A Data Analysis Project- Data Science Job Postings.
Data Analysis of Glassdoor Data Science Job postings-2024
“Job hunting may be tough. If you try to adapt and enjoy the process of gaining experience, one day you will succeed.”
Data Analysis is the processing and analyzing of the data in and out and coming up with insights and those insights need to be converted into solutions to any business problems.
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Therefore, a data analyst should be capable of solving any business problems with his valuable insights from data.
Gathered the data from Kaggle. Click here to see the data set.
Done the basic Ad-hoc data analysis using Power BI and as you can see those dashboards here. By looking at them you can easily understand the following insights in a short interval of time. I just removed all spam job postings by considering there are postings not mention company size either they have given null or unknown.
A few insights are:
1. Number of jobs posted in different locations.
2. Number of jobs posted in different sectors/ industries.
3. Number of jobs per different revenue ranges.
4. Number of jobs in different job roles.
5. Number of jobs in a range of employee counts.
6. Average salaries offered by different jobs & and their roles.
7. different rating feedback from former employees of particular companies.
8. Overall rating of companies.
There can be even more insights can be extracted in terms of numerical values and tables and to extract those insights I am going to use SQL and SQL Server for the remaining data analysis part.
Here I have mentioned below even more extensions of insights in detail of data analysis to help job seekers understand the patterns and values of all companies as mentioned in the data.
1. Find the top 5 companies listed with the highest average salary estimates among all job postings from the company.
with cte as (
select company, avg(salary_avg_estimate) as avg_salary
FROM ['Glass door data science jobs'] where company is not null
group by company having avg(salary_avg_estimate) is not null)
select company, avg_salary, rnk from (
select company, avg_salary, dense_rank() over(order by avg_salary desc) as rnk
from cte )x
where rnk<=5
2. list the number of companies that come under different average pay scale ranges (<1L as low, 1L-3L as medium, remaining as high).
select salary_range, count(*) as no_of_companies
from (
select company, case
when salary_avg_estimate < 100000 then 'Low'
when salary_avg_estimate between 100000 and 300000 then 'Medium'
else 'High' end as salary_range
from ['Glass door data science jobs']) a
group by salary_range
order by no_of_companies desc
3. list down the companies with average culture and values rating for companies with a company rating above 4.5
select company, avg(culture_and_values_rating) as avg_culture_rating
from ['Glass door data science jobs']
where company_rating>4.5
group by company
order by 2 desc
4. Find the average management for each sector.
select sector, round(avg(senior_management_rating),2) as avg_senior_rating
from ['Glass door data science jobs'] where sector is not null
group BY sector
order by 2 desc
5. Analyze the average career opportunities rating for companies with different revenues.
select revenue, round(avg(career_opportunities_rating),2) as avg_career_rating
from ['Glass door data science jobs']
where revenue is not null
group by revenue
6. give the average compensation and benefits rating for companies in different locations.
select location,
round(avg(comp_and_benefits_rating),2) as com_and_benefits_rating
from ['Glass door data science jobs']
where salary_avg_estimate is not null
group by location
order by com_and_benefits_rating desc
7. Give me the top 5 industries and sectors with the highest average work-life balance.
select top 5 industry, sector, avg(work_life_balance_rating) as avg_work_life_balance
from ['Glass door data science jobs']
group by industry, sector
order by avg_work_life_balance desc
8. Give me details about the average company rating with its company-size category.
select company_size, round(avg(company_rating),2) as avg_company_rating
from ['Glass door data science jobs'] where company_size is not null
group by company_size
order by avg_company_rating desc
9. Rank sectors based on the average company rating, considering only sectors with more than 10 companies.
select sector, round(avg(company_rating),2) as avg_rating
from ['Glass door data science jobs'] where sector is not null
group by sector
having count(distinct company) > 10
order by avg_rating desc;
This is how we can get many insights from data by analyzing the data using any analytical tool. I hope this article is helpful for you in data analysis and applying basic concepts to data analysis of any data you want to do the data analysis.
Click Here to Prepare a few data analyst interview questions and use cases in my reading list focused on data analysis. You can see the entire analysis in both Power BI and SQL Server Studio files in my GitHub repositories.
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