Get Instant Help From 5000+ Experts For

Writing: Get your essay and assignment written from scratch by PhD expert

Rewriting: Paraphrase or rewrite your friend's essay with similar meaning at reduced cost

Editing:Proofread your work by experts and improve grade at Lowest cost

And Improve Your Grades
Phone no. Missing!

Enter phone no. to receive critical updates and urgent messages !

Attach file

Error goes here

Files Missing!

Please upload all relevant files for quick & complete assistance.

Guaranteed Higher Grade!
Free Quote
Undertaking an analysis of a Real-world Data Set for Quantitative and Modern Data Analysis

Main Objective of the Assessment

Main Objective Of The Assessment

The coursework is based on undertaking an analysis of a real world data set. It is a shared assessment block for  Quantitative Data Analysis and Modern Data.
(Note: We have made some small modifications so please use our provided version from Blackboard learn; do not use any original source version.)

This assessment offers an opportunity to bring together your skills from Quantitative Data Analysis and  Modern Data.
Note that there is also a second shared assessment block which takes the form of a written examination.

Description Of The Assessment:

the Requirements For the Assessment Block are as Follows:
1.You Will Be Provided With:
 A.  A data set and its metadata, (note students will work on a subset of the data but in the same data format)
 B.  Research questions to guide your analysis
 C.  A proforma .rmd file to use to complete your coursework.

2.Using the pro forma please address the following:

A. Organise And Clean The Data
(a). Subset The Data Into The Specific Data Subset Allocated
(b). Data Quality Analysis
(c). Data Cleaning

B.  Exploratory data analysis

C.  Modelling:
(a). Build a model for player potential
(b). Critique your model using relevant diagnostics
(c).  Based on 3.2 suggest improvements to your model

D. Extension work:
(a). Plan and build a model for the variable high wage indicator (high.wage.ind)

3. Generating Your Personal Data sets

A. Each student should use data based a subset from the overall datasetrda which can be downloaded from the blackboard learn page of.

B. The subset of the data that you will work on depends on your student 

C. At the top of the proforma.rmd file provided (can be downloaded from the blackboard learnpage of there are instructions on how to use your student id to obtain the subset of data you need to work 

D. The code for sub setting is embedded in the rmarkdown need to configure it for your student 

E. If you are uncertain, please check!

General Guidance:

1. You Are Expected To Use r And Rmarkdown For Your Analysis.

2. Use The Template Rmarkdown As a Starting Point To Structure Your Report But Remember To Remove Our Scaffolding And Guidance Comments Before You Submit. It Is Available From The Cs5801 Blackboard Learn Page (Link)

3. Update The Yaml To Include Your Name And Other Identifier Information.

4. Include All Relevant r Code Chunks And Provide Explanation, Comments And Discussion As Appropriate.

5. Follow The Principles Of ‘Literate Programming’ So Choose Meaningful Variable And Function Names And Add Comments.

6. You Can Also Submit Supplementary Files If You Wish, But You Must Include a Single Report File That Contains Your Entire Report In .Rmd Format. Make Sure Any Supplementary Files Are Cross- Referenced From Your Main Report.

7. Where Appropriate Cite External Sources And Add a Bibliography At The End Of Your Main Report.

8. The Report Should Be Professionally Presented With Good Structure, An Absence Of Spelling Errors And Other Typos And Written In An Appropriate Style (i.e., Simple To The Point, Unemotive Language).

9. Make Sure You Respect The 4000 Word Limit As We Discourage Excessive Padding, So Unnecessary Words And Waffle Will Militate Against Professional Presentation.

10. Sometimes Even Suitable Models Do Not Have Good Fit Due To The Nature Of The Data. In Such Circumstances You Will Not Be Penalised.

11. Whilst We Encourage Collaboration And Sharing Of Ideas This Is An Individual Report And So Must Be Based On Your Own Understanding, Analysis And Words. Wiseflow Automatically Cross- Compares All Submissions.

12. Wiseflow Also Has a Plagiarism Detector For External Sources. We Encourage You To Use Such Sources Including r Packages, Code, Ideas For Data Analysis And Other Statistical Sources, But You Must Acknowledge The Sources. In Other Words, Do Not Attempt To Pass Off The Work Of Others As Your Own.

13. If You Have Questions, Please Check The Assessment Faqs Or Ask One Of Us. Don’t Guess!

Lo1: Design And Implement Methods And Protocols For Data Preparation And Exploration Using Advanced Statistical Techniques.

Lo2: Apply These Methods On Real Data To Generate Novel Insight, Critically Evaluate Its Value And Design a Framework For Data Management And Sharing.

Below We Give The Detailed Marking Scheme.


Total Marks Available

Marking Criteria (For Full Marks)

0. Understandability Of The Analysis


0.1 Quality Of Report


Clearly And Professionally Presented. Appropriate Use Of Cited External Sources.

0.2 Quality Of Code Including Comments, Clear Layout And Structure, Meaningful Identifiers.


Easy To Understand, Well Documented Code Following Principles Of Literate Programming.

1. Organise And Clean The Data (25)

1.1 Subset The Data Into The Specific Dataset Allocated


Uses r Code To Correctly Select The Subset Of Data Allocated.

1.2 Data Quality Analysis


Provides a Description Of a Comprehensive Plan To Assess The Quality Of The Data, And Documents Its Findings. Includes All Columns/Variables (5), And Full Implementation (5).

1.3 Data Cleaning


Explains Data Quality Issues Found In 1.2, (5) Justifies And Documents The Responses Made (If Any) (5). Nb Even If No Data Quality Issues Are Identified You Should Still Check And Report.

2. Exploratory Data Analysis (20)

2.1 Eda Plan


Outlines a Suitable Plan To Explore The Data.

2.2 Eda And Summary Of Results


Undertakes And Summarises The Findings Of The Data Exploration, Particularly With Respect To The Research Questions. Use Appropriate Summary Statistics (Univariate And Multivariate) And Visualisations.

2.3 Additional Insights And Issues


Highlights Potential Further Issues Or Insights Uncovered In 2.2.

3. Modelling (25)

3.1 Build a Model For Player Potential (Potential)


Given The Research Question (i.e. Target Attribute) Outlines An Analysis Plan That Incorporates/References Any Findings From The Data Cleaning (1.3) And Eda (2.2) (5). Uses r To Build a Suitable Model (10).

3.2 Critique Model Using Relevant Diagnostics


Offers An Interpretation Of The Model Characteristics, Goodness Of Fit And Graphical Diagnostics (5) For The Model Built In 3.1.

Explains Any Potential Weaknesses (5). (When Multiple Models Are Considered a Concise Explanation For The Selection Of One Model To Present In Answer To 3.1.)

3.3 Suggest Improvements To Your Model


Based On The Findings In 3.2 Articulates Possible Alternative Approaches To Address Them (5).

4. Extension Work



Format Of The Assessment

[Provide Guidelines On Expected Format And Length Of Submission]

Submission Instructions

You Must Submit Your Coursework As a .Rmd File On Wiseflow By 10th Of January 2022 At 11am. You Can Follow The Link To Wiseflow Through The Module’s Section On Blackboard Learn Or Login In Directly At Https://Uk.Wiseflow.Net/Brunel. The Name Of Your File Should Follow The Normal Convention Set Out In The Student Handbook, And Must Therefore Include Your Student Id Number (e.g., 0612345.Rmd). It Can Also Include The Module Code (e.g., Cs2001_0612345.Rmd).

Avoiding Academic Misconduct:

Before Working On And Then Submitting Your Coursework, Please Ensure That You Understand The Meaning Of Plagiarism, Collusion, And Cheating (Including Contract Cheating) And The Seriousness Of These Offences. Academic Misconduct Is Serious And Being Found Guilty Of It Results In Penalties That Can Reduce The Class Of Your Degree And May Lead To You Being Expelled From The University. Information On What Constitutes Academic Misconduct And The Potential Consequences For Students Can Be Found In Senate Regulation 6.

You May Also Find It Useful To Read This Powerpoint Presentation Which Explains, In Plain English, The Different Kinds Of Misconduct, How To Avoid (Even Accidently) Committing Them, How We Detect Misconduct, And The Common Reasons That Students Give For Engaging In Such Activities.

If You Are Experiencing Difficulties With Any Part Of Your Studies, Remember There Is Always Help Available:

  • Speak To Your Personal Tutor.If You’Re Not Sure Who Your Tutor Is, Please Ask The Taught Programmes Office ([email protected]).
  • Alternatively, If You Prefer To Speak To Someone Outside Of The Department You Can ContactThe Student Support And Welfare.

    Late Coursework

    The Clear Expectation Is That You Will Submit Your Coursework By The Submission Deadline Stated In The Study Guide. In Line With The University’s Policy On The Late Submission Of Coursework (Revised In July 2016), Coursework Submitted Up To 48 Hours Late Will Be Accepted, But Capped At a Threshold Pass (d- For Undergraduate Or c- For Postgraduate). Work Submitted Over 48 Hours After The Stated Deadline Will Automatically Be Given a Fail Grade (f).

    Please Refer To The Computer Science Student Information Pages And The Coursework Submission Procedure Pages For Information On Submitting Late Work, Penalties Applied And Procedures In The Case Of Extenuating Circumstances.

    Appendix 1 : The Metadeta

    Column Name

    Column Description


    Player Id Code


    Player Potential Overall Attribute – Measured On a Scale 0-100


    Weekly Player Wage In Eur


    Player Age


    Player Height In Cm


    Player Weight In Kg.


    Name Of The Player’s Club


    Player Preferred Foot


    Player Pace Attribute – Measured On a Scale 0-100


    Player Shooting Attribute– Measured On a Scale 0-100


    Player Passing Attribute– Measured On a Scale 0-100


    Player Dribbling Attribute– Measured On a Scale 0-100


    Player Defending Attribute– Measured On a Scale 0-100


    Player Physic Attribute– Measured On a Scale 0-100


    Player Strength Attribute– Measured On a Scale 0-100


    Player Long Shots Attribute– Measured On a Scale 0-100


    Binary Variable Based On Weekly Wage - Is Weekly Wage Above 8000 Euro

sales chat
sales chat