Homework answers / question archive / BE277 Coursework Spring 2022 Imaging yourself as an analyst of the Digital Development initiative of World Bank which focuses on expanding access to fast, affordable internet, and developing reliable online platforms that promote improved service delivery, good governance, and social accountability

BE277 Coursework Spring 2022 Imaging yourself as an analyst of the Digital Development initiative of World Bank which focuses on expanding access to fast, affordable internet, and developing reliable online platforms that promote improved service delivery, good governance, and social accountability

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BE277 Coursework Spring 2022

Imaging yourself as an analyst of the Digital Development initiative of World Bank which focuses on expanding access to fast, affordable internet, and developing reliable online platforms that promote improved service delivery, good governance, and social accountability. As an analyst you are required to identify 10 countries which you believe are the best candidates in need for aid to achieve digital development. The selection of countries is to be done based on need for aid and the ability to use the aid effectively. Additionally, you need to rank these 10 countries starting with the most deserving to the least deserving with this set.

All the data that you require for this task will be available at the World Bank’s own website: http://data.worldbank.org/

http://www.worldbank.org/en/topic

You will have to choose which data sets are relevant for your task and then you will have to download and organise the data as you may see fit. The data indicators (http://data.worldbank.org/indicator) and the data catalogue (http://datacatalog.worldbank.org/) on the website provide information on all databases available for download.

You may use any data you see fit and adopt any set of methodologies you prefer. The requirement is your choices need to be scientifically justified, and all details about your work and relevant findings must be explained in a Report. At least two types of analytics are required. Below are some suggestions:

1. Descriptive analytics. For example, you can use the methods introduced in this module to describe your dataset, such as partition a set of countries into different clusters/groups with K-means algorithm.
2. Predictive analytics. For example, if the dataset is time series dataset, you can use regression, exponential smoothing or ARIMA model to forecast the future. If you want to predict based on available information, will the aid successful or not, you can use logistic regression or decision tree and other algorithms introduced in this module.

The Report should contain the following sections:

1. Introduction (200 words)

Outline the research context, a brief description of the dataset you choose, what methods you choose to conduct the analysis

2. Short literature review (300 words, optional)
3. Methodology and Data (600 words)
4. Analysis and Results (600 words)
5. Conclusion and Limitations (300 words)

You are free to add more sections or break up the above ones into two or more if you so desire. Note that you are not required to carry out an exhaustive literature survey, but you can refer to existing work on this topic if you so desire and if relevant for your analysis. You are required to hand in all R code that you have used for the analysis in an Appendix.

The word limit for this Report is 2000 words (excluding Figures, Tables and the Appendix with R code).

Module Learning Outcomes

 

On successful completion of the module, students will be able to:

• obtain a critical understanding of principal theoretical approaches to analysing large data sets available in the modern business world
• develop key analytical skills of analysing these datasets using modern computational tools and techniques from a practitioners’ point of view
• gain overall perspective on the importance of data analysis and other quantitative techniques in both strategic and tactical decision making faced by managers and entrepreneurs in the modern business world
• evaluate typical data related questions faced by managers and entrepreneurs, and be able to devise analytical strategies to tackle these problems
• critically differentiate between the questions which can be tackled using quantitative methods and large data sets and which cannot be answered using the same, but which requires a mixed approach

Coursework Submission Requirements

The coursework should be word-processed, double spaced, and written in an appropriate academic style (Harvard reference style).

 

The assignment should have a clear introduction and a conclusion. You should ensure that you have fully acknowledged the work of others in the body of the text. Coursework will be processed with plagiarism detection software. Your work will be further investigated if it has more than 30% similarities.

 

The assignment should include a full list of references for all articles, books and other sources (e.g., Internet sites) that have been cited in the assignment.

 

All coursework will be anonymous (unless otherwise specified in the ‘Assessment’ section of this module outline), so you should ensure that only your 7-digit registration number is included in the header. Filename of your coursework should be your 7-digit registration number plus _BE 277. Provision is made for modules where it is not practical to have anonymous marking, e.g., for presentations.

 

Assignments should be submitted electronically to FASER by 11:59:59 on 4/April/2022.

 

For details on electronic submission, see:

http://faser.essex.ac.uk/

 

Key readings

• Evans, James Robert. Business analytics. London: Pearson, 2017.
• Berry, Michael JA, and Gordon S. Linoff. Data mining techniques: for marketing, sales, and customer relationship management. John Wiley & Sons, 2004.
• Shmueli, Galit, and Kenneth C. Lichtendahl Jr. Practical time series forecasting with r: A hands-on guide. Axelrod Schnall Publishers, 2016.
• Ohri, Ajay. R for business analytics. Springer Science & Business Media, 2012.

Recommended journals

• Management science
• Information system research
• Decision support system
• Omega