Data has become a highly prevalent, powerful and potentially dangerous comodity in our society. From websites and social media to the government and smart cities, there is data collection everywhere. The need to have people that can explore, protect and maximize the positive potential of that data is huge. Data can help save lives when used to explore biological, medicinal, chemical and physical threats. Data can improve quality of life by exploring lifestyles, daily habits, psychological therapies and treatments, physical activity and mindfulness. Data can improve the functionaity of a city or company by exploring where and when more traffic accidents take place in an area, how to improve user engagement and experience, and what security measures result in the least amount of breaches. Data plays a role in decision making, business growth, scientific advancement, overall wellbeing and so much more.
Data careers provide the potential to help make the world a better place. Change comes about first through acknowledging that it is needed. What better way to do so than by showing quantitative and qualitative proof. The dataset in this project goes over the career potentials for those that get into a data related field. These people are needed and knowing that the career is not only personally fulfilling, but also provides strong job growth, relocation opportunities, and competitive compensation further encourages people to pursue it. The data explored in this project is just the tip of the iceberg. There is so much more to explore around educational resources, industry types, industry growth, new technologies and specialization paths. This data can be used for different purposes: whether to explore another career path, help with salary negotiation and/or determine how relocation will alter your opportunities. Looking at and exploring data related careers can help people that currently work in the field as well as people that wish to join.
-
Initial Questions:
- What is the average salary for each year?
- How many unique counties are employees and companies located at?
- What is the average salary for each experience level?
- What percentage of employee's are in the same location as the company?
- How many unique job titles are there?
- What are the different job titles? What is the average salary for each job title?
- How many remote workers are there per year and does it increase or decrease over time?
- What is the average salary per company size (and per job title)?
- How does company size influence salary?
- Are certain job titles more likely to be a specific employement type(FT, PT, etc.)?
Data downloaded from Kaggle.
Kaggle data originally sourced from AI-Jobs.
1. work_year: The year the salary was paid.
2. experience_level: The experience level in the job during the year
EN Entry-level / Junior MI Mid-level / Intermediate SE Senior-level / Expert EX Executive-level / Director
3. employment_type: The type of employment for the role
PT Part-time FT Full-time CT Contract FL Freelance
4. job_title: The role worked in during the year.
5. salary: The total gross salary amount paid.
6. salary_currency: The currency of the salary paid as an ISO 4217 currency code.
The first two letters of the ISO 4217 three-letter code are the same as the code for
the country name, and, where possible, the third letter corresponds to the first letter of the currency name.
https://www.iso.org/iso-4217-currency-codes.html
7. salaryinusd: The salary in USD
8. employee_residence: Employee's primary country of residence in during the work year as an ISO 3166
country code. https://www.iso.org/obp/ui/#search
9. remote_ratio: The overall amount of work done remotely
10. company_location: The country of the employer's main office or contracting branch
11. company_size: The median number of people that worked for the company during the year