-7)
Reasonable use of visualisation techniques to explore the data but there is limited discussion of them. Recommendations/Conclusions tend to be rudimentary in nature.	(5)
Basic use of visualisation techniques to explore the data and there is limited discussion of them. Recommendations/Conclusions tend to be rudimentary in nature.	(3-4)数据分析作业代写
Some attempt to use visualisation though lacks the data exploration. Conclusions drawn are unconvincing.	(2)
No real attempt to use the visualisation techniques for data exploration.	(0-1)

Task A – Feature Selection and Construction (15 marks)

	Mark range	MARK
Exceptional robust and accurate solutions that demonstrate an exceptional understanding of the data. Sophisticated and accurate use of features. An excellent data analysis that shows both general understanding and specific observations of the data. All important theoretical concepts and assumptions are well explained. A clear structure and coherent argument throughout, offering full support for all points made. Clear and appropriate referencing, correct spelling and punctuation.	(14-15)
Excellent 数据分析作业代写 solutions cover most of the ground required to demonstrate an excellent understanding of data. Mostly accurate features. An excellent data analysis that shows both general understanding and specific observations of the features. A clear structure and coherent argument throughout, offering reasonable support for points made. Clear and appropriate referencing, correct spelling and punctuation.	(11-13)
Very good solutions cover most of the ground required to demonstrate a reasonable understanding of data. Some minor errors in choosing/creating the features. A good data analysis that shows both general understanding and specific observations of the features. Largely well written throughout and adequate to express ideas. Most points are supported by further arguments or else by appropriate citations or evidences. Few spelling mistakes and/or grammatical errors	(9-10)
Good solutions at least half of the solutions are largely correct and demonstrate some understanding of the main concepts in feature selection. Some significant errors in feature selection/extraction. A reasonable data analysis that shows both general understanding and specific observations of the features. Largely well written throughout and adequate to express ideas, but there is inadequate support for the points made. Some spelling mistakes and/or grammatical errors.	(7-8)
Demonstrates limited understanding of the project requirements. Solutions cover less than half the ground required to demonstrate understanding of feature selection/extraction. Significant gaps in the knowledge are evident. Significant errors in feature selection. Some attempt at a clear structure although the line of argument may not always be clear or well supported. Some spelling mistakes and/or grammatical errors.	(5-6)
Inefficient awareness of the project requirements. Solutions miss most of the ground required to demonstrate understanding of feature selection/construction. Some attempts at a clear structure but the line of argument is unclear or unsupported. Some spelling mistakes and/or grammatical errors.数据分析作业代写	(3-4)
Little or no awareness of the project requirements. Solutions show poor understanding of feature selection/extraction. Little or no attempt to produce a professionally written document. Some spelling mistakes and/or grammatical errors.	(1-2)

Task A – Prediction of who would be interested in buying a caravan insurance (25 marks)

	Mark range	MARK
Exceptional level of knowledge and a deep understanding of the task. Robust and accurate solutions that demonstrate an exceptional level of understanding of data modelling and chosen techniques. The discussion is in-depth and thorough, demonstrating exceptional knowledge and understanding of the prediction task. A clear structure and coherent argument throughout, offering full support for all points made. Clear and appropriate referencing, correct spelling and punctuation.	(23-25)
Excellent critical level of knowledge and understanding of the task. Excellent and accurate solutions that demonstrates an excellent level of knowledge and a good understanding of data modelling and chosen techniques. The analysis of the prediction task is excellent and supported by evidence. A clear structure and coherent argument throughout, offering reasonable support for points made. Clear and appropriate referencing, correct spelling and punctuation.数据分析作业代写	(20-22)
Very good level of knowledge of and understanding of the task. Very good solution that demonstrates a very good level of knowledge and a understanding of data modelling and chosen techniques. The analysis of the prediction task is very good and supported by evidence. Largely well written throughout and adequate to express ideas. Most points are supported by further arguments or else by appropriate citations or evidences. Few spelling mistakes and/or grammatical errors	(17-19)
Good level of knowledge and understanding of the task. The discussion demonstrates partial understanding of data modelling and chosen techniques but with gaps in the knowledge. Techniques is correctly identified but the prediction analysis is either fragmented or superficial. Largely well written throughout and adequate to express ideas, but there is inadequate support for the points made. Some spelling mistakes and/or grammatical errors.	(14-16)
Adequate attempt has been made to predict who will buy a caravan insurance. Knowledge and understanding of the task is very limited. Limited amount of analysis. Some attempt at a clear structure although the line of argument may not always be clear or well supported. Some spelling mistakes and/or grammatical errors.	(11-13)
Demonstrates a minimal ability to model the data. The analysis is superficial and unstructured. There is some evidence of caravan insurance prediction. Some attempts at a clear structure but the line of argument is unclear or unsupported. Some spelling mistakes and/or grammatical errors.数据分析作业代写	(9-10)
Failed to demonstrate a minimal ability to model the data. The analysis is superficial and unstructured. Lacks the evidence of caravan insurance prediction. Some attempts at a clear structure but the line of argument is unclear or unsupported. Some spelling mistakes and/or grammatical errors.	(5-8)
Failed to demonstrate any understanding of the project requirements. Solutions show poor understanding of data modelling and prediction. No attempt to produce a professionally written document. Some spelling mistakes and/or grammatical errors.	(1-4)

Task A – Profile of a caravan insurance buyer (15 Marks)

	Mark range	MARK
Exceptional, robust and accurate solutions that demonstrate an exceptional level of understanding of a caravan insurance customer profile. Sophisticated and accurate functional rules that can be used by a marketing team to target the right customers. The work is exceptionally critical and analytical and draws robust recommendations/conclusions to target a potential caravan customer.	(14-15)
Excellent and accurate solutions that demonstrate an excellent level of understanding of a caravan insurance customer profile. Actionable and accurate functional rules that can be used by a marketing team to target the right customers. Recommendations/Conclusions drawn are supported and the concept is critical and analytical.	(12-13)
Very good solutions which cover most of the ground required to demonstrate a a very good level of understanding of a caravan insurance customer profile. Some minor errors in rules. The work is descriptive although some attempt is made to be analytical.数据分析作业代写	(10-11)
Good to adequate level of knowledge and understanding of a caravan insurance customer profile. Some errors in rules. The work is descriptive although very little attempt is made to be analytical. Conclusions tend to be rudimentary in nature.	(8-9)
At least half of the solutions are largely correct and demonstrate some understanding of a caravan insurance customer profile. Some significant errors in rules. The work is descriptive in the main although some attempt is made to be analytical. Conclusions tend to be rudimentary in nature.	(6-7)
A poor understanding of the project requirements. Solutions miss most of the ground required to demonstrate understanding of a caravan insurance buyer profile.	(3-5)
Little or no understanding of the project requirements. Solutions show poor understanding of a caravan insurance customer profile	(1-2)

Task A – Use of SPSS and ‘R’ (10 marks)

	Mark range	MARK
Exceptional use of ‘R’ and ‘SPSS Modeller’ for data modelling and analytical tasks.	(9-10)
Excellent use of ‘R’ and ‘SPSS Modeller’ for data modelling and analytical tasks	(7-8)
Only one tool was used exceptionally well for data modelling and analytical tasks	(5-6)
Poor use of ‘R’ and ‘SPSS Modeller’ modelling tools.	(3-4)
No use of the modelling tools.数据分析作业代写	(0-2)

Task B – Overview

Excellent critical level of and executive summary and literature review, excellent database and SQL application, excellent visualisations and analytics, excellent application of methods.	(21-30)
Good level of and executive summary and literature review, good database and SQL application, good visualisations and analytics, good application of methods.数据分析作业代写	(18-20)
Adequate level of and executive summary and literature review, adequate database and SQL application, adequate visualisations and analytics, adequate application of data analytics methods.	(15-18)
Insufficient standard for an executive summary and literature review, insufficient knowledge of databases and SQL, insufficient standard for visualisations and analytics, minimal ability to apply data analytics methods.	(0-15)

Please note that despite the detail this list provides, the final mark relies on the experience and judgement of the marker and the moderator, and marks are not negotiable.

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