Data Profession Salary Trends (2009-2016)

This dataset provides detailed information about the salaries and various attributes of data professionals from 2009 to 2016. It is designed to help understand the salary trends and other related factors in the data profession over this period. The dataset consists of two files, each containing specific details about data professionals, including personal information, job details, and performance metrics.

Salary Prediction of Data Professions.csv - Original File

FIRST NAME: First name of the data professional (String)

LAST NAME: Last name of the data professional (String)

SEX: Gender of the data professional (String: 'F' for Female, 'M' for Male)

DOJ (Date of Joining): The date when the data professional joined the company (Date in MM/DD/YYYY format)

CURRENT DATE: The current date or the snapshot date of the data (Date in MM/DD/YYYY format)

DESIGNATION: The job role or designation of the data professional (String: e.g., Analyst, Senior Analyst, Manager)

AGE: Age of the data professional (Integer)

SALARY: Annual salary of the data professional (Float)

UNIT: Business unit or department the data professional works in (String: e.g., IT, Finance, Marketing)

LEAVES USED: Number of leaves used by the data professional (Integer)

LEAVES REMAINING: Number of leaves remaining for the data professional (Integer)

RATINGS: Performance ratings of the data professional (Float)

PAST EXP: Past work experience in years before joining the current company (Float)

cleaned_data.csv - Processed File

FIRST NAME: First name of the data professional (String)

LAST NAME: Last name of the data professional (String)

SEX: Gender of the data professional (String: 'F' for Female, 'M' for Male)

DOJ (Date of Joining): The date when the data professional joined the company (Date in MM/DD/YYYY format)

CURRENT DATE: The current date or the snapshot date of the data (Date in MM/DD/YYYY format)

DESIGNATION: The job role or designation of the data professional (String: e.g., Analyst, Senior Analyst, Manager)

AGE: Age of the data professional (Integer)

SALARY: Annual salary of the data professional (Float)

UNIT: Business unit or department the data professional works in (String: e.g., IT, Finance, Marketing)

LEAVES USED: Number of leaves used by the data professional (Integer)

LEAVES REMAINING: Number of leaves remaining for the data professional (Integer)

RATINGS: Performance ratings of the data professional (Float)

PAST EXP: Past work experience in years before joining the current company (Float)

DAY: Day of the current date (Integer)

MONTH: Month of the current date (Integer)

YEAR: Year of the current date (Integer)

Usage:

This dataset is valuable for researchers, analysts, and data enthusiasts who want to explore and analyze salary trends in the data profession. It can be used to build predictive models, perform statistical analysis, and gain insights into how different factors such as gender, age, experience, and performance ratings affect salaries in the data industry.

Source:

This data was sourced from Kaggle - Salary Prediction of Data Professions. It has been cleaned and prepared for analysis.

License:

Please refer to the original dataset on Kaggle for licensing details