Tech Salaries by Kaggle | Finance and Economics

About this Dataset

Tech Salaries

A Detailed Look into the US and International Salary & Experience Landscape

By Brandon Telle [source]

About this dataset

Explore the tech industry landscape from the inside out with this comprehensive dataset of user salaries and survey responses from 2016 Hacker News. Detailed and cleansed of impurities, this data cumulated from a publicly shared questionnaire offers a valuable peek into pay rates, geographical relevance, job titles and their categories. Submitted comments promise an enlightening account of typical salaries for employers such as Google or Amazon alongside educational requirements, experience levels and length-of-service bonuses - meant to guide future aspirants on their journey towards career success. If you're considering a change in roles or looking to land your dream job in tech - take advantage of Hacker News' invaluable insights!

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How to use the dataset

-Understand the data – The first step is to read and understand what each of the columns are and what type of information they contain (e.g., text strings, numbers). This will help you develop an understanding for how to use this data further on in your project.

Clean the Data – Once you have an understanding for what type of data is contained within each column it's important to clean it up so that it can be analyzed properly and accurately reflect trends within the tech industry. This can include transforming string fields into numerical values or other forms if desired, cleaning irrelevant data points (such as outliers), or rearranging values to make more sense for your project's purpose/needs

Analyze – Now that your data has been cleaned there are many possibilities on how one may proceed with analyzing this information. One option would be clustering techniques such as k-means clustering which could essentially group together workers with similar wage levels while another may choose visual methods such as box plots or histograms which provides a good visual representation of relative salary differences between different employers, job titles etc..

Create Reports & Disseminate Results – After analyzing your dataset it is important to create interesting reports from them such as charts or graphs that paint an interesting picture about trends within the tech industry regarding salary comparisons . These findings should then be shared back out into relevant channels where stakeholders involved can help utilize these findings effectively!

Research Ideas

Uncovering location trends in the tech industry:By analyzing the location_name,location_state,location_country and location_longitude/latitude columns we can uncover geographical clusters of activity in the tech industry, identify differentiation in expected salaries by city/region, and observe how job titles & credentials vary by locations.

Assessing & predicting career paths: By grouping job title categories together (e.g.:developers vs operations) with parameters such as total experience years or years with current employer we can potentially build statistical models that assess progression between different roles within a career path (for example from front-end developer - > lead engineer -> engineering manager).

Analyzing salary differences between men & women: By Leveraging comments column or further data cleansing of other columns like ‘employer name’ we may be able to gather more insights about who submitted survey information. These demographic insights could then be leveraged when assessing salary statistics to uncover potential inequalities between genders related to compensation structures within each job title description

Acknowledgements

If you use this dataset in your research, please credit the original authors.
Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: salaries_clean.csv

Column name	Description
employer_name	Name of the employer. (String)
location_name	Name of the location. (String)
location_state	State of the location. (String)
location_country	Country of the location. (String)
location_latitude	Latitude of the location. (Float)
location_longitude	Longitude of the location. (Float)
job_title	Job title of the respondent. (String)
job_title_category	Category of the job title. (String)
job_title_rank	Rank of the job title. (Integer)
total_experience_years	Total experience of the respondent. (Integer)
employer_experience_years	Experience with the current employer. (Integer)
annual_base_pay	Annual base pay of the respondent. (Integer)
signing_bonus	Signing bonus of the respondent. (Integer)
annual_bonus	Annual bonus of the respondent. (Integer)
stock_value_bonus	Stock value bonus of the respondent. (Integer)
comments	Comments from the respondent. (String)
submitted_at	Date and time of submission. (DateTime)

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit Brandon Telle.

Tables

Salaries Clean

@kaggle.thedevastator_know_your_worth_tech_salaries_in_2016.salaries_clean

103.38 KB
1655 rows
19 columns


CREATE TABLE salaries_clean (
  "index" BIGINT,
  "salary_id" BIGINT,
  "employer_name" VARCHAR,
  "location_name" VARCHAR,
  "location_state" VARCHAR,
  "location_country" VARCHAR,
  "location_latitude" DOUBLE,
  "location_longitude" DOUBLE,
  "job_title" VARCHAR,
  "job_title_category" VARCHAR,
  "job_title_rank" VARCHAR,
  "total_experience_years" DOUBLE,
  "employer_experience_years" DOUBLE,
  "annual_base_pay" DOUBLE,
  "signing_bonus" DOUBLE,
  "annual_bonus" DOUBLE,
  "stock_value_bonus" VARCHAR,
  "comments" VARCHAR,
  "submitted_at" VARCHAR
);