NYC Data Science Academy| Blog
Bootcamps
Lifetime Job Support Available Financing Available
Bootcamps
Data Science with Machine Learning Flagship ๐Ÿ† Data Analytics Bootcamp Artificial Intelligence Bootcamp New Release ๐ŸŽ‰
Free Lesson
Intro to Data Science New Release ๐ŸŽ‰
Find Inspiration
Find Alumni with Similar Background
Job Outlook
Occupational Outlook Graduate Outcomes Must See ๐Ÿ”ฅ
Alumni
Success Stories Testimonials Alumni Directory Alumni Exclusive Study Program
Courses
View Bundled Courses
Financing Available
Bootcamp Prep Popular ๐Ÿ”ฅ Data Science Mastery Data Science Launchpad with Python View AI Courses Generative AI for Everyone New ๐ŸŽ‰ Generative AI for Finance New ๐ŸŽ‰ Generative AI for Marketing New ๐ŸŽ‰
Bundle Up
Learn More and Save More
Combination of data science courses.
View Data Science Courses
Beginner
Introductory Python
Intermediate
Data Science Python: Data Analysis and Visualization Popular ๐Ÿ”ฅ Data Science R: Data Analysis and Visualization
Advanced
Data Science Python: Machine Learning Popular ๐Ÿ”ฅ Data Science R: Machine Learning Designing and Implementing Production MLOps New ๐ŸŽ‰ Natural Language Processing for Production (NLP) New ๐ŸŽ‰
Find Inspiration
Get Course Recommendation Must Try ๐Ÿ’Ž An Ultimate Guide to Become a Data Scientist
For Companies
For Companies
Corporate Offerings Hiring Partners Candidate Portfolio Hire Our Graduates
Students Work
Students Work
All Posts Capstone Data Visualization Machine Learning Python Projects R Projects
Tutorials
About
About
About Us Accreditation Contact Us Join Us FAQ Webinars Subscription An Ultimate Guide to
Become a Data Scientist
    Login
NYC Data Science Acedemy
Bootcamps
Courses
Students Work
About
Bootcamps
Bootcamps
Data Science with Machine Learning Flagship
Data Analytics Bootcamp
Artificial Intelligence Bootcamp New Release ๐ŸŽ‰
Free Lessons
Intro to Data Science New Release ๐ŸŽ‰
Find Inspiration
Find Alumni with Similar Background
Job Outlook
Occupational Outlook
Graduate Outcomes Must See ๐Ÿ”ฅ
Alumni
Success Stories
Testimonials
Alumni Directory
Alumni Exclusive Study Program
Courses
Bundles
financing available
View All Bundles
Bootcamp Prep
Data Science Mastery
Data Science Launchpad with Python NEW!
View AI Courses
Generative AI for Everyone
Generative AI for Finance
Generative AI for Marketing
View Data Science Courses
View All Professional Development Courses
Beginner
Introductory Python
Intermediate
Python: Data Analysis and Visualization
R: Data Analysis and Visualization
Advanced
Python: Machine Learning
R: Machine Learning
Designing and Implementing Production MLOps
Natural Language Processing for Production (NLP)
For Companies
Corporate Offerings
Hiring Partners
Candidate Portfolio
Hire Our Graduates
Students Work
All Posts
Capstone
Data Visualization
Machine Learning
Python Projects
R Projects
About
Accreditation
About Us
Contact Us
Join Us
FAQ
Webinars
Subscription
An Ultimate Guide to Become a Data Scientist
Tutorials
Data Analytics
  • Learn Pandas
  • Learn NumPy
  • Learn SciPy
  • Learn Matplotlib
Machine Learning
  • Boosting
  • Random Forest
  • Linear Regression
  • Decision Tree
  • PCA
Interview by Companies
  • JPMC
  • Google
  • Facebook
Artificial Intelligence
  • Learn Generative AI
  • Learn ChatGPT-3.5
  • Learn ChatGPT-4
  • Learn Google Bard
Coding
  • Learn Python
  • Learn SQL
  • Learn MySQL
  • Learn NoSQL
  • Learn PySpark
  • Learn PyTorch
Interview Questions
  • Python Hard
  • R Easy
  • R Hard
  • SQL Easy
  • SQL Hard
  • Python Easy
Data Science Blog > Python > Web-scraping Glassdoor: Exploring the Data Science Job Market

Web-scraping Glassdoor: Exploring the Data Science Job Market

Aungshuman Zaman
Posted on Aug 29, 2018


Photo by rawpixel on Unsplash

Introduction:

A few years ago, when I first heard about data science, my idea about what the job entails was fairly vague. Everyone has heard how it is rapidly growing into an exciting career option, but where actually do most data scientists work, how much do they make, or what skills are most valued? I wanted to explore first hand the current state of  data related job market, and what better way to do that than doing a data analysis project on it?

This was also an exciting opportunity to build a project from scratch, where you get your data from the world-wide-web, clean it and then do your analysis on it. As much as I looked forward to doing my analysis, one of my primary objective  was to get some expertise in web-scraping and data cleaning. There are many great job websites with huge catalogue of current job postings, e.g. Indeed, Monster or even LinkedIn. However, I decided to explore Glassdoor. The script used to scrape the website is called, aptly, scrape.py. There is also a file called helper.py from where I import useful functions. Both these files and the analysis notebook can be found on the github repository. The csv file containing the scraped data can be found in this link.

Glassdoor is an American company established in 2007. It started off as a platform where employees can anonymously post reviews about salary and workplace environment. It has grown to be one of the most trusted websites for company research and also for job hunting. Glassdoor provides their own salary estimate for many job posting on the website, and also provides a rating for a company; both of which are partly based on employee reviews.

Questions:

At the outset, I was hoping to answer the following questions.

1. Which states have the highest number of data related jobs?
2. Which industries are offering more jobs? Which industries have highest mean salaries?

3. Which companies are hiring most? Which companies offer highest salaries?
4. Does company size matter? Does glassdoor rating have anything to do with salary?

5. Which job positions are mentioned the most?
6. Is there a relationship between job positions and educational qualifications?

7. Which computational skills are most sought after?
8. How does the salary spread look for job positions and educational requirement?

Biggest challenges scraping Glassdoor:

Glassdoor uses javascript to dynamically generate html files. For example, for a particular query Glassdoor will return a number of pages, with 30 job postings on each page. After your first 30 posts, to scrape the thirty first one you have to get to the next page, which can only be accomplished by clicking on the 'next' button.  Also, to get the detailed information about a job posting, you have to click on another set of buttons. I used selenium to get around this problem. The limitation of that approach is its speed. Scrapy can scrape html files really fast, whereas Selenium emulates a browser and is much slower. Also, to not get banned by the website, I had to  put several 'sleep' statements in my code which causes further slow-down.

The second problem with Glassdoor is that it only provides 30 pages for a query.

Although it shows there are many more pages, if you click next after the 30th page, it will tell you that it "can't find the page".

As far as I know, there is no going around it, which is a little deflating. It means that I cannot really scrape all the job postings for a particular location. So, I ended up writing a script, which will randomly create queries for data related jobs for a few major U.S. cities, and then scrape the first 20 to 30 pages.

Eventually, I managed to scrape about 10,000 job postings from Glassdoor. It included company's name, rating, size, industry, the city the job was being offered at, salary range estimated by Glassdoor (on rare occasions, the posting specified a salary) and a detailed description of the job, which sometimes included educational requirement, professional experience and/or preferred skills.

At first, I checked the composition of my sample. It should be noted that my sample is not exactly a random sample, because I searched for jobs for certain U.S cities. A state like California that is represented by three cities, San Francisco, San Jose, and Los Angeles, naturally will be well represented in  the sample.

People may also be interested in the mean and median salary in different states.

The west coast states of California and Oregon had highest median salaries, closely followed by New York. But it should be noted that those are also some of the most expensive states in the country. It may be useful to look at salary normalized by living cost. That will tell us which states offer more for your labor. Unfortunately, I didn't have the time to do that adjustment.
Arguably, when it comes to data related jobs, California and New York are the two most important states. I looked at those two states and tried to find which industries in those two states offer the highest number of jobs.
In California, information technology is by far the largest sector. In New York, although IT is still the largest contributor, business services and finance are also major slices of the pie.

The largest industries employing data professionals right now are IT, business services and finance.

We can also look at the salary boxplot for different industries.

Let's also find the top twenty companies in terms of hiring volume. Amazon seems to be a huge employer right now.

When it comes to paying the most, a different set of companies come to the fore.

I also wanted to investigate if company size plays any role when it comes to salary. The finding in this case is not very conclusive. Smaller companies (less than 50 employees) seem to have lower median salary, but the spread of salary is much bigger than the small difference in median seen in data.

Are company ratings meaningful? The company rating has a bimodal distribution, the second peak at 5 possibly coming from a large number of very happy employees. Rating also seems to go up with average salary. I guess paying your employees well, ensures a good rating in Glassdoor.

Most data related jobs are either for a Data Scientist, a Data Engineer or a Data Analyst position. Our sample had a high number of Data Scientist job postings. It is to be noted, in some cases job title mentions two or more of the three classes mentioned.

Not all job postings explicitly mention an educational requirement. For the ones that do, all required at least a bachelor's degree. A large number of Data Science jobs require a PhD. Very few required an MBA.

Python, SQL and R are the most widely valued skills for data related jobs, followed by Java, Hadoop, Spark and Excel.

Conclusion:

As far as web scraping is concerned, this was a challenging project. One limitation of this analysis was that I did not have any historical data. It will be interesting to gather more data in future and keep this analysis going. In future, a more granular look at the job market is possible. For example, we may differentiate between data science/ engineer/analyst positions and find how salary, educational or skill sets requirement differs for different positions.We may also compare this work on Glassdoor with work on other job sites, e.g. Indeed. We may also explore factors like job experience. I believe this analysis can lead to interesting machine learning projects. For example: given your skill sets, can we recommend which jobs you should apply to? Or given location, salary, company size, industry etc. can we predict job satisfaction (rating)?

About Author

Aungshuman Zaman

I am a Physics PhD with experience of working with big datasets in large scientific collaboration. I worked for the ATLAS high energy physics experiment at the European Particle Physics Laboratory CERN, where I collected, cleaned and interpreted...
View all posts by Aungshuman Zaman >

Related Articles

Capstone
Catching Fraud in the Healthcare System
Capstone
The Convenience Factor: How Grocery Stores Impact Property Values
Capstone
Acquisition Due Dilligence Automation for Smaller Firms
Machine Learning
Pandemic Effects on the Ames Housing Market and Lifestyle
Machine Learning
The Ames Data Set: Sales Price Tackled With Diverse Models

Leave a Comment

Cancel reply

You must be logged in to post a comment.

Matthew February 10, 2019
Heya, I wrote a tool to help with projects like this: https://github.com/MatthewChatham/glassdoor-review-scraper. Give it a whirl!

View Posts by Categories

All Posts 2399 posts
AI 7 posts
AI Agent 2 posts
AI-based hotel recommendation 1 posts
AIForGood 1 posts
Alumni 60 posts
Animated Maps 1 posts
APIs 41 posts
Artificial Intelligence 2 posts
Artificial Intelligence 2 posts
AWS 13 posts
Banking 1 posts
Big Data 50 posts
Branch Analysis 1 posts
Capstone 206 posts
Career Education 7 posts
CLIP 1 posts
Community 72 posts
Congestion Zone 1 posts
Content Recommendation 1 posts
Cosine SImilarity 1 posts
Data Analysis 5 posts
Data Engineering 1 posts
Data Engineering 3 posts
Data Science 7 posts
Data Science News and Sharing 73 posts
Data Visualization 324 posts
Events 5 posts
Featured 37 posts
Function calling 1 posts
FutureTech 1 posts
Generative AI 5 posts
Hadoop 13 posts
Image Classification 1 posts
Innovation 2 posts
Kmeans Cluster 1 posts
LLM 6 posts
Machine Learning 364 posts
Marketing 1 posts
Meetup 144 posts
MLOPs 1 posts
Model Deployment 1 posts
Nagamas69 1 posts
NLP 1 posts
OpenAI 5 posts
OpenNYC Data 1 posts
pySpark 1 posts
Python 16 posts
Python 458 posts
Python data analysis 4 posts
Python Shiny 2 posts
R 404 posts
R Data Analysis 1 posts
R Shiny 560 posts
R Visualization 445 posts
RAG 1 posts
RoBERTa 1 posts
semantic rearch 2 posts
Spark 17 posts
SQL 1 posts
Streamlit 2 posts
Student Works 1687 posts
Tableau 12 posts
TensorFlow 3 posts
Traffic 1 posts
User Preference Modeling 1 posts
Vector database 2 posts
Web Scraping 483 posts
wukong138 1 posts

Our Recent Popular Posts

AI 4 AI: ChatGPT Unifies My Blog Posts
by Vinod Chugani
Dec 18, 2022
Meet Your Machine Learning Mentors: Kyle Gallatin
by Vivian Zhang
Nov 4, 2020
NICU Admissions and CCHD: Predicting Based on Data Analysis
by Paul Lee, Aron Berke, Bee Kim, Bettina Meier and Ira Villar
Jan 7, 2020

View Posts by Tags

#python #trainwithnycdsa 2019 2020 Revenue 3-points agriculture air quality airbnb airline alcohol Alex Baransky algorithm alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus ames dataset ames housing dataset apartment rent API Application artist aws bank loans beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep boston safety Bundles cake recipe California Cancer Research capstone car price Career Career Day ChatGPT citibike classic cars classpass clustering Coding Course Demo Course Report covid 19 credit credit card crime frequency crops D3.js data data analysis Data Analyst data analytics data for tripadvisor reviews data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization database Deep Learning Demo Day Discount disney dplyr drug data e-commerce economy employee employee burnout employer networking environment feature engineering Finance Financial Data Science fitness studio Flask flight delay football gbm Get Hired ggplot2 googleVis H20 Hadoop hallmark holiday movie happiness healthcare frauds higgs boson Hiring hiring partner events Hiring Partners hotels housing housing data housing predictions housing price hy-vee Income industry Industry Experts Injuries Instructor Blog Instructor Interview insurance italki Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter las vegas airport lasso regression Lead Data Scienctist Lead Data Scientist leaflet league linear regression Logistic Regression machine learning Maps market matplotlib Medical Research Meet the team meetup methal health miami beach movie music Napoli NBA netflix Networking neural network Neural networks New Courses NHL nlp NYC NYC Data Science nyc data science academy NYC Open Data nyc property NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time performance phoenix pollutants Portfolio Development precision measurement prediction Prework Programming public safety PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R Data Analysis R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn seafood type Selenium sentiment analysis sentiment classification Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau teachers team team performance TensorFlow Testimonial tf-idf Top Data Science Bootcamp Top manufacturing companies Transfers tweets twitter videos visualization wallstreet wallstreetbets web scraping Weekend Course What to expect whiskey whiskeyadvocate wildfire word cloud word2vec XGBoost yelp youtube trending ZORI

NYC Data Science Academy

NYC Data Science Academy teaches data science, trains companies and their employees to better profit from data, excels at big data project consulting, and connects trained Data Scientists to our industry.

NYC Data Science Academy is licensed by New York State Education Department.

Get detailed curriculum information about our
amazing bootcamp!

Please enter a valid email address
Sign up completed. Thank you!

Offerings

  • HOME
  • DATA SCIENCE BOOTCAMP
  • ONLINE DATA SCIENCE BOOTCAMP
  • Professional Development Courses
  • CORPORATE OFFERINGS
  • HIRING PARTNERS
  • About

  • About Us
  • Alumni
  • Blog
  • FAQ
  • Contact Us
  • Refund Policy
  • Join Us
  • SOCIAL MEDIA

    ยฉ 2025 NYC Data Science Academy
    All rights reserved. | Site Map
    Privacy Policy | Terms of Service
    Bootcamp Application