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Data Analytics Assignment: Analysis and Implementation

Task 1:

On successful completion of this module students will be able to demonstrate:

  1.  A critical understanding of the knowledge base in Data Science and its inter-relationship with other modules in the programme such as Big Data Analytics;
  2.  An ability to work with ideas developed in Data Science at a level of abstraction, arguing from competing perspectives, and identifying the possibility of new concepts within existing knowledge frameworks and relevant approaches;
  3.  The confidence in using investigative strategies and techniques to undertake a critical analysis of machine learning models and evaluating the outcomes of this analysis;

An ability to analyse new, novel and/or abstract data using an appropriate range of established techniques relevant to machine learning algorithms; as well as judging the reliability, validity and significance of evidence to support conclusions and/or recommendations relevant to the subject covered by this module.

This is a group work assignment to be submitted as a 2000 words group report. Students will work in a group of 3-4 students.

In this group assignment, you will apply concepts and principles of Data Science to develop a strategy for quantitative/qualitative analysis and translate this strategy in the implementation of a data analytics engine using appropriate Machine Learning approaches. You are required to test the performance, robustness and correctness of the analytics engine by using known technological solutions.

Your group report will provide justification(s) for the selected data analytics approaches. A reflection on the measures/steps taken to enhance the usability of the developed analytics engine is also required in the report.

Formative feedback will be provided in workshop sessions. Students are advised to have regular meetings during their group project. All group members must contribute to the assignment tasks equally.

Data sets can be found at the end of this document.

Assignment support:

Although you will be guided throughout the module by your lecturer, you can get extra support for your assignment, just make an appointment with the ACE team for any language, research and study skills issues and/or talk, email the Computing ACE expert for any advice on how to approach your assignment. REMEMBER: they are not here to give you the answers!

Specific requirements for the assignment: The recommended tool for this assignment is Python, however, you may use other tools such as Tableau or R.

Wilmslow Astute is a data analytics firm located in Greater Manchester. The company was established in 2016 by two graduate friends who were both passionate about data science and business intelligence. The company has quickly grown to more than 40 staff members serving both national and international clients in various industries and market sectors, namely healthcare, automotive and property.

Due to high demand and inability to quickly hire new Data Scientists, the company has contracted you and your team to complete three pieces of work for them and submit your collective findings in the form of a single report.

Your first task is to discuss the implications and strategies used in Data Science. In task 2, you will be required to extract the required insights about Covid-19 vaccinations from the dataset provided, then to cleanse a car sales dataset and aggregate its required fields and finally, you are required to build a linear regression model in order to predict property prices, using the third and final dataset.

Task 2:

Note: Relevant assumptions, if required, should be made and justified.

Tasks/Deliverables for Assignment 1

Marks will be awarded based on properly tackling each of the following questions and providing your answers in an appropriate format. Please note that the use of tables or tabulated format is only permissible when required.

Task 1:

a)Evaluate the importance of Data Science and appraise its relevance in three different business sectors of your choice.

b)Explain the significance of machine learning and discuss its impacts on modern life.                                                                                  

c)Compare and contrast between Quantitative Analysis and Qualitative Analysis and explain how each of them can be used by Wilmslow Astute.

d)Provide a summary of the Data Analytics Life Cycle while explaining how each of its different stages can be utilised by Wilmslow Astute in better serving its customers.

Task 2:

Use an appropriate tool to analyse the following datasets. The recommended tool is Python, however, you may use other tool(s) such as Tableau, MS Excel or R. Regardless of the tool(s) used, you need to show the main steps taken in obtaining your answers for each of the following questions.

NOTE: Dataset “VaccinationByCountry” can be used to answer questions a) to c), “MercedesBenzSales” to answer question d) and “HousePrices” to answer question e).

a)How many people were vaccinated in February 2021 in each of the following countries:                                                                                  

  • United Kingdom;
  • United States;
  • China;
  • South Africa;
  • Australia?

Present your findings using visualisation(s).

b)Summarise the utilisation of each of the listed vaccines based on the number of countries.                                                                            

c)From 1st to 11th March 2021 what proportion of the population of each of the following countries was fully vaccinated:

  • United Kingdom;
  • Romania;
  • Bulgaria;
  • United Arab Emirates?

How do they compare against the highest and lowest proportions for the same period in the dataset?

d)Calculate the following aggregates for Mercedes Benz CL and SL class units:

  • Sum;
  • Average;
  • Standard Deviation;
  • Variance.

e)Build a simple linear regression model, using “Distance to the nearest station” field as the independent variable to predict “House Price”. Give the values for the intercept, coefficient of the independent variable and R2. Test your model on two randomly generated values of the independent variable.

Based on your result, how can the accuracy of your model be increased?

Presentation, Report Layout and References:

Although much of your report will contain an existing body of knowledge, you must write your assignment in your own words to demonstrate your understanding of the subject. You are required to follow the Harvard referencing system when citing others' work. An accompanying list of references must also be provided as part of your report. Extensively referenced work reflects the level of research you conducted in the process of producing the document. It is also an acknowledgment of another people’s work. Correct referencing demonstrates your academic and professional skill. It also reflects your academic honesty and thus to some degree protects you from cases of plagiarism.

a)You should evaluate the importance of Data Science and assess its relevance in three different business sectors. For example, discussing how data science can be used in retail, automotive and healthcare.                                                              

b)You should explain why machine learning is an important and growing field and how it impacts our daily lives

c)You should compare and contrast between Quantitative Analysis and Qualitative Analysis. Furthermore, you should elaborate on under what circumstances Wilmslow Astute should use Quantitative Analysis and under what circumstances it should us Qualitative Analysis.

d)You should provide a summary of the Data Analytics Life Cycle and briefly explain how each of its different stages can be used by Wilmslow Astute in better serving its customers.

You have correctly loaded the given data and produced the required output for the problems in this task.

You have provided snippets of all outputs.

You have provided a rationale and justification for your solution, and strategies used to produce the desired output.

You have thoroughly commented on your code.

You have used academic literature to support your arguments.

Your report is well laid out and formatted according to the given requirements. Your report is free from grammatical and spelling errors. Harvard references style has been used to cite the work where necessary and a list of references is also provided.

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