Dress code; the dos and don’ts of workplace dress code has reversed in the decade. While more than half of the global workplaces are following the formal dress code, the new age companies and start-ups have a more relaed approach. Today, we have CEOs of the big billion digital empires having press conferences in plain tees! The lines of power dressing has blurred, yet there are unspoken rules on what and what not to wear.
What Is Power DressingAnd It’sImportance
Power dressing is all about smart dressing, and only smart dressing. Well, the right dressing helps you create the first impression and can even get you ahead in work. It is the window to your personality and contributes towards how others (external or colleagues) perceive you. The corporate dress codes are categorized into 4 groups: Business Professional, Business Formal, Business Casual, and then at last Casuals. Here I’m listing a few of the power dressing tips for women and men:
When planning for ‘what to wear for work’ consider these three things — company, your industry, and the work environment. People who work in law firms or financial institutes do need to dress more traditional compare to an individual who is working for a creative agency such as writers or artists. Generally, you will never go wrong on your choice to go with a beautifully pressed suit, but there is no denying that it’s not fit for a creative industry. If your job profile doesn’t require you to meet clients, then also you should dress appropriately to make an impression amongst your team.
Dress As If You Care
For instance, even if your workplace doesn’t have any strict rules for the dress code, you should always portrait approachability. If your company is open to casual dressing, make sure that your weekend casuals are not your company casuals. Pyjamas can make an interesting airport look, but are complete no-no for office.
Dress for The Occasion
It’ important to understand the purpose of your outfit. Beach wears are only for the beaches, formal outfits can be for both at the formal gatherings or the weddings. Don’t carry them as to make it a substitute. While going on a company trip or attending a formal gathering, know the meeting agenda in advance; and dress for the occasion.
Always make it a mandatory point to be well-groomed, whether it’s a casual or business dress code. Cleanliness goes a long way. Keep your nails clean, hair neat and combed. Avoid wearing wrinkled clothes. Clothing with inappropriate or offensive terms or words written on your shirt or t-shirt, is not at all suitable to a work environment. To generalize, don’t be a homeless magician.
Maintain Your Style
Whatever is your style statement, be consistent. You know what suits you, do not try to experiment way out of your comfort zone. Are you a traditional person or modern dresser? Trendy or all-time? Vibrant colours or pastel shades? Whatever your style is, carry it with elegance and stick to it. Embrace your personality, do not try hard to dress up as someone else.
Know The Importance of Fit
When you are dressing for work, always stick to the right fit. It is the thumb rule of buying new clothes. Always remember, the costliest suit out there will not look good on you if it does not fit you well. A perfect fit cloth would blend in smoothly and draw attention to portions you like to highlight, as your neck, shoulders, or face. Simultaneously, it should not draw attention to the parts which you like to hide. Consult with a tailor if required to find out your measurements of your body.
Avoid Exposing Too Much Skin
A lower neckline can draw attention, especially if you are a woman. Apart from the fact that it is inappropriate, it also distracts the business environment. Studies have revealed that showing skin in the work environment has a psychological impact on colleagues. Women who dress in this manner are wrongly perceived as being less competent. Females who dress more competently get an appraisal compared to those who do not.
Have confidence in Your Power Dressing
Confidence and smile can make all the difference in your power dressing. A simple dress, worn with confidence will always create a stronger impression than an expensive suit on a timid person.
If you are looking for data extraction from a fashion site or e-commerce site you can opt for web scraping service.
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Performance Management Of Employees In The Pandemic
Performance management often confused with the annual appraisals. End-of-the-year appraisals are directly tied to variable pay. And more often than not, increments do very little to judge the performance of individuals. It does not help them understand their strengths and weaknesses or improvement areas either. Employee performance management is not a simple affair. If you do not get it right, employees might feel dissociated with the work and your attrition rates could go up.
It is important that you maintain a constant communication bridge with every employee and update them about their work outputs and performance at regular intervals. This helps employees improve their performance and in the pandemic time, even save their jobs. In the COVID-19’s volatile atmosphere, it is absolutely necessary to set the expectations and share regular employee performance’s feedback, to help save jobs and costs to the organization.
What Are the Stages of Performance Management?
If you want to roll out new performance management metrics and processes without employee consideration and planning, you might face resistance to the new workflows. It is beneficial to connect both managers and stakeholders so that they are more involved in the process.
Planning: Open communication between the reporting manager and the employee is the key to planning. Clear objectives, SMART goals, and expectations setting are the focus in this stage.
Execution: Empowering the employee to achieve the set results, and help them execute and achieve the goals is crucial. With the right planning and execution, you can create a sense of awareness and address all the employee pain-points.
Continuous Tracking of Metrics: Regular sync-ups, monthly updates, one-on-one meetings can help keep the reporting managers and the employees on track. Clear performance metrics need establishing, followed, and tracked.
Review and Feedback Cycles: Even if you set up an exemplary performance management system at your company, you cannot expect it to be relevant forever. Hence, it is important to take continuous feedback from all the stakeholders about how comfortable they are with the current system, what changes they would prefer, and more. These changes need integration into the system based on priority.
Steps to Better Performance Management
Here are some procedures that companies should follow to design a better performance management system:
Recognition of Employees: Employees perform better when their efforts recognized. Whether it is a crucial project, extra working hours, or extra responsibilities, a performance review should reflect and report these.
Transparent Feedback and Communication: HR processes related to employee appraisals. Increments and promotions have to be structured at every level and department. Any change in the process should openly be communicated to all. Regular feedback and metrics can help make things more transparent.
Opportunities to Improve: Constructive feedback is a necessity for performance improvement. The right guidance can be the make or break factor when it comes to employee motivation.
Focus on Coaching and Not Appraisals: Performance Management could easily reduce to a mundane task. Instead, if companies can coach employees throughout the year and conduct an all-round performance review, it will be far more beneficial to all.
Feature-Based OKRs: Create feature-based OKRs so that employees have better focus. OKRs given to employees early on and could contain both targets as well as projects.
Opportunity to Choose Goals: At times, one may want to switch tracks or move to a different project. Employees allowed to create their own goals that help employees gain some personal growth and fulfillment.
Discuss the Quality of Work, Not Hours: In many organizations, it is the norm for bosses to count the hours that an employee worked for. It is imperative that this practice phased out and work quality gets discussed and debated. Not the number of hours one puts in.
Performance Management in the time of COVID19
COVID19 has made most employees work from home to maintain social distancing practices to slow down the spread of the virus. In times like these micromanagement and counting the hours of login are neither recommended nor feasible. Instead, you can track the quality of work and focus on work completion, employee responsibility. Appreciating those who take on the responsibility of taking the extra leap to make sure that the team continues to work together towards a common goal is a must. While this may be a down-time for many things, when it comes to performance management techniques, this is the best time for it to evolve.
How Does It All Add Up?
If I had to sum up the biggest benefit of putting in all these efforts, I could do it in just two words — “Lesser attritions”. Today, organizations are adopting an employee-first approach. If your employee’s taken care of, they will surely take care of the customers they have. Employees who feel valued will value the work they do and the company as well. Loyal, long-standing employees will grow to take up leadership positions at your company. The result, lesser attrition, and better productivity.
We at JobsPikr and PromptCloud have adopted a completely remote policy. We are using a variety of tools to conduct daily catch-ups. It used to track all our OKRs, measure employee performances, and keep employees engaged. For more details, read here.
Scrape Job Postings From Internal And External Sources
Most recruitment agencies and online job boards scrape job postings from multiple sources, aggregate the job data, and provide candidates with a broader list of opportunities to explore. The internal job postings include career pages of the company website. While the external job postings include any other source apart from the company — job boards, job listing sites, and more.
The Difference Between Internal and External Job Postings
Apart from the fundamental difference of the job source, there lie more differences between internal and external job postings.
Many times, external job postings cater to niche sectors such as Aviation, Technology, and Startups these days
Job board listings follow a specific format. For example, if you open two job posts, it is likely to have the same data fields or key-value pairs. Different data points may be missing for different posts but the total number of data points is likely to be the same
External job postings usually give various sorting options, selections, and search filters, so that you do not need to go through hundreds of job listings before finding the ones relevant to you. However, not all websites provide the same filters and every website has different ways of making the search process easier
External job posts usually have links to the actual job post on a company website, so you can use these links to get more information as well
Scrape Job Postings From Company Websites
As for company job posts, the variations should be far too less but that generally, is not the case. Some well-established companies have proper career pages with different sections like Technology, Product, Marketing, and Human Resources. Then, specific jobs are listed under these sections having the job role, description, location, good to have, salary, benefits, and more. If the company is a multinational entity and hires across different regions. Then you might need to select a specific country or city before being able to view the job listings. However, these are the best-case scenarios where the data is presented just like it is in job websites; the only difference being that all the job posts are from a single company.
On the other end of the spectrum are startups and other companies that do not hire regularly. These companies usually do not have a specific career page or even if they do. The page itself is not well maintained. They usually ask candidates to send their resumes to an HR email address to apply for possible open positions. Others have a generic job description and the type of work undertaken for every department. But do not have different job descriptions for specific job titles. This makes it difficult to gather information about job openings for these companies.
How to Scrape Internal andExternal Job Data?
When it comes to scraping job posts, external job listings are much easier to tackle. Let me explain why. When you train your scraping engine to scrape data points from a single job post from a job board. You can use the same code to scrape hundreds or even thousands of other job posts. All this on the same job board. What you will need to handle programmatically is how to parse through the webpages. You can automatically go from one job post to another. Scraping web pages in parallel to reduce the time taken for the process can be another challenge.
While tackling these common issues related to scraping job listings from external websites. You also need to make sure that the site does not block you due to multiple requests coming from the same IP together. This can be handled through IP rotation and VPNs.Another benefit of scraping from external job websites is that if you want to scrape jobs using a specific filter, or keywords, you can do that by using the filters present on the webpages.
As for different company websites. You will need to create a long list of companies (you can start with the Fortune 500). Listing the company careers pages and manually checking if they have a separate career or job listing page may be a laborious task. Scraping job postings from every single webpage may take even more time.
The reason behind this is that you will need to analyze the webpages in every single company — website that you need to scrape before you can get job listings from each. Also, every website will probably end up giving you a few job posts only. The time spent analyzing each website will not seem to be very rewarding. But doing this is important since job applicants will want to view job posts from top companies.
How To Go About Scraping Job Data?
If you are planning to analyze and scrape both internal and external job data, you must have data analysts and software engineers with web-scraping experience. Starting from scratch will not be easy, but if you have an experienced team, you can still go for it. But if you need a quick solution and do not want to take up the hassle of building and maintaining the solution and taking care of the cloud infrastructure required. The best thing for you would be a DaaS (Data as a Service) solution specialist like PromptCloud.
Our team at PromptCloud has come up with an automated job discovery solution called JobsPikr that makes it much easier for job agencies to scrape job postings from multiple sources. You can enhance your hiring platform using data from JobsPikr to benefit both your clients and applicants, and generate a higher revenue stream.
Data Scientist: The Most Popular Job of the 21st Century
Data Scientist touted to be “The Sexiest Job of the 21st Century” in 2012 by The Harvard Business Review, a little more than 10 years after the term coined in 2001! Come 2020, and we are facing a shortage of anywhere between 200,000-350,000 data scientists all over the world.
So, what exactly is a data scientist? What does he/she do? Do they have to be a real scientist with a Ph.D. degree? Well, let’s find out.
A data-scientist does not require formal college degree, rather the right set of skillsets. Based on the size of a company he/she may also take up the responsibilities of a data engineer, typically here are some of the must-haves:
Coding and scripting knowledge
A deep understanding of fundamental and advanced statistics
Calculus and algebra
Data visualization and communication
Who Is A Data Scientist?
A company may require managers to plan products. Engineers to build them and marketing and advertising personnel to spread the word. You may also have other departments like operation and logistics based on the requirement. A data science team or a Data Scientist is not there to run your business. So why are they important? The answer is, to grow your business.
In the earlier days, every department used to conduct its work and, in the process, generate a lot of data. Unfortunately, this data was never passed on to other departments to take a look into. For example, say the marketing department receives negative feedback in its survey sheet regarding deliveries (which is not directly linked to the product). This information needs to reach the operations team but does not because of the lack of a data flow process.
A Data Scientist, if present, would analyze the structured and unstructured data produced by different departments of your company. They then enhance this data using information gathered from external sources and then build data models and make predictions and find hidden trends. These would then communicated to the entire company so that each department can use the information relevant to them to drive the product ahead.
Why Do You Need A Data Scientist?
Companies fetch data from multiple sources today. Data can be in the form of internal data generated by different teams based on their interactions with users. It can be system data — log files generated by the system that captures the activity of different users on your website. It can also be external data— competitor pricing data gathered through web-scraping. Data can also capture from various devices (using the Internet of Things) and appliances. Today almost anything you buy. Be it a fridge or a watch connected to the internet (read IoT). All this data needs cleaning, analyzing, and conversion to a format understood and absorbed by the business team. All of this taken care of by the Data Scientist. They not only take care of all the data sources at hand and communicate findings to all but also look into other sources to boost business and increase productivity.
There was a time when data meant excel-sheets. It’s consumed easily by the business team easily. Today, data has boomed and a very small percentage of that data is in a structured tabular format. Big data exists in semi-structured formats like JSON on XML and even in unstructured formats such as images, pdfs, videos, audio recordings, or plain text. While “plain text” may seem to be a good data source, breaking it into pieces and extracting information from it is a subject in itself, called NLP or natural language processing.
Understanding The Role Of A Data Scientist
Data science is the understanding of what a text means using code. Its knowledge is essential for data scientists who work on textual data. One of the common sources of data with all formats is social media — texts, images, videos, and more, all sitting together. With the new and varied data types that are not analyzed manually just by looking at the data. The business team is in a fix. The Data Scientist enables the business team to understand the data by converting it into a simpler format, breaking it down to understandable bits.
A pile of data is of no use unless worked upon. A Data Scientist uses the various tools at his/her disposal to understand the data first, and then uses data wrangling techniques to convert it to a workable format. Using this converted data, they usually create predictive-models. These models help the business team weigh different metrics against each other and find the optimum way to maximize business gains.
The Demand For Data Scientists?
The average base salary for a Data Scientist is $113000/year. And yes, that figure is higher than a Data Analyst or a Software Engineer. The reason, they are highly in demand. A Data Scientist needs to work on different stacks. He/ She needs to understand the questions that the business team needs answers for, from the data. He/ She needs to know coding and statistics to handle the data. And should have the infrastructure know how to make decisions based on which cloud infra to run an algorithm on. They also need to have visualization skills to know which is the best way to present the data and findings to the wider audience.
Since it is a highly specialized job title that requires a diverse skill set. It is understandable that the market has always faced a crunch of candidates. While the market has automated tools options, we still need data-scientists to use them. Today, the Fortune 500 companies have scooped up a large percentage of Data Scientists. The remaining have gone to well-funded startups. For the rest of the companies, there are a few fish in the pond and too many fishermen.
The Impact of COVID19
The current generations (Gen X, Y, and Z) have never experienced a pandemic before. And understandably we have no usable data from a previous one. This leads to an increase in the demand for Data Scientists. There is a requirement to simulate the circumstances and deduce consumer demand. Uncertain behavior, like high sale of paper tissues, can also lead to a decrease in sales later on, when everyone has a stockpile. Thus, market data needs analyzing in real-time today to best predict what tomorrow will bring. Even the spread of the virus itself being analyzed by Data Scientists to understand how fast-spreading and what are the means spreading through.
How Can You Become A Data Scientist?
A large percentage of data scientists who are working on data today were Software Engineers or Data Analysts, who learned the extra skills necessary through online courses or MOOCs on websites like Coursera and Udacity. There’s also a dedicated website, Kaggle, which has developed into the world’s large platform for Data Scientists and Machine Learning Engineers. Companies like Google and Lyft host competitions on the platform, where the team with the most optimized solution for a machine learning problem wins. There are also discussion boards, job-board, and more. All the resources that you may need are already there; you just need to take the leap.
JobsPikr is your-go-to-source for the job market insights. It is a customizable job feed and analytics solution that has the widest range of historical and active jobs from the job market across the globe. If you liked the content above leave your valuable feedback in the comments section below.
Scraping Indeed Job Data, Using Python
Indeed is one of the most popular job websites in the market today. It is a job aggregating website available in 60+ countries and covers multiple job boards, staffing firms, and company career pages. Scraping job sites like Indeed can help you access the latest job data, analyze job trends, and automate job boards. Indeed allows you to search job-based on location and keywords. These keywords can be a job title, skills, or any search term in the job listing. We will be using these two search boxes along with the number of pages of search results to crawl Indeed and extract the data.
Where is the Code for Indeed Job Scraping?
First, you need to have the requirements installed to begin the job scraping from Indeed. These are Python3.7 or higher, BeautifulSoup, and a code editor. Once that is done you can save the code below to a file with the “.py “ extension and run it. But before we go into running the code, let us first understand the code itself.
It is the “main” method, where the execution starts. We take three inputs from the user —
name of the city for which he or she wants job listings, keyword, and the number of pages of search results that are desired. Once we have these data points, we create the URL that needs to be hit for getting the search results. The “scrape_data” function is called next, which loops over the number of pages of search results that we want and calls the “get_data_from_webpage” function to extract job data from Indeed’s webpages.
In the “get_data_from_webpage” function, we extract the data for all job posts on a single webpage by looping over all the job posts on a single webpage of search results. We also strip the job post content to just the first 100 characters. You can change that piece of code so that you can get the required data at hand. In turn, the “extract_data_points” function called for every job post on a single page. It captures various data points by going into the specific job post links on Indeed. It captured the HTML data and converts it into a BeautifulSoup object, which is then parsed.
In simple terms, there are three levels of web data scraping on Indeed for job posts:
We loop through the n pages of search results
Then we loop through all the job posts in a single web page
We scrape the data for a single webpage by going to its link
Once the code runs on the number of pages we selected, we get an array of dicts where each dict contains the data of a single job post. We tested this code using these following values that you can see below-
The Output Of Job Scraping On Indeed
For the input data that we showed above. The below JSON is what was received as a result. You can see that there are just three job posts. But that is because we truncated the list to fit the blog. In reality, we scraped around seven job posts for the given search terms on page 1 of the search results. The data points that we captured for each job post are:
Name of the Company
The Date posted
All the data points are self-explanatory. We specifically captured these because we believe these are most important for job applicants and job analysts.
Certain data points like salaries may seem to be missing. The reason is that a large number of companies did not have the salary in the job posts and those who have it, it is in their job details itself.
Can This Work at An Enterprise Level?
This is a DIY code and cannot run at an enterprise level, that needs Indeed crawling and the job data scraped 24×7. The site will block you, the code is likely to break at some job listing with a different format, and more issues that can plague your production system.
For enterprise requirements, we have a professional job scraping solution in JobsPikr. We can automate job scraping and delivery to help you in your efforts at building a job board or in conducting research using job data.