Data analyst Salaries
Microsoft Excel Dashboard Project
Abstract
This project explores Data Scientist salaries through an interactive Excel dashboard, analysing variations based on location, role, and seniority. The dataset required minimal cleaning, primarily involving minor formatting adjustments in Microsoft Excel. To enhance the analysis, a table mapping country codes to full country names was web-scraped and integrated into the workbook. Pivot tables were employed to facilitate comprehensive analysis, followed by the design and creation of a dashboard. The final product highlights key performance indicators (KPIs), enables salary comparisons across countries and seniority levels, and incorporates interactive slicers for filtering by year and role. Additionally, a macro button was implemented to reset all slicers, enhancing user functionality.
Data assessment
Source: Data Scientist Salary (2024) Data on Kaggle
Outline: This dataset analyzes Data Scientist salaries based on location, role, experience level, and work setup. It provides key insights into how these factors influence salaries across various roles and regions. The data is particularly valuable for identifying locations with the highest-paying opportunities and evaluating the earning potential of different roles within the field.
Completeness: No gaps in the data, generally a good collection of data from 2020 to 2024.
Accuracy: The dataset is complete, with no gaps, and provides a comprehensive collection of data from 2020 to 2024.
Key variables:
- Salary
- Employee / Company location
- Job title
- Experience level
- Date
Potential relationships: Only one dataset, no relationships required.
Issues and Limitations: No major issues with the data, just needed some basic formatting for currency and expanding the table to provide full words for the country and contract type.
Outcome + Data workflow
Microsoft Excel: An Excel dashboard was developed to provide a clear comparison of salaries across various categories, showcasing the main KPIs of the dataset. Users can filter the data using slicers for year, experience level, and contract type. A macro-enabled reset button allows for quick removal of all applied filters. The KPIs dynamically update based on the selected filters. For data visualisation, bar charts were predominantly used to display salary trends over the years and highlight the top 10 countries and job roles in the dataset. These top 10 charts utilise a SWITCH function linked to pivot tables and are sorted in descending order. Using form controls, the bar charts showing country salaries and job titles can be toggled to visualise the top and bottom 10 of the dataset. A summary table presents salary data with breakdowns by experience level, contract type, and percentage of remote working. This dashboard provides an intuitive and interactive tool for analysing salary patterns and trends.
Analysis Conclusions
Introduction
The aim of this analysis is to explore trends in data scientist salaries over time and across different countries, experience levels, and job roles.
General Trends
Salary over time : The average salary for data scientists has demonstrated a consistent upward trend over the years, showing positive correlation with time. In 2020, the average salary was $179,958, gradually increasing to $202,374 by 2024. However, 2021 saw a notable drop to $99,922, likely influenced by the COVID-19 pandemic, which disrupted industries, employers, and economies worldwide. Overall, the gradual rise in salaries can be attributed to various factors, including inflation, which has increased consistently from 2020 to 2024.
Salary based by country : Countries offering the highest average salaries for data scientists include Qatar, Israel, and Puerto Rico, with figures ranging between $175,000 and $300,000. Beyond these top three, there is a steady decline in average salaries, with most countries around $150,000 or lower. At the opposite end of the spectrum, countries like Ecuador and Moldova report the lowest average salaries, ranging from $15,000 to $20,000, highlighting a significant disparity in compensation between regions.
Experience level : Unsurprisingly, experience plays a critical role in salary progression. Average salaries increase steadily with experience, with the most significant jump occurring between mid-level and senior-level roles, reflecting a $38,000 increase.
Contract type : The type of employment contract has a substantial impact on data scientist salaries. Freelance and part-time roles typically lack executive-level opportunities and offer lower average salaries. On the other hand, executive roles in contract positions command exceptionally high pay, with a $200,000 difference compared to full-time executive positions. However, at lower experience levels, contract roles pay less than full-time roles. Freelance work is the least lucrative, followed by part-time positions.
Job roles : Executive roles, such as Analytics Engineering Manager and Data Science Tech Lead, lead the pack in terms of salaries. In contrast, entry- to mid-level roles, such as Insight Analyst and Sales Data Analyst, rank among the lowest-paid positions. This reflects the natural progression of salaries aligning with responsibility, expertise, and organisational impact.
Key insights and Recommendations
This has been an analysis of Data Scientist salaries with reference to numerous factors which can affect them.
Key outcomes : The average salary of data scientists has generally increased over time, likely driven by factors such as inflation and the growing demand for data science expertise globally. Experience also plays a significant role in salary progression, with substantial increases observed between mid- and senior-level roles. Contract types have a notable impact, with full-time and contract work being the most lucrative, particularly at the executive level. This aligns with role-specific trends, where executive positions such as managers and team leads command the highest salaries, while entry- and mid-level roles earn considerably less. Additionally, salaries vary significantly across countries, reflecting differences in demand for data science expertise and local economic conditions.
Recommendations : In the data science sector, individuals with greater experience and full-time positions tend to command the highest salaries. However, this varies depending on a country’s demand for data science professionals and its economic conditions. To attract and retain skilled talent, organisations should focus on creating opportunities for employees to progress from entry-level roles to higher-paying positions. Alternatively, increasing the salaries of entry-level roles could help draw more qualified individuals into the field, fostering long-term growth and development in the sector.
Files
Microsoft Excel: (Give a minute for the embedded file to load )
NOTE: Zooming in/out the excel file does not work with Firefox browser, try Chrome + The reset filters button is a macro and will only work on desktop Excel with macros enabled. There also form controls in the document which only work in Excel desktop. The file can be downloaded from a button in the bottom right in the embedded file.