Scraping Data From Job Portal-An Insight

The Job Industry has never been going through times as turbulent as these. With new roles coming up to handle the different positions that have opened up based on customer requirements, job seekers either need to skill up, or switch roles. Web scraping job feed has been a requirement for most job agencies, but today it is more important than ever before since almost all job listings are directly being posted online, across multiple portals (for better reach). Scraping Data from Job Portal is on an increase and with high demand. Let us take a look at how it is done.

What Are Your Options?

As the owner of a job-portal, or job-agency, you would want to make the most of the current situation and scrape more and more jobs and keep your feed updated to get more applicants on your website and also to drive conversions. But what are your options here?

Paid Web Scraping Tools:

Multiple paid web scraping tools are present in the market today. These tools are available at different prices, and some are even free, but come with limited functionalities. They usually require no coding knowledge and can be learned in a matter of days. The problem with these tools is that they all come with some constraints and in case your company needs to shift from one tool to another due to cost restraints, you will have to learn the new tool all over again.

Coding Your Solutions:

Coding your web scraping solution using an open-source language like Python which has loads of third party packages and a huge developer base, is the best idea. However, if you are starting from scratch- that is if you have no previous coding or scraping experience, the learning curve can be rather steep. Also, web scraping is something that one gets better at after scraping hundreds of different types of websites. Scraping data from a single job portal may be a very different task compared to scraping data from ten, since all ten may come with different user interfaces, some may allow you to access data once you log in, and some may even make you solve a captcha. 

DaaS Providers:

This is the last and easiest solution for companies that want to set up shop fast, and need their data in a plug and play format, so there is no delay in the business. Our team at PromptCloud provides a fully automated job feed for your business through our tool called JobsPikr. Such an automated tool would mean all you need to provide are your requirements and you will be able to use the data feed that is shared with you for your business. When using a DaaS like ours you need not worry about a learning curve or a separate team for infra and maintenance. You give the requirements and you get the data- that is how easy DaaS makes scraping data from Job Portals.

Scraping Data From Job Portal Like Indeed?

But suppose you want to scrape data yourself for a DIY project. How easy or difficult would it be to say scrape data from a website like Indeed? Well, you can refer to the code below.

We are using our usual combination of Request and BeautifulSoup, to capture the HTML content and then convert it to a BeautifulSoup object to parse through it easily and extract the data points. 

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23Scraping Data From Job PortalWhen you run the code, you will be asked 3 questions, to which we provided these answers as you can see below-
Scraping Data From Job Portal

Once the code execution is complete. It will ask you to check the JSON file that it has produced as output. The JSON will contain a few job listings based on the values that you provided earlier. Our values produced a lot of job listings. But we truncated it to just 3 to show you how it worked. 

Scraping Data From Job PortalScraping Data From Job PortalIf you go through the output JSON, you can see that there is a block for each job listing, and each of these blocks contains certain data points. The data points that we have extracted are-

 

Of these, we have stripped the details section to just the first 100 characters, but based on your usage you can extract the entire details or some other specific number of characters or words. 

Conclusion:

We showed you the code to scrape data from a popular job portal, and it sure is not easy. We did not handle cases where the code might break, or where it might be blocked by the website. When you are scraping 10-15 different job portals. You need to handle edge-case scenarios for all of them and also maintain your code and make updates. It is based on updates to the UI of all the websites. Quick updates are a must to reduce data-downtime. If you look at all the factors at hand. This is not an easy task, and unless you have a full-blown web scraping team at your disposal. You should leave such a task to a team of professionals like ours at JobsPikr.

Reasons To Start Scraping Data From Company Career Pages

Introduction To Scrape Data From Company Career Pages:

Scraping data from the web is not new. And whether you are using automated web scrapers, DaaS providers, DIY code, or the plain old copy-paste. Every industry is scraping data from the web. But today, due to the global Covid-19 pandemic. There has been a growing percentage of unemployment. And more people are on the lookout for jobs than ever before in recent history.

Across the globe, multiple industries like hospitality and tourism have taken a big hit. Few like airlines are still in the recovery stage, and with “good-times” nowhere in sight. People have hit the streets to apply in sectors like healthcare, delivery-management, and logistics which have seen growth or are slowly trudging back to normal. 

Many big companies are also on the lookout for bright talent that is currently being exposed to the market due to multiple small and promising startups shutting shops. Now the only way these two parties can be connected is through data. This is where you can come in. You can play a vital role today to help both recruiters and applicants during these trying times.

Scraping Data

Why Scrape Job Data?

Scraping job data can help you build a business on its own. Whether you want to build a job consultancy service or create an online job portal of your own, you can cover quite a few miles using job data. You can build intelligent systems to match job applicants to job posts, or just build a portal that will help filter down jobs in an easier fashion – the possibilities of services that can be built on top of job data is limitless. 

Most job boards scrape data from other sources and have tie-ups with companies that hire through them. This is how websites can add fresh job listings every single day.  Some also allow individuals (usual recruiters of companies) to post job postings, for a small fee. One website where you can see such plans would be Linkedin

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Where Should You Scrape Data From?

Deciding where to scrape data from is a tough job in itself, but to break it down in simple terms, you could say that the Job Boards and Career Section of Companies together cover almost all job listings on the internet. Of course, today smaller companies even hire people through social media, but that is usually for regional or local hirings and limited to some specific job-profiles.

When scraping job data, the best place to start is from job boards and job-aggregators. You can get loads of job listings by targeting just the top ten job boards in each region, and some of the top ones worldwide. Web pages across a single job board are usually similar, so one can use the same code to scrape multiple job listings from a single website, but you will need to handle different job boards since no two job boards will have the same webpage format.

Another thing to remember is that many job boards will show you their listings only when you create an account and login to their website. Now in such a scenario, when you are logged in, you are agreeing to certain terms and conditions. Scraping data that sits behind a login page may land you in trouble. You may also need to check the robot.txt file for each job-board to make sure that they permit web scraping.

Job

When it comes to company career pages, you may need to set up scraping for separate websites- probably more than hundreds of them. This is because the website of every company’s career page will vary. Also, you will need to scrape the top companies in the sectors you want to target, based on regions. Or if you are going after all types of jobs, then you will need to scrape career sections of almost all the Fortune 500 companies. This big task completed gradually with time. You might also need to take into account booming startups that are hiring aggressively after being able to secure funding. For capturing such information you will need to scrape data from websites that provide the latest news related to startups.

Jobs

How Do You Scrape Data?

Once you have your goals set. You have to decide how you want to scrape the data. How to process it, and how to plug it into your business’ use case. While you could use a graphical scraping tool. It is unlikely that it will be flexible enough that you can use it on hundreds of websites without a massive amount of effort spent on understanding the software.

On top of this, using proprietary software would mean depending on a third party company for updates, bug fixes, and more. It is recommended that such a large scale solution be built using an open-source language like Python where third-party libraries are easily available (such as BeautifulSoup) that allow simplified scraping of websites, and yet keep space for infinite customizations based on the website that you are scraping. 

Data

What Do You Do With So Much Data?

When you are scraping such massive amounts of job-data from different websites. You can create a live job feed on your website, and use it to get customers. But the true secrets lie deep within the data. You can put this hoard of data to good use through Data Analytics and Data Sciences. We can create graphs that show market trends related to hiring in different sectors of the industry. You can track how many candidates who use your website to find jobs land one. And further use that data to decide how to list jobs on your website or which ones to list first. 

Job Data

Conclusion

While we did mention the two ways in which you can start scraping job data from the web and enrich your website or research and recommended the latter. Something to remember is that a scraping task like this, that needs to run on so many websites. It needs to run at periodic intervals to keep the data stream healthy and refreshed. Needs expensive infrastructure, maintenance, and most importantly, a team to build and look after.

Our intelligent job data delivery tool JobsPikr does the same job, without the need for a separate web scraping or infrastructure team at your end. Your business team or research team can directly consume the data and put it to its end-use. 

How Does Data Science And Analytics Help Job Portals?

Introduction To Data Science In Job Portals:

What can be the common factor between an award-winning journalist, a photo-editor, the CEO of a fast-food chain, and an investment banker? Any guesses? If you guessed data, then you are correct. Every sector has seen an increase in decisions backed by data. This is beneficial on multiple counts. There are lesser chances of getting things wrong- as long as you are using correct data and drawing correct analysis. You will no more need to depend on using random guesses or polls to decide on solutions to your business problems. 

Data Science and Analytics has helped companies use the massive amount of data generated within their companies along with that from external sources like the web. When it comes to Job Portals and Job Boards, most of their data sources are external, and hence require a lot of cleaning and sorting of data so that users have an easier time using the data, analyzing it and eventually, running models on them.

Data Science

Data Science Vs Data Analytics:

First and foremost, we must understand the difference between these terms, since they are often used interchangeably (and incorrectly). Since we will be discussing how both benefit Job Portals, it is important to have a basic understanding of both. Data Science is an umbrella term for all the fields used to mine information from large data sets. Data Analytics is a more focused subset, that mostly deals with actionable items applied or implemented into business processes very easily.

Another massive difference lies in the degree of exploration. Data Science is mostly the parsing of huge datasets, using common methodologies in hope of exposing certain metrics and insights. It gives you a broad perspective of the data at hand. On the other hand, data analytics used to answer specific questions, and draw conclusions that reveal hidden gems in the data. Let me explain this with some data. Say you scrape job listings of a certain sector from a website like Indeed.

Now you are with millions of job listings of a certain region and sector. You could explore the data and come up with insights such as – people with greater than 5 years of work experience can expect double the pay, or find an outlier – a company that pays a lot more than its competitors because it wants the best talent. All this would fall under data science. But suppose you want to know which perks are most common among companies in the sector and want to stack them up by frequency in which they are listed in the job postings that you scraped. Now this will fall under data analytics- because you are trying to find the answer to a very specific question. 

Data Science

What Can Data Science And Analytics Do?

When it comes to job data- a lot. Today, job data scraped by anyone with a good scraping engine and time. New regional and niche job boards seen every day, and they aim to cater to a smaller crowd but in a more personalized manner. What they miss out is doing more with the data in hand. Just presenting the data in an infinite scroll with a few regular filters may not be enough in today’s world. Data Science and Analytics can set a Job Portal apart when used correctly.

 

There Are Multiple Applications, But Some Would Be:

Matching job profiles or job listings to applicants is something recruiters try to do manually over hours. However, data science and machine learning models built on top of the data can help solve this problem in minutes.

The extent of the correctness of results will depend upon how well you have analyzed the data and separate data points that are key to identifying a suitable candidate. The process can help at both ends of the spectrum. You can offer this service to companies who need to filter out candidates or find the best candidates for a post, or even to job applicants who want to know which job applications they are the best fit for. This is probably the most complicated solution that job portals can use on top of their data but also one that is most desirable and can set you a mile ahead of your competition.

HR

 

What are the filters that you usually see in job posts? Filter by location? Filter by position? Salary? Well, what about filtering job posts for Software Engineers where the programming languages mentioned are Java and Python? Or what about a job post where the keyword “marketing” should be there but not “sales”? Such custom filters are not easy to build. But are important for a better analysis of the data and also for job seekers to get their desired job more easily. Deciding on which filters to build can be done based on tracking search patterns and other data generated by users on your job board.

HR

While this may not be specifically helpful in applying to jobs. Such data insights can draw a greater and varied crowd to your job board. These insights can range from – “289 job posts for Data Engineer posted on our website today.” to “76 candidates who applied for the post of HR manager at Xyz Inc. are selected.”

Insights

When you are applying for a job, one of the first questions that come to your mind is- “Where do I stand in the crowd?” Thankfully, in today’s world, data answers these questions and more. Websites like Linkedin are already providing deeper insights when applying to jobs for individuals with the paid “premium” plan. These insights can help candidates get certifications or train in technologies that they might need to get a job. Say a software engineer finds that a programming language is known by most of his competitors when he applies to different jobs. He will likely learn the language or do a MOOC, and then apply to jobs with his newly updated credentials. 

Data Analytics Recruitment

 

Say a candidate who comes across your job board is not sure of the salary that he should expect with his given experience. Or he is unsure whether to apply for the role of an associate professor or a senior professor. Using data at your disposal, you can help candidates get the answer to such questions using custom queries. 

Big Data Recruitment

Conclusion:

Today data is the key to everything- be it unlocking market secrets. Or finding the secret sauce to making your business a success. While certain sectors of businesses are still stuck to the brick and mortar format. Job hunting moved to the web a long time back. Whether you are getting job listings from companies or scraping them from websites. Data can help build more services on top, and that is the only way to further innovate in the field of job-search. Our team at PromptCloud helps job portals and online job boards through an automated job discovery tool called JobsPikr. It not only provides you with a way to get job posts in an automated plug-and-play format. But also streamlines the flow of data through the use of custom filters and preprocessing. 

Job Data Scraping: A Guide To All Things Job Data

Why Job Data Scraping?

Throughout the years of being within the web scraping industry, job data scraping stands out as being one among the foremost wanted information online. As per a survey, 51% of employed adults who check out new jobs are trying to find new work opportunities, and 58% of job seekers search for jobs online, in another word, this market is large. This data can be helpful in many ways like:

  1. Collecting data for analyzing job trends and therefore the market.
  2. For fueling job aggregator sites with fresh job data.
  3. Staffing agencies scrape job boards to keep job databases up-to-date.
  4. Tracking competitor’s open positions, compensations, benefits decide to get yourself a leg up the competition.
  5. Finding leads by pitching your service to companies that are hiring for an equivalent.

And trust me, these are only the tip of the iceberg. With that being said, scraping job postings isn’t the simplest thing to do. 

Job Data Scraping

Challenges For Scraping Job Postings:

First and foremost, you will need to decide on the source to extract this information. There are two main sorts of sources for job data:

Next, you will need a web scraper for any of the websites mentioned above. Large job portals are often extremely tricky to scrape because they’re going to nearly always implement anti-scraping techniques to stop scraping bots from collecting information off of them. A number of the more common blocks include IP blocks, tracking for suspicious browsing activities, honeypot traps, or using Captcha to stop excessive page visits. On the other hand, the company’s career sections are usually easier to scrape. Yet, as each company has its web interface/website, it requires fixing a crawler for every company separately. Such that, not only is the upfront cost is high but it’s also challenging to take care of the crawlers as websites change very often. 

Job Data Scraping

What Are The Choices For Job Data Scraping?

There are a couple of options for a way you’ll scrape job listings online. 

  1. Hiring A Job Data Scraping Service:

These companies provide what’s generally referred to as “managed service”. Some well-known web scraping vendors are Jobspikr, PromptCloud, Datahen, Propellum, Data Hero, Scrapinghub, etc. they’re going to take your requests in and find out whatever is required to urge to get the work done, like the scripts, the servers, the IP proxies, etc. Data is going to be provided to you within the format and frequencies required. Scraping services usually charge based on the number of internet sites, the quantity of knowledge to fetch, and therefore the frequencies of the crawl. Some companies may charge additional for the number of knowledge fields and data storage. Website complexity is, of course, a serious factor that would have affected the ultimate price. For each website setup, there’s usually a one-off setup fee and monthly maintenance fee. 

Pros:

Cons:

Web Scraping Services

  1. In-house Web Scraping Setup:

Doing web scraping in-house together with your tech team and resources comes with its perks and downfalls. 

Pros:

Cons:

Web Scraping Services

  1. Employing A Web Scraping Tool

Technologies are advancing like anything, web scraping can now be automated. There are many web scraping software that’s designed for non-technical people to fetch data from the internet. These so-called web scrapers or web extractors transverse the web site and capture the designated data by deciphering the HTML structure of the webpage. you will get to “tell” the scraper what you would like through “drags” and “clicks”. The program learns about what you would like through its built-in algorithm and performs the scraping automatically. Most scraping tools are often scheduled for normal extraction and integrated into your system. 

Pros:

Cons:

Web Scraper

Conclusion:

To sum up, there’s surely going to be pros and cons with any of the choices you select. the proper approach should be one that matches your specific requirements (timeline, budget, project size, etc). An answer that works well for businesses of the Fortune 500 might not work for a university student. That said, weigh in on all the pros and cons of the varied options, and most significantly, fully test the answer before committing to at least one.

Mattermost- An Alternative To Slack

Introduction To Mattermost:

Mattermost is the brand new and cool alternative used by organizations. In a world fueled by digitalization, it is of prime importance that businesses collaborate in the virtual environment as well. Slack understood the need and so, in 2013 it created a digital workspace for employees to send messages privately or in groups, share files, and integrate third-party programs. It entered the Software-as-a-Service (SaaS) world as a platform where developers, corporate employees, and gamers can communicate with each other and in 2019, reported 10 million daily active users.

The strongest feature of the app is its extensive third-party app directory with more than 2000 integrations. However, even with the Enterprise Grid, it does not tackle all problems in the workplace which is why Mattermost became a strong competitor. Mattermost is an open-source, private-cloud messaging platform designed to keep the needs of the enterprises in mind and in many ways, is superior to Slack. According to CEO Ian Tien, Mattermost is a creation “by developers for developers.”    

Mattermost

 

 

Slack:

Slack, which is the shortened version for Searchable Log of All Conversation and Knowledge is a cloud-based, SaaS messaging system made to maintain online workplace communication.  It has many redeeming qualities, some of them being maintaining an archive of all conversations that can be searched later, sharing files, integrating apps, having an open API for developers to build new apps for it. Plans cater to both teams and enterprises. It is a good platform for businesses that are looking for a more streamlined workflow and improved communication amongst team members, and do not have any ownership, privacy, or security concerns. It is also ideal for companies that wish to scale the messaging solution or integrate it into their existing systems. A business should choose Slack if they have the following things in mind:

Slack

Mattermost:

An open-source solution, Mattermost acknowledges the organizational problems that most businesses face with workspace communication tools when it comes to the issues of security, privacy, scalability, and legal consent. The practical approach of the messaging platform has secured for itself funding of $50 million to create more plugins and integrations. 

It has two versions-

It is a private-cloud workspace solution designed for users with a non-technical background but with the necessary IT skills. It equips on your desktop and as well as mobile devices and brings workplace communication into a single environment. Mattermost is also equipped with instant search, robust archiving, and a selection of third-party integrations. 

It has all the functionalities of the Team Edition with a few added features such as advanced security and higher-grade messaging. It also comes with options for scalability tools for users.

Mattermost

 Mattermost Deployment:

It can deploy in private, public, and in hybrid clouds and is the optimal choice for businesses that require more from their messaging solutions. You can install it behind your firewall in a trustworthy environment with security parameters. This will ensure that SaaS services cannot learn about your business matters or activities.

This setup further ensures compliance with laws such as consumer data protection and GDPR. These are the reasons why Tesla Motors decided to replace Slack with Mattermost for its team of developers; it comes equipped with more privacy, enterprise licensing, and private cloud security.

Since enterprises can access the source code, APIs, drivers, open-source integrations, samples, and more, they control the customization of the platform. The single-tenant private cloud architecture also offers horizontal scaling and high availability. Uber needed a better messaging solution that could evolve along with the growing company.

After testing the available options, Uber chose Mattermost due to its scalability, redundancy, privacy, and enterprise licensing. Uber created around 20,000 chatrooms that had the strength to support conversations between individual employees and between groups. The new and improved solution supports 32,000 users and handles around 700,000 messages every day.

Wargaming, chose Mattermost as an on-premises solution for workplace communication that can serve more than 180 million players at once. Its highly-available and scalable architecture has allowed Wargaming to grow and evolve. You should choose Mattermost if you are looking for:

Mattermost

Conclusion:

The messaging solution that is perfect for your business depends on your needs and preferences. When it comes to productivity, Slack is the clear superior as its hosts over 2000 integrations while Mattermost only has a few hundred.

Some small-scale businesses may find Mattermost suitable due to its out-of-the-box approach towards messaging. Others may find the Enterprise Grid more accessible. Whatever your preferences, if there are any concerns about content ownership, security, privacy, and want scalability as you evolve, then Mattermost is the one for you. 

At Jobspikr and PromptCloud, we use certain plugins that allow for better workspace collaboration, namely, Standup Raven and BBB. Standup Raven, a Mattermost plugin used to track the daily activity of employees. Big Blue Button or BBB is an enterprise plugin used for video conferencing and audio conferencing. It allows you to add multiple people to the call and also has share screen options that allow you to show people your screen, you can even record the calls for later reference.