At the end of 2024, Alphabet had 183,323 full-time employees. Up until 2015, these figures were reported as Google employees. The alphabet was created through a corporate restructuring of Google in October 2015 and became the parent company of Google as well as several of its former subsidiaries, including Calico, X, CapitalG and Sidewalk Labs. Google’s popularity Google is one of the most famous internet companies in the world, and in May 2024, the most visited multi-platform website in the United States, with over 278 million U.S. unique visitors during that month alone. The California-based multinational internet company has been delivering digital products and services since its creation in 1996. Due to the popularity of its search engine, the verb “to google” has entered the everyday language and the Oxford Dictionary. In addition to that, the company has also crafted itself as one of the most desirable employers, largely due to the many perks it offers in its offices worldwide. Some of the most appealing aspects of working for Google according to its employees include readily available foods and drinks, good working conditions, and ample communal spaces for relaxing, as well as many health benefits and generous salaries. Google offices and employees As of February 2022, Google and Alphabet had more than 70 offices in over 200 cities throughout 50 around the globe, including Germany, Czechia, Finland, Canada, Mexico, Turkey, and New Zealand. The company’s headquarters, also known as “the Googleplex,” are located in Mountain View, California, while other office locations in American states include New York, Georgia, Texas, Washington D.C., and Massachusetts. As Alphabet, the company employs a total over 182 thousand full-time staff, in addition to many other temporary and internship positions. Per the most recent diversity report published in July 2021, most Google employees were male and only 34 percent were female – a figure that has barely changed since the company started reporting on the diversity of its employees in 2016. Furthermore, as of 2021, women occupied only 28.1 percent of leadership positions and 24.6 percent of tech positions. Although Google has regularly stated that the company is committed to promoting ethnic diversity among its personnel, some 54.4 percent of its U.S. employees are White and only 3.3 percent of employees are Black.

As one of the largest tech companies in the world, Google plays a major role in shaping digital infrastructure, workplace culture, and talent demand. From artificial intelligence research to cloud computing and global ad platforms, its workforce powers the innovation behind billions of daily interactions. For instance, Google’s growing investment...

In the fiscal period that ended December 31, 2023, Google UK Limited's average number of employees was 7,442 employees. This was a slight increase compared to the previous reporting period ending December 31, 2022, where 7,005 individuals were reported to have had worked for the company in the UK.

In fiscal year 2024, the total number of employees at Alphabet was 183,323. The employee count increasedby 821 from 182,502 (in 2023) to 183,323 (in 2024). It represents a 0.45% year-over-year growth in employee count.

As of January 2024, the majority of Google employees worldwide, almost 66 percent, were male. The distribution of male and female employees at Google hasn’t seen a big change over the recent years. In 2014 the share of female employees at Google was 30.6 percent. In 2021 this number has increased by only 3 percent. Considering that the total number of Google employees increased greatly between the years 2007 and 2020, the female quota among the employees had seen rather a small increase. Google as a company Google is a diverse internet company that provides a wide range of digital products and services. In 2022, the company’s global revenue was over 279 billion U.S. dollars. Most of its revenue, around 305 billion U.S. dollars, was from advertising. Among its services, the most popular ones are YouTube and Google Play. Male and female employees at tech companies Google is not the only tech company with a lower number of female employees. This pattern can be seen in other big tech companies too. In 2019, in a ranking of 20 leading tech companies worldwide, only 23andMe had more than a 50 percent share of female employees. The majority of tech companies in the ranking have far more male than female employees.

As of 2024, 7.5 percent of U.S. Google employees were of Hispanic or Latinx ethnicity. The biggest share of Google employees were white. Currently, more than four in ten Google employees were white, down from more than six in ten in 2014.

In 2023, Amazon.com was the top-ranked internet company based on number of employees. The e-commerce giant reported a workforce of more than **** million employees. Amazon has consistently topped the ranking as the online company with the biggest workforce, but the global COVID-19 pandemic has widened the gap as e-commerce has boomed since. During the same period, Meta (formerly Facebook Inc.) had a total of ****** full-time employees. Additionally, Google's parent company Alphabet had ******* full-time workers in 2024.

In 2024, 4.3 percent of U.S. Google leadership employees were of Latinx ethnicity. The majority of leadership employees, about six in ten, were white. Asian Google employees accounted for the second-largest group of employees in leadership positions.

In 2024, the share of female Google employees worldwide in leadership positions amounted to 32.8 percent. The majority of leadership employees were men. Overall, about two thirds of Google employees worldwide were male.

Google 2020-2025 Stock Price

Alphabet Inc. is an American multinational technology conglomerate holding company headquartered in Mountain View, California. Alphabet is the world's second-largest technology company by revenue, after Apple, and one of the world's most valuable companies. It was created through a restructuring of Google on October 2, 2015, and became the parent holding company of Google and several former Google subsidiaries. It is considered one of the Big Five American information technology companies, alongside Amazon, Apple, Meta, and Microsoft.

The establishment of Alphabet Inc. was prompted by a desire to make the core Google business "cleaner and more accountable" while allowing greater autonomy to group companies that operate in businesses other than Internet services. Founders Larry Page and Sergey Brin announced their resignation from their executive posts in December 2019, with the CEO role to be filled by Sundar Pichai, who is also the CEO of Google. Page and Brin remain employees, board members, and controlling shareholders of Alphabet Inc.

Source: https://en.wikipedia.org/wiki/Alphabet_Inc.

Information about this dataset

This dataset provides historical data of GOOG. stock (Google). The data is available at a daily level. Currency is USD.

These terms are key indicators in stock market trading and analysis, providing information about a stock's price movements and trading activity over a specific period (e.g., a day, week, or month):

Close Price:

The final price at which a stock trades during a specific trading session (e.g., at the end of the day). This price is often used as a reference point for comparing daily price movements.

Open Price:

The first price at which a stock trades when the market opens for the day. It can be influenced by after-hours trading, news, or economic events.

High Price:

The highest price at which a stock trades during a specific trading session. It shows the maximum value reached by the stock in that period.

Low Price:

The lowest price at which a stock trades during a specific trading session. It represents the minimum value reached by the stock in that period.

Volume:

The total number of shares traded during a specific period. It indicates the level of interest or activity in a stock, with higher volumes often reflecting greater market interest or volatility.

The global HR software market is estimated to be valued at XXX million in 2025, with a CAGR of XX% from 2025 to 2033. The market is driven by the increasing adoption of cloud-based HR solutions, the growing need for efficient and effective HR management, and the increasing focus on employee engagement and retention. Cloud-based HR solutions offer a number of advantages over on-premises solutions, including lower costs, greater flexibility, and easier access to data. Additionally, the growing need for efficient and effective HR management is driving the market, as organizations seek to streamline their HR processes and improve their employee management capabilities. Key trends in the HR software market include the increasing adoption of artificial intelligence (AI) and machine learning (ML) technologies, the growing use of mobile HR applications, and the increasing focus on employee experience. AI and ML technologies can be used to automate a variety of HR tasks, such as recruiting, onboarding, and performance management. Mobile HR applications allow employees to access their HR information and services from anywhere, anytime. The increasing focus on employee experience is driving the demand for HR software solutions that provide employees with a personalized and engaging experience.

This dataset provides processed and normalized/standardized indices for the management tool group focused on 'Talent & Employee Engagement', including concepts like Employee Engagement Surveys/Systems and Corporate Codes of Ethics. Derived from five distinct raw data sources, these indices are specifically designed for comparative longitudinal analysis, enabling the examination of trends and relationships across different empirical domains (web search, literature, academic publishing, and executive adoption). The data presented here represent transformed versions of the original source data, aimed at achieving metric comparability. Users requiring the unprocessed source data should consult the corresponding Talent/Engagement dataset in the Management Tool Source Data (Raw Extracts) Dataverse. Data Files and Processing Methodologies: Google Trends File (Prefix: GT_): Normalized Relative Search Interest (RSI) Input Data: Native monthly RSI values from Google Trends (Jan 2004 - Jan 2025) for the query "corporate code of ethics" + "employee engagement" + "employee engagement management". Processing: None. Utilizes the original base-100 normalized Google Trends index. Output Metric: Monthly Normalized RSI (Base 100). Frequency: Monthly. Google Books Ngram Viewer File (Prefix: GB_): Normalized Relative Frequency Input Data: Annual relative frequency values from Google Books Ngram Viewer (1950-2022, English corpus, no smoothing) for the query Corporate Code of Ethics+Employee Engagement Programs+Employee Engagement Surveys+Employee Engagement. Processing: Annual relative frequency series normalized (peak year = 100). Output Metric: Annual Normalized Relative Frequency Index (Base 100). Frequency: Annual. Crossref.org File (Prefix: CR_): Normalized Relative Publication Share Index Input Data: Absolute monthly publication counts matching Engagement/Ethics-related keywords [("corporate code of ethics" OR ...) AND (...) - see raw data for full query] in titles/abstracts (1950-2025), alongside total monthly Crossref publications. Deduplicated via DOIs. Processing: Monthly relative share calculated (Engage/Ethics Count / Total Count). Monthly relative share series normalized (peak month's share = 100). Output Metric: Monthly Normalized Relative Publication Share Index (Base 100). Frequency: Monthly. Bain & Co. Survey - Usability File (Prefix: BU_): Normalized Usability Index Input Data: Original usability percentages (%) from Bain surveys for specific years: Corporate Code of Ethics (2002); Employee Engagement Surveys (2012, 2014); Employee Engagement Systems (2017, 2022). Processing: Semantic Grouping: Data points across related names treated as a single conceptual series representing Talent/Engagement focus. Normalization: Combined series normalized relative to its historical peak (Max % = 100). Output Metric: Biennial Estimated Normalized Usability Index (Base 100 relative to historical peak). Frequency: Biennial (Approx.). Bain & Co. Survey - Satisfaction File (Prefix: BS_): Standardized Satisfaction Index Input Data: Original average satisfaction scores (1-5 scale) from Bain surveys for specific years (same names/years as Usability). Processing: Semantic Grouping: Data points treated as a single conceptual series. Standardization (Z-scores): Using Z = (X - 3.0) / 0.891609. Index Scale Transformation: Index = 50 + (Z * 22). Output Metric: Biennial Standardized Satisfaction Index (Center=50, Range?[1,100]). Frequency: Biennial (Approx.). File Naming Convention: Files generally follow the pattern: PREFIX_Tool_Processed.csv or similar, where the PREFIX indicates the data source (GT_, GB_, CR_, BU_, BS_). Consult the parent Dataverse description (Management Tool Comparative Indices) for general context and the methodological disclaimer. For original extraction details (specific keywords, URLs, etc.), refer to the corresponding Talent/Engagement dataset in the Raw Extracts Dataverse. Comprehensive project documentation provides full details on all processing steps.

Alphabet's CEO salary and other executives compensation in 2021 was as follows: Philipp Schindler Senior Vice President, Chief Business Officer, Google at Alphabet, received a total compensation of $28.66 M in 2021, Prabhakar Raghavan Senior Vice President, Knowledge and Information, Google at Alphabet, received a total compensation of $28.65 M in 2021, Ruth M. Porat Senior Vice President, Chief Financial Officer, Alphabet and Google at Alphabet, received a total compensation of $14.66 M in 2021, Kent Walker President, Global Affairs and Chief Legal Officer, Alphabet and Google at Alphabet, received a total compensation of $14.66 M in 2021, Sundar Pichai Chief Executive Officer, Alphabet and Google, and Director at Alphabet, received a total compensation of $6.32 M in 2021.

This dataset contains raw, unprocessed data files pertaining to the management tool group focused on 'Talent & Employee Engagement', including concepts like Employee Engagement Surveys/Systems and Corporate Codes of Ethics. The data originates from five distinct sources, each reflecting different facets of the tool's prominence and usage over time. Files preserve the original metrics and temporal granularity before any comparative normalization or harmonization. Data Sources & File Details: Google Trends File (Prefix: GT_): Metric: Relative Search Interest (RSI) Index (0-100 scale). Keywords Used: "corporate code of ethics" + "employee engagement" + "employee engagement management" Time Period: January 2004 - January 2025 (Native Monthly Resolution). Scope: Global Web Search, broad categorization. Extraction Date: Data extracted January 2025. Notes: Index relative to peak interest within the period for these terms. Reflects public/professional search interest trends. Based on probabilistic sampling. Source URL: Google Trends Query Google Books Ngram Viewer File (Prefix: GB_): Metric: Annual Relative Frequency (% of total n-grams in the corpus). Keywords Used: Corporate Code of Ethics+Employee Engagement Programs+Employee Engagement Surveys+Employee Engagement Time Period: 1950 - 2022 (Annual Resolution). Corpus: English. Parameters: Case Insensitive OFF, Smoothing 0. Extraction Date: Data extracted January 2025. Notes: Reflects term usage frequency in Google's digitized book corpus. Subject to corpus limitations (English bias, coverage). Source URL: Ngram Viewer Query Crossref.org File (Prefix: CR_): Metric: Absolute count of publications per month matching keywords. Keywords Used: ("corporate code of ethics" OR "employee engagement" OR "employee engagement programs" OR "employee engagement surveys") AND ("human resources" OR "management" OR "organizational" OR "culture" OR "development" OR "performance") Time Period: 1950 - 2025 (Queried for monthly counts based on publication date metadata). Search Fields: Title, Abstract. Extraction Date: Data extracted January 2025. Notes: Reflects volume of relevant academic publications indexed by Crossref. Deduplicated using DOIs; records without DOIs omitted. Source URL: Crossref Search Query Bain & Co. Survey - Usability File (Prefix: BU_): Metric: Original Percentage (%) of executives reporting tool usage. Tool Names/Years Included: Corporate Code of Ethics (2002); Employee Engagement Surveys (2012, 2014); Employee Engagement Systems (2017, 2022). Respondent Profile: CEOs, CFOs, COOs, other senior leaders; global, multi-sector. Source: Bain & Company Management Tools & Trends publications (Rigby D., Bilodeau B., Ronan C. et al., various years: 2003, 2013, 2015, 2017, 2023). Data Compilation Period: July 2024 - January 2025. Notes: Data points correspond to specific survey years. Sample sizes: 2002/708; 2012/1208; 2014/1067; 2017/1268; 2022/1068. Bain & Co. Survey - Satisfaction File (Prefix: BS_): Metric: Original Average Satisfaction Score (Scale 0-5). Tool Names/Years Included: Corporate Code of Ethics (2002); Employee Engagement Surveys (2012, 2014); Employee Engagement Systems (2017, 2022). Respondent Profile: CEOs, CFOs, COOs, other senior leaders; global, multi-sector. Source: Bain & Company Management Tools & Trends publications (Rigby D., Bilodeau B., Ronan C. et al., various years: 2003, 2013, 2015, 2017, 2023). Data Compilation Period: July 2024 - January 2025. Notes: Data points correspond to specific survey years. Sample sizes: 2002/708; 2012/1208; 2014/1067; 2017/1268; 2022/1068. Reflects subjective executive perception of utility. File Naming Convention: Files generally follow the pattern: PREFIX_Tool.csv, where the PREFIX indicates the data source: GT_: Google Trends GB_: Google Books Ngram CR_: Crossref.org (Count Data for this Raw Dataset) BU_: Bain & Company Survey (Usability) BS_: Bain & Company Survey (Satisfaction) The essential identification comes from the PREFIX and the Tool Name segment. This dataset resides within the 'Management Tool Source Data (Raw Extracts)' Dataverse.

The Synthetic Employee Attrition Dataset is a simulated dataset designed for the analysis and prediction of employee attrition. It contains detailed information about various aspects of an employee's profile, including demographics, job-related features, and personal circumstances.

The dataset comprises 74,498 samples, split into training and testing sets to facilitate model development and evaluation. Each record includes a unique Employee ID and features that influence employee attrition. The goal is to understand the factors contributing to attrition and develop predictive models to identify at-risk employees.

This dataset is ideal for HR analytics, machine learning model development, and demonstrating advanced data analysis techniques. It provides a comprehensive and realistic view of the factors affecting employee retention, making it a valuable resource for researchers and practitioners in the field of human resources and organizational development.

FEATURES:

Employee ID: A unique identifier assigned to each employee. Age: The age of the employee, ranging from 18 to 60 years. Gender: The gender of the employee Years at Company: The number of years the employee has been working at the company. Monthly Income: The monthly salary of the employee, in dollars. Job Role: The department or role the employee works in, encoded into categories such as Finance, Healthcare, Technology, Education, and Media. Work-Life Balance: The employee's perceived balance between work and personal life, (Poor, Below Average, Good, Excellent) Job Satisfaction: The employee's satisfaction with their job: (Very Low, Low, Medium, High) Performance Rating: The employee's performance rating: (Low, Below Average, Average, High) Number of Promotions: The total number of promotions the employee has received. Distance from Home: The distance between the employee's home and workplace, in miles. Education Level: The highest education level attained by the employee: (High School, Associate Degree, Bachelor’s Degree, Master’s Degree, PhD) Marital Status: The marital status of the employee: (Divorced, Married, Single) Job Level: The job level of the employee: (Entry, Mid, Senior) Company Size: The size of the company the employee works for: (Small,Medium,Large) Company Tenure: The total number of years the employee has been working in the industry. Remote Work: Whether the employee works remotely: (Yes or No) Leadership Opportunities: Whether the employee has leadership opportunities: (Yes or No) Innovation Opportunities: Whether the employee has opportunities for innovation: (Yes or No) Company Reputation: The employee's perception of the company's reputation: (Very Poor, Poor,Good, Excellent) Employee Recognition: The level of recognition the employee receives:(Very Low, Low, Medium, High)

Attrition: Whether the employee has left the company, encoded as 0 (stayed) and 1 (Left).

This is a subset of ZoomInfo's full Company Data Cube of over 100m Companies worldwide of all shapes and sizes. Accurate and comprehensive commercial B2B data is foundational to an organization’s success. The next wave of top businesses are going to be built by organizations that embrace best-in-class external B2B data as a core part of their business strategy. With seamless access to the world’s best B2B data, drive your account-based strategy, augment your master data strategy, supplement your multi-vendor strategy, or run advanced analysis and modeling. ZoomInfo Data Cubes provide unlimited access to ZoomInfo data where you work, so you can build a reliable go-to-market data foundation to analyze, predict, and engage with the right accounts to drive revenue. Actualize your total addressable market, identify new opportunities for growth, build your account maps with linkages, prospect your buying committee, assign territories, predict look-alikes, share go-to-market insights, and more with holistic account intelligence needed for your sales and marketing needs. Not being data-driven risks suffering from disengaged audiences, missed opportunities, and lost revenue.Weitere Informationen

Context

Technology companies have become a dominant driver in recent years of economic growth, consumer tastes and the financial markets. The biggest tech stocks as a group, for example, have dramatically outpaced the broader market in the past decade.

That's because technology has reshaped in a major way how people communicate, consume information, shop, socialize, and work.

Broadly speaking, companies in the technology sector engage in the research, development, and manufacture of technologically based goods and services. They create software, and design and manufacture computers, mobile devices, and home appliances. They also provide products and services related to information technology.

Content

This dataset contains 3 files with the daily stock price and volume of the three companies: Google, Apple, and Facebook from 07/09/2017 to 07/09/2022. Source: Yahoo! Finance

Profile

Apple

Apple Inc. (AAPL) One Apple Park Way Cupertino, CA 95014 United States 408 996 1010 https://www.apple.com

Sector(s): Technology Industry: Consumer Electronics Full Time Employees: 154,000

Total Revenue (2021): $365,817,000
Net Income (2021):$94,680,000
Exchange: Nasdaq

Google

Alphabet Inc. (GOOG) 1600 Amphitheatre Parkway Mountain View, CA 94043 United States 650 253 0000 https://www.abc.xyz

Sector(s): Communication Services Industry: Internet Content & Information Full Time Employees: 174,014

Total Revenue (2021): $257,637,000 Net Income (2021):$76,033,000 Exchange: Nasdaq

Facebook

Meta Platforms, Inc. (META) 1601 Willow Road Menlo Park, CA 94025 United States 650 543 4800 https://investor.fb.com

Sector(s): Communication Services Industry: Internet Content & Information Full Time Employees: 83,553

Total Revenue (2021): $117,929,000 Net Income (2021):$39,370,000 Exchange: Nasdaq

Acknowledgements

Yahoo! Finance Investopedia Nasdaq

Start A New Notebook!

The American technology company Microsoft Corporation employed approximately ******* people in full-time positions worldwide as of its 2025 financial year. Around ** percent of Microsoft’s employees are located in the company’s home country the United States. The employees are spread out over four business units: operations (manufacturing, distribution, product support, and consulting services), research and development, sales and marketing, and general and administration. Product portfolio and business segments Microsoft sells a wide range of consumer and enterprise software, hardware, and services. The technology company had a revenue standing at around *** billion U.S. dollars in fiscal year 2025, most of which came from its productivity and business processes segment. For example, Microsoft Windows is a dominating presence in the desktop operating systems market. Microsoft U.S. tech giant Microsoft is one of the biggest technology companies in the United States next to Apple, Facebook, Google, Amazon, and IBM. Microsoft’s market capitalization has consistently grown to over ***** trillion U.S. dollars over the period from 2014 to 2024. Today, Microsoft is one of the most valuable brands worldwide, with only Apple having a higher brand value.

Employee-Reviews Description Context Over 67k employee reviews for Google, Amazon, Facebook, Apple, and Microsoft

Content This dataset contains employee reviews separated into the following categories:

Index: index Company: Company name Location : This dataset is global, as such it may include the country's name in parenthesis [i.e "Toronto, ON(Canada)"]. However, if the location is in the USA then it will only include the city and state[i.e "Los Angeles, CA" ] Date Posted: in the following format MM DD, YYYY Job-Title: This string will also include whether the reviewer is a 'Current' or 'Former' Employee at the time of the review Summary: Short summary of employee review Pros: Pros Cons: Cons Overall Rating: 1-5 Work/Life Balance Rating: 1-5 Culture and Values Rating: 1-5 Career Opportunities Rating: 1-5 Comp & Benefits Rating: 1-5 Senior Management Rating: 1-5 Helpful Review Count: A count of how many people found the review to be helpful Link to Review : This will provide you with a direct link to the page that contains the review. However it is likely that this link will be outdated NOTE: 'none' is placed in all cells where no data value was found.

Acknowledgements This data was scraped from Glassdoor

3 Inspiration To inspire people to create ML models to search for meaningful trends within this dataset

Introduction

This case study is my capstone project for the Google Data Analytics Certification. This is my first notebook, so should I incorrectly credit or display this data, please add a comment and I will make adjustments. I have enjoyed this challenge, and I look forward to growing and developing as a data analyst.

Thank you to Cedric Aubin for introducing me to this dataset here.

Scenario

I am a junior data analyst in an HR department of a multi-national company. I have been given data on AI/ML salaries from countries around the world (over a three year period from 2020 to 2022). The company is having difficulty balancing the high cost of tech talent with the highly competitive market for talent. The business questions are, what is the going rate for ML/AI employees in the US where our highest salaries are paid, and which countries might provide the same workforce at a reduced cost to help manage the growing salary overhead?

I will analyze the data to answer the questions with a particular focus on the United States where salaries are amongst the highest and there is a highly competitive market for tech talent. I will also analyze the data to provide answers to the question of which countries might be worth investing in to establish a network of employees at a potentially cheaper rate.

Ask

Study the data on salaries and provide answers to the following more detailed questions:

1) What are the average salaries per job title in the US?

Reason: With current salaries (per job title) we can target our candidate packages to be enticing and help beat the competition in the war for talent in the US, our country source for most employees.

2) What countries pay the lowest salaries on average so that we can consider establishing a presence in those countries, or hiring there to reduce global salary overhead?

Reason: While considering our global hiring strategy, we would like to consider setting up hubs around the world that will offer tech talent at a reduced cost.

Prepare

The original data was collected and made available at ai-jobs.net..

Ai-jobs collects salary information from professionals around the world in the AI/ML and Big Data space and makes it publicly available for anyone to use, share and play with. The data is being updated regularly with new data coming in, usually on a weekly basis. The primary goal is to have data that can provide better guidance in regards to what's being paid globally.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10824815%2F856a5811338d692669b8c86d4ef2ccad%2FThis%20one%20too.png?generation=1671140523190325&alt=media" alt="">

Process

I decided to use SQL for my analysis and Tableau for visualization of the key findings.

In total, the complete data is held in one table "salaries" and has 1,332 rows. For my analysis, I removed a number of fields that weren't pertinent e.g. employment_type and experience_level. Instead, I focused on employee_residence, salary_in_usd, work_year and job_title.

Data was cleaned and checks were made to ensure there were no duplicates or other errors:

SELECT DISTINCT job_title FROM salaries

• I discovered that I have 64 job titles to work with and no duplicates or errors. Each job title was listed from this query.

SELECT DISTINCT work_year FROM salaries

• This query checked that the data only spans 2020, 2021, and 2022

SELECT DISTINCT salary_in_usd
FROM salaries 
ORDER BY salary_in_usd

• This yielded 574 results for all salaries from the minimum to the maximum in all job titles. The lowest salary in US dollars per year is 2,324 USD, and the highest is 600,000 USD.

SELECT DISTINCT employee_residence FROM salaries

• There are 64 distinct employee countries of residence in the data. The abbreviations are from the ISO 3166 standard country codes. Here's an example of some of the results:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10824815%2F9ce5e3fa93709fa4e72257ea93264f57%2FScreen%20Shot%202022-12-01%20at%208.28.53%20AM.png?generation=1669904953100886&alt=media" alt="">

When considering averages per country in all job categories, I decided to remove any employee residence results with ****less than 10**** entries. This removes outliers or countries with incomplete amounts of data. This is the query I used:

select employee_residence, COUNT (*)
FROM 
salaries 
GROUP BY employee_residence
having count(*) > 10

Analyze & Share

As expected, the country with the highest salaries for 2020, 2021 and 2022 is the United States, by a large degree. The closest 3 countries are ...