The document presents insights into salaries and benefits in the data industry based on a Kaggle dataset, focusing on aspects such as remote work opportunities, average salaries, and job growth globally. It highlights key insights including higher salaries for certain job titles, the prevalence of remote work for mid-level and senior positions, and the analysis methods used to visualize data in Power BI. The author also shares challenges encountered during the analysis and suggestions for further research.

1. SALARIES AND
BENEFITS IN DATA
INDUSTRY
Ximena Bustamante| Data Analyst

2. INTRODUCTION
Data is a flourishing industry. This Kaggle dataset provides insights
into some of the trade’s most common benefits, such as remote
work opportunities, average salaries and job growth across the
world.
This dataset intends to provide valuable understanding for data
enthusiasts and answers common questions such as average
income for a data professional in a specific country. Remote
opportunity by experience level and/or company size. Or where
most companied are located vs. where employees reside.
Come explore this trending industry with me!
2023 Salaries and Benefits in Data Industry-Ximena Bustamante

3. DATASET
Variables and Definitions
(per data source)
# : Index Number (Added)
work_year : The year during which the salary was paid
experience_level : The experience level in the job during the year
employment_type : The type of employment for the role
job_title : The role worked in during the year
salary : The total gross salary amount paid
salary_currency : The currency of the salary paid as an ISO 4217 currency code
salary_in_usd : The salary in USD (FX rate divided by avg USD rate for the respective year via fxdatafoorillacom)
employee_residence : Employee's primary country of residence in during the work year as an ISO 3166 country code
remote_ratio : The overall amount of work done remotely (adapted to reflect In-Person, Hybrid, Virtual instead of
percentages--0,50,100--for greater clarification
company_location : The country of the employer's main office or contracting branch as an ISO 3166 country code
company_size : The average number of people that worked for the company during the year
2023 Salaries and Benefits in Data Industry-Ximena Bustamante
https://www.k aggle.com/datasets/saurabhshahane
/data-science-jobs-salaries

4. KEY INISGHTS
2023 Salaries and Benefits in Data Industry-
Ximena Bustamante

5. KEY INSIGHTS
REMOTE WORK
2023 Salaries and Benefits in Data Industry-Ximena Bustamante
Mid-Level and Senior Level have
more “fully-remote” opportunities
compared to their Entry and
Executive Level counterparts.
Medium size companies offer more
virtual opportunities.
Data Engineer
Data Scientist
Data Analyst
Machine Learning Engineer
Data Architect
Analytics Engineer
Respectively are the titles that offer the
most remote opportunities.

6. KEY INSIGHTS
AVERAGE SALARIES IN THE US
2023 Salaries and Benefits in Data Industry-Ximena Bustamante
Titles such as Data Analytics Lead,
Data Science Tech lead, Director
of Data Science were the top
earners, with average annual
salaries ranging from $60,000 to
$405,000 (USD)
Meanwhile, on average, larger
companies were responsible for higher
salaries and “in-person” also appeared to
lead to a slightly higher salary compared
to virtual

7. KEY INSIGHTS
JOB GROWTH IN THE US
2023 Salaries and Benefits in Data Industry-Ximena Bustamante
Titles that had the most
reoccurrence throughout the years
were Analytics Engineer, Data
Engineer, Data Analyst, Data
Scientist, Data Architect and
Machine Learning Engineer

8. KEY INSIGHTS
WORLDVIEW
2023 Salaries and Benefits in Data Industry-Ximena Bustamante

9. DATA PROCESS-ACQUISITION,
PREPARATION AND ANALYSIS
2023 Salaries and Benefits in Data Industry-
Ximena Bustamante

10. DATA ACQUISITION, PREPARATION AND ANALYSIS
EXCEL
2023 Salaries and Benefits in Data Industry-Ximena Bustamante
• Data was acquired via Kaggle and downloaded as a CSV file
• Data was initially explored and analyzed in Excel using sorting
conditional formatting, pivot tables and pivot charts

11. DATA ACQUISITION, PREPARATION AND ANALYSIS
SQL
2023 Salaries and Benefits in Data Industry-Ximena Bustamante
• Data was then queried and analyzed in SQL Server
Management Studio
• Statements and functions such as create index, case when, count, average,
round, cast, and CTEs were used to prepare and analyze the data
• For complete code, please visit my GitHub repository

12. DATA ACQUISITION, PREPARATION AND ANALYSIS
POWER BI
2023 Salaries and Benefits in Data Industry-Ximena Bustamante
• In Power BI, Power Query Editor was used to prepare the data for
visualization
• Within Power Query, the dataset was checked for nulls or missing values,
unnecessary columns were removed, and tables were merged
• Variables such as company_location and employee_residence data types
were changed to reflect geographical points instead of strings

13. DATA PROCESS-VISUALIZATIONS
2023 Salaries and Benefits in Data Industry-
Ximena Bustamante

14. DATA VISUALIZATION
POWER BI
2023 Salaries and Benefits in Data Industry-Ximena Bustamante
• A dynamic dashboard was created in Power BI to visualize the key
insights previously presented
• Interactive graphs and charts were done for key observations
regarding remote wok and salaries in the US, popular job titles in the
US and a worldview of jobs and employee residence by country

15. CHALLENGES AND COOL
TECHNIQUES
2023 Salaries and Benefits in Data Industry-
Ximena Bustamante

16. CHALLENGES AND COOL TECHNIQUES
2023 Salaries and Benefits in Data Industry-Ximena Bustamante
Challenge: High cardinality. Some of the variables (ie.title. Company location, employee residence) had numerous unique
values
Cool Technique: I identified the top percentage when necessary and grouped/excluded the rest for visualization purposes
Challenge: Large number of variables (columns)
Cool Technique: I included “all pages” slicers and filters to focus the data and to give the viewer the option to drill down
based on specific “work year” or “job title”
Challenge: Unspecified scope/focus. Because this was a self-started project, I did not start it with a question(s) in mind.
Initially, that made me feel overwhelmed as to where to start
Cool Technique: I created a set o questions that guided my whole analysis
What If I had More Time?
If I had more time, I would have compared other top-ranking countries to see how patterns may differ or relate

17. THANK YOU FOR
CHECKING OUT MY
PROJECT!
 Follow me for more project ideas
 If you have any questions, comments, feedback, JOB
OFFERS , feel free to DM me
2023 Salaries and Benefits in Data Industry-Ximena Bustamante