Data Science Job Salaries Dataset contains 11 columns, each are work_year :- The year the salary was paid.
experience_level :- The experience level in the job during the year.
employment_type :- The type of employment for the role.
job_title :- The role worked in during the year.
salary :- The total gross salary amount paid.
salary_currency :- The currency of the salary paid as an ISO 4217 currency code.
salaryinusd: :- The salary in USD.
employee_residence :- Employee's primary country of residence in during the work year as an ISO 3166 country code.
remote_ratio :- The overall amount of work done remotely.
company_location :- The country of the employer's main office or contracting branch.
company_size :- The median number of people that worked for the company during the year.
Data is collected from Kaggle. Here is the link of dataset https://www.kaggle.com/datasets/arnabchaki/data-science-salaries-2023
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The purpose of the analysis: is to get useful information from data. Here we are analyzing Data Science Salaries dataset . The basic purpose of this analysis is to get answers of some business questions from this dataset.
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This project starts with exploring and cleaning a dataset to prepare it for analysis. The data exploration process involved identifying and understanding the characteristics of the data, such as the data types, missing values, and distributions of values. The data cleaning process involved detecting and resolving any issues in the data, such as errors, missing values, or duplicate records and remove=ing outliers.
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Once the data has been cleaned and prepared, we will create visualizations graphs and check the relationship between different variables of the dataset.
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We will perform exploratory data analysis (EDA) of this dataset that consists of three steps (1) Univariate Analysis (2) Bivariate Analysis (3) Multivariate Analysis.