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BIA B452F Assignment 1
Weighting: 30% (Deadline: 30 March 2022, Wednesday)

Learning outcome:
 Explain and select analytic techniques for business intelligence and big data analysis.
 Apply data visualization tools and predictive analytics to summarize and analyze business data.

Important note:
 You should note that there might not be a single correct answer to the questions. Your answers to
these questions may be different from each other and could all be equally valid.
 This is an individual assignment. Copying some or all of another student’s assignment is plagiarism.
 Discussing your assignments with other students and seeking their comments and advice is
acceptable but it is not acceptable for two students to hand in assignments that are substantially the
same. When you collaborate on an individual assignment, it is important that the final product is
your own work.
Task
In this assignment, you need to perform exploratory analysis to investigate the salary survey of European
IT specialists conducted in 2019. The sample Dataset “IT Salary Survey EU 2019.csv” consists of 991
observations for the following 21 features (source: https://www.kaggle.com/parulpandey/2020-it-salary-
survey-for-eu-region?select=T+Salary+Survey+EU+2019.csv).
1. age – Age
2. gender – Gender
3. city – City
4. seniority – Seniority level
5. position – Position (without seniority)
6. experience – Years of experience
7. technology – Your main technology / programming language
8. brutto_salary – Yearly brutto salary (without bonus and stocks)
9. bonus – Yearly bonus
10. stocks – Yearly stocks
11. brutto_salary_before – Yearly brutto salary (without bonus and stocks) one year ago. Only answer
if staying in same country
12. bonus_before – Yearly bonus one year ago. Only answer if staying in same country
13. stocks_before – Yearly stocks one year ago. Only answer if staying in same country
14. vacation_days – Number of vacation days
15. home_office_days – Number of home office days per month
16. language_at_work – Main language at work
17. company_name – Company name
18. company_size – Company size
19. company_type – Company type
20. contract_duration – Contract duration
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21. business_sector – Company business sector
(Note: Brutto salary is the sum of salary before the deduction of tax and insurance(s) and the salary
and bonus are in Euro.)
You must apply exploratory analysis to salary package and trend of different IT specialists in European
regions. You must define your own research questions (or hypotheses) and use summary statistics and data
visualization to find the answers for your research questions. For example, you may hypothesize that the
salary package of IT specialists is sexual independent. To collect evidence to verify the hypothesis, you
may derive the average salary by different type of IT specialists and sex to verify the hypothesis, present
the results using bar chart, and then draw your conclusion about the hypothesis.
You must pre-process the data and select appropriate visualization methods in the analysis. You may need
to handle the missing data, re-code the variables, and perform data aggregation. You may use any
appropriate approach to handle the missing data and make reasonable assumptions in the analysis, if
necessary. You must justify your methods and assumptions made. You must analyze the statistics and
graphical output in detail and write up your interpretation.
The following two references should be a good start for preparing this assignment:
 “IT Salary Survey December 2020” at https://www.asdcode.de/2021/01/it-salary-survey-
december-2020.html
 “Assignment 1 Sample Analysis” on OLE
(Note: The sample analysis only illustrates how to write up an analysis report on using R to perform
the exploratory analysis of credit card usage. The program and analysis are not directly applicable
to the given problem. You are expected to provide more in-depth discussion of the findings in your
analysis.)
Write a report to present and discuss your findings of the exploratory analysis. You are recommended to
use R markdown to prepare the report. The report must include an overview of the problem, describe
analysis of the survey data, your hypotheses, R programs/outputs, and analysis.
This individual assignment will be graded based on the following components (for further details please
see rubrics on OLE):
1. Describe analysis
2. Research questions and data analysis
3. Organization and writing skills

Submission Details
Your completed works should be uploaded to OLE before deadline (March 25, Friday), as follows:
1. Analysis report – “Assignment 1”
2. R program (or R markdown) – R program”

Marks will be deducted if any non-compliance with the submission requirements.