Overall findings
Problem
Scope
Data
Overall findings
- Most data science professionals make between 100,000 and 180,500 USD annually (a small fraction of professionals earn notably over the average.
- Data scientists employed by firms headquartered in nations like Israel, the USA, and Russia usually receive higher average salaries. In contrast, companies based in South East Asia, Africa, and Eastern Europe tend to offer lower average remuneration.
- Data scientists in countries such as Israel, the USA, and Puerto Rico typically enjoy higher average salaries.
- The United States, the United Kingdom, Spain, Canada, and Germany are the top five countries with higher on-site job locations.
- Data scientists are able to command high salaries by being employed by companies located in high-wage countries.
- Data science management is the highest-paid job category.
- The attributes that contribute to reaching the highest salary are Senior experience level, medium company size, full-time job, management category, on-site job, and ML/data architecture job category.
- The best model was Gradient Boosting Classifier with an accuracy of 0.23.
Problem
In the ever-evolving field of Data Science, predicting a data scientist’s salary can be quite a challenging task due to the variability in roles, job requirements, geographical location, and other related factors. While some data scientists may specialize in machine learning, others may be experts in statistical analysis or data visualization. Additionally, data scientists’ salaries can vary widely based on the cost of living and competitive job markets in different locations.
Scope
In this project the following tasks are performed:
- Data analysis: Univariate and bivariate analysis.
- Geographic distribution
- Salary Prediction using ML algorithms: Logistic Regression, Random Forest, Random Forest Classifier, and Gradient Boosting Classifier.
Data
Data scientist of 2023 was obtained from Kaggle. Raw data have the following attributes:
- work_year [categorical]: the year in which the salary was disbursed.
- experience_level [categorical]: The level of experience a person holds in a particular job.
- employment_type [categorical]: full-time, part-time, or contractual.
- job_title [categorical]: The role an individual holds within a company.
- salary [numerical]: The total gross salary paid to the individual.
- salary_currency [categorical]: The specific currency in which the salary is paid.
- salaryinusd [numerical]: The total gross salary amount converted to US dollars. 7. employee_residence [categorical]: 8. Residence of the employee, denoted by an ISO 3166 code. The cost of living of different countries affects the salary levels.
- remote_ratio [ratio]: The proportion of work done remotely. Companies may adjust salaries based on the cost of living in the employee’s location.
- company_location [categorical]: The location of the employer’s main office. Companies in different locations may offer different salary scales based on especific cost of living.
- company_size [categorical]: The median number of employees in the company. Commonly, larger companies often have structured salary scales and may offer higher salaries due to economies of scale and larger revenue streams.
