Data Analysis on IT Jobs Demand

Posted on Nov 18, 2017
The skills the author demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.

Project Description:

This project is to show you data on the current IT Jobs demand in the USA.
By scrapping the IT Jobs posting Information from the Monster Jobs Website, the statistical data is collected and analyzed to provide you with the following insights:

  • What are the States in the USA having a high demand for IT resources?
  • What are the trending Skills and Job Titles in those States?
  • Which cities in a state are actually searching for those resources of the relevant skills?
  • In addition, you can make a statistical summary of two or more IT Skills to compare their current demand in your selected State and City.
  • Also, you can find links to actual Job posts for your selected categories or skills.

Here is the link to the Shiny App: MONSTER IT Jobs Statistic by Huy Tran. Thank you for reading!

MONSTER IT Jobs Data Statistic App - Introduction:

Firstly, enter your Job Title or Titles (separated by a comma ",") of your interest, and click "Search" button.

For example: in the screenshot below, "Oracle" and "SQL Server" are entered.

  • The US heat map is showing the demand for Oracle and SQL Server skillsets in the States.
  • By selecting the State - California, the State map on the right-hand side is showing you which Cities in the State are actually looking for Oracle and SQL Server.
Data Analysis on IT Jobs Demand

The following bar charts are showing you the top hiring States and Cities by the selected Job Titles/Skillsets.

Data Analysis on IT Jobs Demand

And, by selecting a Time window - the following charts are showing you the number of Job postings during the time, and the actual links to the Job posts.

Data Analysis on IT Jobs Demand

 

Technical Briefing - Data Web Scraping Tactics:

  1. Data from the Jobs Monster Website:

  2. Job Titles Information:
    Data Analysis on IT Jobs Demand

    Location, Time and Number of Posting Information:

     

2. Using Python for web scraping:

  • Packages Used:
    • BeautifulSoup: to analyze HTML page and extract data from the web page.
    • pandas: to store and process Data Frames in memory.
    • re: to compile regular expression for text searching.
    • urllib2: to download Web pages, and handle errors during the downloading process.
    • pickle, os: to save data to local files, and restore data to memory for later processing.
  • Tactics:
    • To reduce the risk of getting disconnected or timeout problems during the process, target pages (HTML pages) were downloaded and saved to local files for the later processing.
    • Though the process is running Mac machine with 16GB memory, the system often got hanged and chances are that you will be losing the data after hours of processing. For example: in this project, I had to download and process about 10,000 pages. So that, I had to make smaller chunks of pages, save them into the local disk, before loading them back to memory for data analyzing and extraction.
  • Python codes snippets:

Web page download function:

Extracting the number of posting information:

 state and the city information:

Extracting posting time information:

3. Data Frame Snapshot:

After extracting data from the Website with Python programs, the Pandas Data Frames are exported into CSV files for Shiny App.

Job Titles data frame - 882 entries:

Location data frame - 7,582 entries:

Time and Job posting data frame - 94,682 entries:

Thank you for reading! Please access to the Shiny App at: MONSTER IT Jobs Statistic.

Questions: Email to [email protected]

About Author

Related Articles

Leave a Comment

Huy Tran January 31, 2018
Thanks for reading. Best!

View Posts by Categories


Our Recent Popular Posts


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 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 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 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