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	<title>People Decode</title>
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	<description>Strategic Staffing &#38; Training</description>
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	<title>People Decode</title>
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		<title>Why Technical Skills Training Is the Best Investment for an Organization</title>
		<link>https://peopledecode.com/why-technical-skills-training-is-the-best-investment-for-an-organization/?utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=why-technical-skills-training-is-the-best-investment-for-an-organization</link>
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		<dc:creator><![CDATA[master]]></dc:creator>
		<pubDate>Tue, 01 Nov 2022 09:44:19 +0000</pubDate>
				<category><![CDATA[Training]]></category>
		<guid isPermaLink="false">https://peopledecode.com/?p=11603</guid>

					<description><![CDATA[Getting employees exposed to relevant and consistent training can help companies improve performance and increase results in the workplace.&#160;Training can therefore address an identified problem area and work toward a solution. It prepares employees for higher responsibilities. Not only will employees who have participated in training and development programs be more confident in their knowledge [&#8230;]]]></description>
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<p>Getting employees exposed to relevant and consistent training can help companies improve performance and increase results in the workplace.&nbsp;Training can therefore address an identified problem area and work toward a solution. It prepares employees for higher responsibilities.</p>



<p>Not only will employees who have participated in training and development programs be more confident in their knowledge and abilities, but so will their managers. This confidence fosters a sense of enthusiasm and accountability for work results.</p>



<p>Importance of Training</p>



<p>Training is crucial for organizational development and success. It is fruitful for both employers and employees of an organization. An employee will become more efficient and productive if he is trained well.</p>



<p>The benefits of training</p>



<ol class="wp-block-list" type="1"><li><strong>Execute business plans more quickly.<br></strong>Giving everyone in your company a foundation in digital literacy and technical skills puts everyone on the same page. Now your executive team can make more informed decisions, your designers and marketers can work directly with your development team, and managers can make better estimates of the time needed to complete projects. By moving as one, you can reach company goals faster.</li><li><strong>Stay on top of rapidly changing technologies.<br></strong>Your developers need to engage in ongoing learning to stay up to date with the latest tech. But what about the rest of your team? Maybe your support team doesn’t need to build an app but understanding how it’s made and recent updates can help them field customer questions. Your business can only compete in a digitally complex world if your employees are comfortable living and working in it.</li><li><strong>Provide opportunities for internal mobility and bring development in-house.<br></strong>The hardest positions to recruit for are technical – and that outlook isn’t changing anytime soon. The Bureau of Labour Statistics estimates there will be a gap between the number of open technical positions and skilled workers able to fill them of nearly 1.4 million. New hires can be risky, too – they need training, on-boarding, and adjustment periods, and even then, they may not end up being a good cultural fit. Why not create your own tech talent? TechCrunch has suggested that the&nbsp;specific technical skills and experience required by companies may need to be addressed by the companies themselves. By nurturing your own talent, you get high-skilled employees with experience specific to your needs and company, and your new developer gets a great new job without having to change companies. Plus, promoting current employees to new technical roles saves you money in recruitment costs, loss of productivity and salary, and they are already familiar with and loyal to the company. Companies that create internal hiring programs see an increase in employee career satisfaction and retention &#8211; always a good thing.</li><li><strong>Improves confidence of employees-</strong>&nbsp;Training helps the employee to get job security and job satisfaction. The more satisfied the employee is and the greater is his morale, the more he will contribute to organizational success and the lesser will be employee absenteeism and turnover.</li></ol>



<p>The&nbsp;benefits of technical skills training sound great, right? Luckily, learning platforms aimed at teaching technical skills to whole teams are now on the market. When shopping for the right platform for you, just make sure it is:</p>



<ul class="wp-block-list"><li><strong>Trackable.<br></strong>Using one system to track the progress of a set of skills is more motivating and measurable for your employees and will allow you to see the impact of your investment. Make sure you can easily download usage data and manage users.</li><li><strong>Engaging.<br></strong>Interactive content that features videos, quizzes, and elements of gamification all make sure users actually want to learn the content and aids in retention. Leader boards are also a great feature, allowing a little friendly competition to drive teams or individuals to excel in training.</li><li><strong>High quality.<br></strong>Not all content is created equal. Choose content developed by experienced, professional instructors to ensure that your team is getting the most up-to-date, easily understood lessons.</li><li><strong>Focused.<br></strong>The breadth of topics can be distracting. Give your team access to a platform designed specifically to teach technical skills &#8211; it should include intuitive interfaces, clear learning paths, and training in your company’s tech stack.</li></ul>



<p>Ultimately,&nbsp;everyone benefits from increased technical skills – businesses get more productive, multifunctional workers and employees gain marketable skills and feel more engaged. Be confident that by choosing an eLearning platform focused on technical skills training and digital literacy, you’re giving your company and workers a leg up in an increasingly technical business environment.</p>



<h2 class="wp-block-heading">Ways/Methods of Training</h2>



<ol class="wp-block-list" type="1"><li><strong>On the job training on</strong> job training methods are those which are given to the employees within the everyday working of a concern. It is a simple and cost-effective training method. The proficient, as well as semi- proficient employees, can be well trained by using such a training method. The employees are trained in the actual working scenario. The motto of such training is “learning by doing.” Instances of such on-job training methods are job rotation, coaching, temporary promotions, etc.</li><li><strong>Off-the-job training-</strong>&nbsp;Off-the-job training methods are those in which training is provided away from the actual working condition. It is generally used in the case of new employees. Instances of off-the-job training methods are workshops, seminars, conferences, etc. Such a method is costly and is effective if and only if a large number of employees have to be trained within a short time period. Off-the-job training is also called vestibule training., the employees are trained in a separate area (maybe a hall, entrance, reception area, etc. known as a vestibule) where the actual working conditions are duplicated.</li></ol>



<p><strong>To summarize</strong></p>



<h5 class="wp-block-heading">1. Training and Development Increases Employee Retention Rate</h5>



<h5 class="wp-block-heading">2. Training and Development Enhance Business Growth</h5>



<h5 class="wp-block-heading">3. Training and Development Empower the Workers in an Organization</h5>



<h5 class="wp-block-heading">4. They Increase Workplace Engagement</h5>



<h5 class="wp-block-heading">5. Training and Development Create Positive Workplace Relationships</h5>



<h5 class="wp-block-heading">6. They Improve an Organization’s Bottom Line</h5>



<h5 class="wp-block-heading">7. They Improve Customer Satisfaction</h5>



<h5 class="wp-block-heading">8. They Improve Performance</h5>



<h5 class="wp-block-heading">9. Training and Development Create a Skilled and Motivated Workforce</h5>



<h5 class="wp-block-heading">10. They Encourage the Optimum Use of Human Resources</h5>



<h5 class="wp-block-heading">11. Training and Development Reduces the attrition rate.</h5>
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			</item>
		<item>
		<title>AI and Recruiters</title>
		<link>https://peopledecode.com/ai-and-recruiters/?utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=ai-and-recruiters</link>
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		<dc:creator><![CDATA[master]]></dc:creator>
		<pubDate>Thu, 27 Oct 2022 13:09:51 +0000</pubDate>
				<category><![CDATA[HR]]></category>
		<guid isPermaLink="false">https://peopledecode.com/?p=11488</guid>

					<description><![CDATA[Hiring methods have evolved drastically in last decades. The latest addition to this has been AI (Artificial Intelligence). With AI in picture the hiring process has been faster and more accurate. Any AI tool can browse through lacks applications and map the right candidate for the required Job description, takes care of pre-screening process, Grabs [&#8230;]]]></description>
										<content:encoded><![CDATA[
<p>Hiring methods have evolved drastically in last decades. The latest addition to this has been AI (Artificial Intelligence). With AI in picture the hiring process has been faster and more accurate. Any AI tool can browse through lacks applications and map the right candidate for the required Job description, takes care of pre-screening process, Grabs the necessary initial information of the applicant. Along with Candidate Mapping, AI tools also help in smooth Interview procedure, Data management, also automate feedback sharing to the applicant.</p>



<p>With the help of AI, the time a recruiter spends in sourcing and shortlisting an application is reduced, and the Recruiter can spend more quality time with the candidate and educate them about the job and work culture. The recruiter can make sure the entire hiring process happen seamlessly.</p>



<p>AI also provides accurate data about the active hiring pool which can help the recruiters bridge the gap between them and hiring managers.</p>



<p>While AI tool provides a data centric smooth hiring process-<br>A recruiter can use this tool, to help the company hire the right candidate also create a good positive relationship between a new employee and the company</p>



<p>With AI and Recruiters going hand in hand, the Company can have a cost effective, Quality based, Short “Hiring Process”</p>
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		<item>
		<title>WHAT IS EMPLOYEE WELLNESS PROGRAM? </title>
		<link>https://peopledecode.com/what-is-employee-wellness-program/?utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=what-is-employee-wellness-program</link>
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		<dc:creator><![CDATA[People Decode]]></dc:creator>
		<pubDate>Tue, 24 May 2022 06:01:54 +0000</pubDate>
				<category><![CDATA[HR]]></category>
		<category><![CDATA[Uncategorized]]></category>
		<guid isPermaLink="false">https://peopledecode.com/?p=11150</guid>

					<description><![CDATA[Employee wellness programs are programs undertaken by an employer in order to improve employee health and also to help individual employees overcome particular health-related problems.&#160; 1. On-site fitness centres&#160; This must be considered as the most important&#160;of all corporate wellness program examples. Not everyone can provide their team members with a 72,000 square foot fitness [&#8230;]]]></description>
										<content:encoded><![CDATA[
<p>Employee wellness programs are programs undertaken by an employer in order to improve employee health and also to help individual employees overcome particular health-related problems.&nbsp;</p>



<p>1. On-site fitness centres&nbsp;</p>



<p>This must be considered as the most important&nbsp;of all corporate wellness program examples. Not everyone can provide their team members with a 72,000 square foot fitness centre, but&nbsp; its a completely justified expense for recruiting and retaining healthy team members. The fitness centre offers team members an Olympic-sized swimming pool, a rock-climbing wall, and access to personal trainers.&nbsp;</p>



<p>The fitness centre is just one part of the company&#8217;s &#8216;Living Well&#8217; program, which offers cash incentives for team members who participate and meet their goals in various areas of the program.&nbsp; it takes care about our team members&#8217; well-being and has provided them with as much support as possible to help them reach their goals.&nbsp;</p>



<p>2.Flu Shots&nbsp;</p>



<p>Flu shot clinics mainly address employees’ physical wellness. The indirect costs of lost productivity and sick leave for employers are also quite significant. Likely driven by recent flu seasons being some of the worse in decades, more companies are looking to launch flu shot clinics as part of their employee benefits. These clinics are usually one-day events with nurses coming to the office/facility and administering flu shots.&nbsp;</p>



<p>3. Lunch and healthy snacks&nbsp;</p>



<p>With only 30 minutes for lunch, many team members may feel that they have to eat fast food when buying their lunch. Companies should offers a 90-minute Fitness Lunch to their team members—&nbsp;to head home for a healthy homemade lunch. Companies&nbsp;sets the bar high with their cafeterias, offering catered lunches and snacks to team members throughout the day. Services have popped up recently that will deliver healthy snacks to your office. &nbsp;</p>



<p>4. Naps&nbsp;</p>



<p>A nap after lunch during a hectic day always leaves one feeling refreshed. For this reason, innovative companies usually have specialized nap rooms where workers who want to take a brief nap after lunch can go. Employees can return to their projects with renewed energy after catching a quick nap. Naps have been clinically proven to provide benefits in productivity.&nbsp;</p>



<p>5. Yoga classes&nbsp;</p>



<p>Companies offers their team members yoga classes twice per week. The most important requirement for practising yoga is the space to do it, and many companies are using their conference or break rooms to provide yoga classes for team members throughout the workday.&nbsp;</p>



<p>Yoga and meditation is an extremely effective stress reliever. Yoga is an effective way to incorporate self-care into work, so that team members don&#8217;t have to feel guilty about neglecting work.&nbsp;</p>



<p>Conclusion</p>



<p>A wellness program&nbsp;helps your employees learn to implement healthy choices in their lives, making them healthier in the long run. When they exercise and eat healthily regularly, they improve their health and reduce the risks of health problems&nbsp;</p>
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		<title>DIVERSITY IN HIRING &#8211; DEIB </title>
		<link>https://peopledecode.com/diversity-in-hiring-deib/?utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=diversity-in-hiring-deib</link>
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		<dc:creator><![CDATA[People Decode]]></dc:creator>
		<pubDate>Tue, 24 May 2022 05:26:42 +0000</pubDate>
				<category><![CDATA[HR]]></category>
		<guid isPermaLink="false">https://peopledecode.com/?p=11147</guid>

					<description><![CDATA[Diversity in hiring is not just bringing in people from different parts of the globe but much more than that – it needs DEIB– diversity, equity, inclusion and belonging.&#160; Diversity refers to the presence of differences within a given setting. In the workplace, that can mean differences in race, ethnicity, gender, gender identity, sexual orientation, [&#8230;]]]></description>
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<p><em>Diversity in hiring is not just bringing in people from different parts of the globe but much more than that – it needs DEIB– diversity, equity, inclusion and belonging.</em>&nbsp;</p>



<p><strong><em>Diversity</em></strong><em> refers to the presence of differences within a given setting. In the workplace, that can mean differences in race, ethnicity, gender, gender identity, sexual orientation, age and socioeconomic class.</em>&nbsp;</p>



<p><em><sup>1</sup></em><strong><em>Equity</em></strong><em> is concerned with the fairness of an organization’s practice and policies. It is not an outcome but a process focused on continuous adjust to ensuring equal access to opportunities for growth, development, and promotion.</em>&nbsp;</p>



<p><em>“</em><strong><em>belonging</em></strong><em>” into the conversation—the experience of being treated and feeling like a full member of a larger community where you can develop well. we can have diversity of representation without inclusion and inclusion without creating an environment in which everyone feels they actually belong.</em>&nbsp;</p>



<p><strong><em>Inclusion</em></strong><em> implies a cultural and environmental feeling of belonging and sense of uniqueness. It represents the extent to which employees feel valued, respected, encouraged to fully participate, and able to be their actual selves.</em>&nbsp;</p>



<p><strong><em>Challenges and solutions to DEIB &#8211;</em></strong>&nbsp;</p>



<ul class="wp-block-list"><li><strong><em>Building a fair and representative talent pool and pipeline.</em></strong>&nbsp;</li></ul>



<p><em>Improving your recruitment strategies is a vital first step to </em><em>building and maintaining a diverse workforce.</em><em> Conducting diverse talent searches to bring in people from groups underrepresented in our workplace is a good starting point. targets you can set for our pipeline. Once we have expanded our&nbsp;&nbsp; pipeline to fit our needs, we can continue the tracking of DEIB throughout the recruitment cycle, identifying sources, keywords, or phrases that led to hired candidates.</em>&nbsp;</p>



<ul class="wp-block-list"><li><strong><em>Communication issues&nbsp;</em></strong>&nbsp;</li></ul>



<p><em>Many companies face </em><em>communication issues</em><em>, but these problems are bound to arise when you ask people from different backgrounds, cultures, genders, ages, etc., to work together. Misunderstandings, clashes, and conflicts will happen. It’s important to address these challenges before they become problematic.</em>&nbsp;</p>



<ul class="wp-block-list"><li><strong><em>Meeting diversity compliance</em></strong>&nbsp;<br><strong><em></em></strong>&nbsp;</li></ul>



<p><em>It’s crucial to </em><em>generate a diverse and inclusive pipeline</em><em>. Because of its many benefits, diversity, inclusion, equity, and belonging is becoming a bigger area of focus for companies. But as our company begins to introduce DEIB best practices, they must keep Equal Employment Opportunity Commission (EEOC) and Office of Federal Contract Compliance Programs (OFCCP) requirements in mind. Both laws allow companies to source for very specific types of candidates to increase diversity.</em>&nbsp;</p>



<ul class="wp-block-list"><li><strong><em>Training for DEIB</em></strong>&nbsp;</li></ul>



<p><em>Getting our entire workforce up to speed on DEIB practices can be a challenge. Good DEIB training empowers employees to look out for one another, and speak up if they see inequitable treatment of a colleague. But even before employees are hired, managers and recruiters need to know how to prioritize DEIB in the talent acquisition process, and to be aware of their biases in the hiring process.</em>&nbsp;</p>



<p><strong><em>Importance of Diversity, Equity, Inclusion, and Belonging in the Workplace</em></strong>&nbsp;</p>



<p><em>When people truly commit to driving diversity, equity, inclusion, and belonging in their workplace, they become more empathetic and engaged. This behaviour has a ripple effect, encouraging other employees to consider how they can bring DEIB to their work as well, thereby turning the office into a more compassionate, productive place</em>&nbsp;</p>



<ul class="wp-block-list"><li><em>Improves bias awareness</em>&nbsp;</li><li><em>Increases talent pool</em>&nbsp;</li><li><em>Boosts employee engagement</em>&nbsp;</li><li><em>Offers better decision making</em>&nbsp;</li><li><em>Improves performance</em>&nbsp;</li></ul>



<p><strong><em>Conclusion</em></strong><strong><em>&nbsp;</em></strong>&nbsp;</p>



<p><em>Diversity, equity, inclusion, and belonging are more than just concepts. When implemented, they supercharge our company’s success in the market and encourage&nbsp;&nbsp; innovation for years to come. But it can be challenging to build a workplace with DEIB values at the core—especially if you lag behind competitors, or fall short of diversity compliance goals.</em>&nbsp;</p>
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		<title>Recruitment is not a job. It’s a Responsibility.</title>
		<link>https://peopledecode.com/recruitment-is-not-a-job-its-a-responsibility/?utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=recruitment-is-not-a-job-its-a-responsibility</link>
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		<dc:creator><![CDATA[master]]></dc:creator>
		<pubDate>Tue, 15 Mar 2022 13:43:49 +0000</pubDate>
				<category><![CDATA[HR]]></category>
		<guid isPermaLink="false">https://peopledecode.com/?p=10097</guid>

					<description><![CDATA[Every time a resource reaches out to a recruiter or Every time a recruiter utters the question “ Are you looking out for a change?”, we trigger a series of responsibilities. A person unknown to us, trusts us with a lot of information. Which includes their job, their salary. And if you are good recruiter [&#8230;]]]></description>
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									<p>Every time a resource reaches out to a recruiter or Every time a recruiter utters the question “ Are you looking out for a change?”, we trigger a series of responsibilities.</p><ol><li>A person unknown to us, trusts us with a lot of information. Which includes their job, their salary. And if you are good recruiter you will even have them talk about their personal commitments, being the reason for change. A person with a stable life wants you to help them get into something better than they already have. The moment we collect this information, it becomes our responsibility to help the resource in best possible way.</li><li>When you are a recruiter, you are not just being a recruiter. You end up behaving and becoming a best friend, mentor, counselor, marketing executive, sales executive, sometimes a psychologist too apart from a recruiter. That’s too many responsibilities we cannot ignore.</li><li>The most important thing we all should remember and understand is whom are we representing to the resource. It doesn’t matter if you are in a Tier 1 company or a small counsultancy, for the candidate you are the point of contact. You are representing your company and your client. So your behaviour will impact the opinion in the eyes of the resource. You become the identity of an entire organization. That’s a huge responsibility.</li></ol><p>Let’s not be a recruiter.<br />Let us be “Responsible Recruiter”</p><p>&#8211; Ashwini Sundar<br />  Manager-Delivery Excellence<br />  PeopleDeCode</p>								</div>
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		<title>The concept of Fuzzy matching in Python</title>
		<link>https://peopledecode.com/the-concept-of-fuzzy-matching-in-python/?utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=the-concept-of-fuzzy-matching-in-python</link>
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		<dc:creator><![CDATA[master]]></dc:creator>
		<pubDate>Tue, 08 Mar 2022 09:44:47 +0000</pubDate>
				<category><![CDATA[Technical]]></category>
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					<description><![CDATA[Ever wondered how spellchecks and auto-corrections in your mobile phones bails you out by automatically suggesting the words that you were just about to type? I’ve come across so many amazing functionalities in Python but the fuzzywuzzy package in particular caught my interest. So i decided to write a blog about this topic and share [&#8230;]]]></description>
										<content:encoded><![CDATA[		<div data-elementor-type="wp-post" data-elementor-id="9212" class="elementor elementor-9212" data-elementor-settings="{&quot;element_pack_global_tooltip_width&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;element_pack_global_tooltip_width_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;element_pack_global_tooltip_width_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;element_pack_global_tooltip_padding&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;top&quot;:&quot;&quot;,&quot;right&quot;:&quot;&quot;,&quot;bottom&quot;:&quot;&quot;,&quot;left&quot;:&quot;&quot;,&quot;isLinked&quot;:true},&quot;element_pack_global_tooltip_padding_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;top&quot;:&quot;&quot;,&quot;right&quot;:&quot;&quot;,&quot;bottom&quot;:&quot;&quot;,&quot;left&quot;:&quot;&quot;,&quot;isLinked&quot;:true},&quot;element_pack_global_tooltip_padding_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;top&quot;:&quot;&quot;,&quot;right&quot;:&quot;&quot;,&quot;bottom&quot;:&quot;&quot;,&quot;left&quot;:&quot;&quot;,&quot;isLinked&quot;:true},&quot;element_pack_global_tooltip_border_radius&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;top&quot;:&quot;&quot;,&quot;right&quot;:&quot;&quot;,&quot;bottom&quot;:&quot;&quot;,&quot;left&quot;:&quot;&quot;,&quot;isLinked&quot;:true},&quot;element_pack_global_tooltip_border_radius_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;top&quot;:&quot;&quot;,&quot;right&quot;:&quot;&quot;,&quot;bottom&quot;:&quot;&quot;,&quot;left&quot;:&quot;&quot;,&quot;isLinked&quot;:true},&quot;element_pack_global_tooltip_border_radius_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;top&quot;:&quot;&quot;,&quot;right&quot;:&quot;&quot;,&quot;bottom&quot;:&quot;&quot;,&quot;left&quot;:&quot;&quot;,&quot;isLinked&quot;:true}}" data-elementor-post-type="post">
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									<p><em>Ever wondered how spellchecks and auto-corrections in your mobile phones bails you out by automatically suggesting the words that you were just about to type?</em></p><p>I’ve come across so many amazing functionalities in Python but the fuzzywuzzy package in particular caught my interest. So i decided to write a blog about this topic and share it to wider network.</p><p>Down below are the sub topics of this blog :</p><p><strong>Levenshtein Distance</strong></p><p><strong>Fuzzywuzzy Package</strong></p><p><strong>Some Examples</strong></p><p><strong>End Notes</strong></p><p><strong>Levenshtein Distance</strong></p><p>The concept of <em>Levenshtein Distance </em>sometimes also called as <em>Minimum Edit distance</em> is a popular metric used to measure the distance between two strings. It is calculated by counting number of edits required to transform one string into another. The edits could be one of the following:</p><ul><li>Addition of a new letter.</li><li>Removal of a letter.</li><li> Substitution</li></ul><p>Mathematical formula behind the calculation goes by something like this.</p>								</div>
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									<p>The formula above contains too many notations and subscripts too wrap one’s head around. I will quickly explain this by simple demonstration. Consider below the two strings <strong>‘Shallow’ and ‘Follow’.</strong></p><p>Following are the 3 simple edits required to change the 1st string to the other.</p><p><strong><em>Edit 1 : Remove the 1st letter ‘S’.</em></strong></p><p><strong><em>Edit 2 : Substitute ‘h’ with ‘F’.</em></strong></p><p><strong><em>Edit 3 : Substitute ‘a’ with ‘o’.</em></strong></p><p>And there we have it the Levenshtein distance between the two strings ‘Shallow’ and ‘Hallow’ is 3.</p><p>Now, there could be multiple ways of transitioning from one word to another but Levenshtein distance chooses the smallest possible path.</p><p>I have written a code in order to calculate the Levenshtein Distance for any two given strings. I highly recommend you to go through it. You can find the links to my notebook down below. Feel free to try out some other combinations and do let know if you find this helpful in the comment section.</p><p>Levenshtein Distance Calculation in Python : <a href="https://github.com/Sreemanto/Fuzzy-Matching/blob/master/Levenshtein%20Distance.ipynb"><strong>Link</strong></a></p><p>Also, to understand the calculations in more details and to make sense of the bizarre mathematical formula shown above do watch this video. Very well explained, very intuitive and I highly recommend you to go through it once.</p><p><strong>Fuzzywuzzy Package</strong></p><p>The concept of fuzzy matching is to calculate similarity between any two given strings. And this is achieved by making use of the Levenshtein Distance between the two strings.</p><p>fuzzywuzzy is an inbuilt package you find inside python which has certain functions in it which does all this calculation for us. I’m going to discuss four of them which are as follows:</p><p>fuzz.ratio()</p><p>fuzz.partial_ratio()</p><p>fuzz.token_sort_ratio()</p><p>fuzz.token_set_ration()</p><p>Note : To use this you have to first install this package. Here’s how you do it.</p><p>pip install fuzzywuzzy</p><p><strong>Examples</strong></p><p>You may need to restart your kernel post installation.</p><p><strong>ratio()</strong></p><p>from fuzzywuzzy import fuzz<br />fuzz.ratio(&#8216;My name is Sreemanta&#8217;,&#8217;My name is Sreemanta Kesh&#8217;)</p><p><em>Output : 89</em></p><p>The output is indicative of the fact that both the strings are 89% similar. Let us try some more examples.</p><p>print(fuzz.ratio(&#8216;My name is Sreemanta&#8217;,&#8217;My name is Sreemanta &#8216;))<br />print(fuzz.ratio(&#8216;My name is Sreemanta&#8217;,&#8217;My name is Sreemanta&#8217;))</p><p><em>Output : 98</em></p><p><em>Output : 100</em></p><p>fuzz.ratio() returns 100% only when it finds exact match. The 2nd example stated is an exact match where as the 1st one differs by a space.</p><p>fuzz.ratio(&#8216;My name is Sreemanta&#8217;,&#8217;Sreemanta name is My&#8217;)</p><p><em>Output : 45</em></p><p>This tells us that order of the string also matters while comapring.</p><p><strong>partial_ratio()</strong></p><p>Lets perform the same example using partial_ratio() method.</p><p>print(fuzz.partial_ratio(&#8216;My name is Sreemanta&#8217;,&#8217;My name is Sreemanta Kesh&#8217;))<br />print(fuzz.partial_ratio(&#8216;My name is Sreemanta&#8217;,&#8217;My name is Sreemanta      &#8216;))</p><p><em>Output : 100</em></p><p><em>Output : 100</em></p><p>Notice that the given strings are not same in either of the two cases still it gives a 100% match. This is because partial_ratio() is just checking if either of the string is a sub string of the other. Down below is another example to confirm the same</p><p>fuzz.partial_ratio(&#8216;New York City&#8217;,&#8217;New York&#8217;)</p><p><em>Output : 100</em></p><p><strong>token_sort_ratio()</strong></p><p>fuzz.token_sort_ratio(&#8216;My name is Sreemanta&#8217;,&#8217;Sreemanta name is My   &#8216;)</p><p><em>Output : 100</em></p><p>This result is sharply different from the one observed when same command was run with ratio() method which yielded 45% match.</p><p>fuzz.token_sort_ratio(&#8216;My name is Sreemanta&#8217;,&#8217;sreemanta name is My **  &#8216;)</p><p><em>Output : 100</em></p><p>From the above two example we can conclude that</p><ol><li>Order of the words does not matter.</li><li>It also ignores punctuation.</li></ol><p>It follows the concept of tokenization where the strings are converted in to tokens and then they are sorted in alphabetical order and thereafter the comparison happens.</p><p><strong>token_set_ratio()</strong></p><p>print(fuzz.token_sort_ratio(&#8216;My name is Sreemanta&#8217;,&#8217;Sreemanta name is My  Kesh &#8216;))<br />print(fuzz.token_set_ratio(&#8216;My name is Sreemanta&#8217;,&#8217;Sreemanta name is My  Kesh &#8216;))</p><p><em>Output : 89</em></p><p><em>Output : 100</em></p><p>token_set_ratio() takes a more flexible approach than token_sort_ratio(). Instead of just tokenizing the strings, sorting and then pasting the tokens back together, token_set_ratio performs a set operation that takes out the common tokens (the intersection) and then makes fuzz.ratio(). Extra or same repeated words do not matter.</p><p>fuzz.token_set_ratio(&#8216;Jingle Bells&#8217;,&#8217; Bells Jingle Bells&#8217;)</p><p><em>Output : 100</em></p><p><strong>End Notes</strong></p><p>When I performed fuzzy matching exercise first time I pondered over the following two questions</p><ol><li>How Levenshtein Distance (LD) is being used to calculate the ratio?</li><li>What is this ratio?</li></ol><p>After doing a small research I was able to unearth the formula.</p><p><strong>Ratio = (len(str1)+len(str2) &#8211; LD) / (len(str1)+len(str2))</strong></p><p>Exercise for you : Try to embed this formula into my Levenshtein Notebook</p><p>There are some more functionalities present inside the fuzzywuzzy package such as WRatio(), URation etc. I urge you to explore more.</p><p>Personally I have used fuzzy matching in my organization, where the aim was to map ‘Vendor Names’ with it’s ‘Invoice Reference’ and check for duplicity by setting the fuzz.ratio() threshold to 96%. This methodology has a various other important application across the industries like spell checks as listed in the beginning, CRM applications, Bioinformatics etc.</p><p> </p><p>Do comment your thoughts below.</p>								</div>
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		<title>5 data formatting task from my last Data Science Project</title>
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		<pubDate>Tue, 08 Mar 2022 08:45:47 +0000</pubDate>
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					<description><![CDATA[Whether you’re Data Analyst, Data Engineer or Data Scientist there’s just no running away from data cleaning. It’s the essential skill that you need if you want to make a career in data. I am writing this blog to share 5 unique data formatting challenge that I faced in my last project working for my [&#8230;]]]></description>
										<content:encoded><![CDATA[		<div data-elementor-type="wp-post" data-elementor-id="9181" class="elementor elementor-9181" data-elementor-settings="{&quot;element_pack_global_tooltip_width&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;element_pack_global_tooltip_width_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;element_pack_global_tooltip_width_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;element_pack_global_tooltip_padding&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;top&quot;:&quot;&quot;,&quot;right&quot;:&quot;&quot;,&quot;bottom&quot;:&quot;&quot;,&quot;left&quot;:&quot;&quot;,&quot;isLinked&quot;:true},&quot;element_pack_global_tooltip_padding_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;top&quot;:&quot;&quot;,&quot;right&quot;:&quot;&quot;,&quot;bottom&quot;:&quot;&quot;,&quot;left&quot;:&quot;&quot;,&quot;isLinked&quot;:true},&quot;element_pack_global_tooltip_padding_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;top&quot;:&quot;&quot;,&quot;right&quot;:&quot;&quot;,&quot;bottom&quot;:&quot;&quot;,&quot;left&quot;:&quot;&quot;,&quot;isLinked&quot;:true},&quot;element_pack_global_tooltip_border_radius&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;top&quot;:&quot;&quot;,&quot;right&quot;:&quot;&quot;,&quot;bottom&quot;:&quot;&quot;,&quot;left&quot;:&quot;&quot;,&quot;isLinked&quot;:true},&quot;element_pack_global_tooltip_border_radius_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;top&quot;:&quot;&quot;,&quot;right&quot;:&quot;&quot;,&quot;bottom&quot;:&quot;&quot;,&quot;left&quot;:&quot;&quot;,&quot;isLinked&quot;:true},&quot;element_pack_global_tooltip_border_radius_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;top&quot;:&quot;&quot;,&quot;right&quot;:&quot;&quot;,&quot;bottom&quot;:&quot;&quot;,&quot;left&quot;:&quot;&quot;,&quot;isLinked&quot;:true}}" data-elementor-post-type="post">
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									<p>Whether you’re Data Analyst, Data Engineer or Data Scientist there’s just no running away from data cleaning. It’s the essential skill that you need if you want to make a career in data. I am writing this blog to share 5 unique data formatting challenge that I faced in my last project working for my client.</p><h2>Tip : If you are preparing for you python interview this might be a good practice.</h2><ol><li><em> Creating tables from a nested json files.</em></li><li><em> Extracting the month name directly from month number.</em></li><li><em> Masking the confidential columns in your data.</em></li><li><em> Concatenating excel sheets with sheet names as alias for identification.</em></li><li><em> Formatting a complex dictionary to a required dataframe.</em></li></ol><h4>1. Creating tables from a nested json files</h4>								</div>
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															<img decoding="async" width="1400" height="117" src="https://peopledecode.com/wp-content/uploads/2022/03/d.png" class="attachment-full size-full wp-image-9183" alt="" srcset="https://peopledecode.com/wp-content/uploads/2022/03/d.png 1400w, https://peopledecode.com/wp-content/uploads/2022/03/d-300x25.png 300w, https://peopledecode.com/wp-content/uploads/2022/03/d-1024x86.png 1024w" sizes="(max-width: 1400px) 100vw, 1400px" />															</div>
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									<p>This is the sample json object that we need to start with <br />My first impression while staring at this problem was there must be definitely a method in pandas that can directly read json files and thank god that assumption came true. But let’s analyze this json little further as it appears to be nested.<br />After little exploration I could understand the structure little better</p>								</div>
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									<p>Next, let ushop on to Jupyter notebook and directly fire a query that reads a json file.<br />df = pd.read_json(‘test.json’)<br />df.head(3)<br />Executing above code produces the below output</p>								</div>
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									<p>Clearly, we can see there’s clean table barring the 1st columns. And if you observe closely, you will also find a mapping between the 1st column and the rest of the table. [ emp_id =1 &amp; Id = 1] At this point, I was convinced that these might be stored as two tables one serving as a master for the employees and other(1st column) having detailed information about them.</p>								</div>
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									<p>#only working on the 1st column.<br />d1 = pd.read_json(&#8216;test.json&#8217;).iloc[:,0]#creating an empty dataframe<br />df = pd.DataFrame()#looping through each sequence and concatenating them<br />for i in d1:<br />    df1 = pd.DataFrame(i)<br />    df = pd.concat([df,df1])<br />df</p>								</div>
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									<p>The above code upon execution will show a nice clean table which goes as follows</p>								</div>
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															<img loading="lazy" decoding="async" width="469" height="427" src="https://peopledecode.com/wp-content/uploads/2022/03/sdfsdf.png" class="attachment-full size-full wp-image-9189" alt="" srcset="https://peopledecode.com/wp-content/uploads/2022/03/sdfsdf.png 469w, https://peopledecode.com/wp-content/uploads/2022/03/sdfsdf-300x273.png 300w" sizes="(max-width: 469px) 100vw, 469px" />															</div>
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									<p>That completes the first table which was just contained in the 1st column. For extracting the master employee table the code goes down below.</p>								</div>
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									<p>#Skipping the first column and saving the rest of the table<br />d2 = pd.read_json(&#8216;test.json&#8217;).iloc[:,1:]</p>								</div>
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									<p>For beginners, I would like to point out that this is just one small task and ideally for carrying out these kind of specific task we should develop a habit of packing our code together by writing them in functions. Down below is the small example of how to piece all things together and also make sure to avoid hard coding.</p>								</div>
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															<img loading="lazy" decoding="async" width="987" height="413" src="https://peopledecode.com/wp-content/uploads/2022/03/sdsddsdd.png" class="attachment-full size-full wp-image-9190" alt="" srcset="https://peopledecode.com/wp-content/uploads/2022/03/sdsddsdd.png 987w, https://peopledecode.com/wp-content/uploads/2022/03/sdsddsdd-300x126.png 300w" sizes="(max-width: 987px) 100vw, 987px" />															</div>
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									<p>This might be less readable. The roadmap to get here should be by writing simple one liners first and then bundle them together in a function like this. <br />2. Extracting the month name directly from month number.</p>								</div>
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															<img loading="lazy" decoding="async" width="717" height="291" src="https://peopledecode.com/wp-content/uploads/2022/03/sfdfsdfffff.png" class="attachment-full size-full wp-image-9191" alt="" srcset="https://peopledecode.com/wp-content/uploads/2022/03/sfdfsdfffff.png 717w, https://peopledecode.com/wp-content/uploads/2022/03/sfdfsdfffff-300x122.png 300w" sizes="(max-width: 717px) 100vw, 717px" />															</div>
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									<p>This is the task.<br />This is easiest of all the five tasks only if you know the right library.</p>								</div>
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									<p>import calendardf[&#8216;Month_Name&#8217;] = df[&#8216;Month&#8217;].apply(lambda x: calendar.month_abbr[int(x)])</p>								</div>
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									<p>The above code should be enough for you if your ‘Month’ column does not contain missing values which was not the case for me. So, I had to write a code to incorporate that. Down below is the complete version of it</p>								</div>
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									<p>df[‘Month_Name’] = df[‘Month’].fillna(0).apply(lambda x: calendar.month_abbr[int(x)] if x!= 0 else np.nan)</p>								</div>
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									<p><strong>calendar.month_abbr[int(x)] </strong>might throw up an exception if it encounters a missing value and hence we need to impute it first and then write an else condition to restore it NaN.</p>								</div>
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									<h1>3. Masking the confidential columns in your data.</h1><p>Before jumping to the code I want to talk about the reason behind masking. When you work with consulting firms chances are you might have to deal with third party who may or may not have signed NDA. For people hearing NDA for the first time — Non-disclosure Agreements are an important legal framework used to protect sensitive and confidential information from being made available by the recipient of that information.<br />I was in a situation where we were dealing with the sample data and it involved sending it to a third party.<br />Now let’s jump on to the code. Here’s the sample data</p>								</div>
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															<img loading="lazy" decoding="async" width="663" height="465" src="https://peopledecode.com/wp-content/uploads/2022/03/sdfsdfsdfsdfsdfsdfsd.png" class="attachment-full size-full wp-image-9193" alt="" srcset="https://peopledecode.com/wp-content/uploads/2022/03/sdfsdfsdfsdfsdfsdfsd.png 663w, https://peopledecode.com/wp-content/uploads/2022/03/sdfsdfsdfsdfsdfsdfsd-300x210.png 300w" sizes="(max-width: 663px) 100vw, 663px" />															</div>
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									<p>find the data at : <a href="https://github.com/Sreemanto/5-data-formatting-task-from-my-last-Data-Science-Project/blob/main/Confidential_data.csv">Data</a><br />Chance are you might have never heard of the package called <strong>‘cape-privacy’<br /></strong>So. I would recommend you install this right away if you wish to emulate the same exercise at your end.</p>								</div>
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									<p># pip install cape-privacy</p>								</div>
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									<p>Now this library has an array of method. It generally aims at providing data privacy and data perturbation. I will link some good resources below right at the end of this blog where you can explore more about this package.<br />We want to import the transformation class from this module</p>								</div>
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									<p>from cape_privacy.pandas import transformations as tfms</p>								</div>
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									<p>This class has lot of methods associated with it to use. We can check them by firing below query</p>								</div>
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									<p>tfms??</p>								</div>
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									<p>This will take you to the below output.</p>								</div>
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															<img loading="lazy" decoding="async" width="943" height="352" src="https://peopledecode.com/wp-content/uploads/2022/03/kkjh.png" class="attachment-full size-full wp-image-9194" alt="" srcset="https://peopledecode.com/wp-content/uploads/2022/03/kkjh.png 943w, https://peopledecode.com/wp-content/uploads/2022/03/kkjh-300x112.png 300w" sizes="(max-width: 943px) 100vw, 943px" />															</div>
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									<p>You can check the documentation of each one of them in details by further querying on the specific method. I’m going to use Tokenizer let’s read and understand the documentation as we are using this for the very first time.</p>								</div>
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															<img loading="lazy" decoding="async" width="848" height="412" src="https://peopledecode.com/wp-content/uploads/2022/03/vvvv.png" class="attachment-full size-full wp-image-9195" alt="" srcset="https://peopledecode.com/wp-content/uploads/2022/03/vvvv.png 848w, https://peopledecode.com/wp-content/uploads/2022/03/vvvv-300x146.png 300w" sizes="(max-width: 848px) 100vw, 848px" />															</div>
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									<p>This is just a part of the documentation. When you execute this you will see details of attributes and examples associated with the same method.<br />Ok. So, the example given tells us that we need to specify a parameter called <strong>‘max_token_len’</strong> which should tell python the length of the masked data. The other parameter <strong>‘sceret’</strong> allows the masked data to be reversed to their original data when the secret<br />key is known we which really don’t need as of now.<br />And here goes the final execution.</p>								</div>
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									<p>tokenize_name = tfms.Tokenizer(max_token_len = 20)<br />df[&#8216;Name&#8217;] = tokenize_name(df.Name)<br />df</p>								</div>
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									<p>And we have our masked data.</p>								</div>
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															<img loading="lazy" decoding="async" width="715" height="497" src="https://peopledecode.com/wp-content/uploads/2022/03/cvvc.png" class="attachment-full size-full wp-image-9196" alt="" srcset="https://peopledecode.com/wp-content/uploads/2022/03/cvvc.png 715w, https://peopledecode.com/wp-content/uploads/2022/03/cvvc-300x209.png 300w" sizes="(max-width: 715px) 100vw, 715px" />															</div>
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									<p>Pheww !! Now I can safely ship the data to third party.</p>								</div>
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									<h1>4. Concatenating excel sheets with sheet names as alias for identification.</h1><p>If the heading does not make sense to you then just ignore it and look at the image down below.</p>								</div>
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															<img loading="lazy" decoding="async" width="1400" height="958" src="https://peopledecode.com/wp-content/uploads/2022/03/exvefff.png" class="attachment-full size-full wp-image-9197" alt="" srcset="https://peopledecode.com/wp-content/uploads/2022/03/exvefff.png 1400w, https://peopledecode.com/wp-content/uploads/2022/03/exvefff-300x205.png 300w, https://peopledecode.com/wp-content/uploads/2022/03/exvefff-1024x701.png 1024w, https://peopledecode.com/wp-content/uploads/2022/03/exvefff-748x512.png 748w, https://peopledecode.com/wp-content/uploads/2022/03/exvefff-760x520.png 760w" sizes="(max-width: 1400px) 100vw, 1400px" />															</div>
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									<p>We have <a href="https://github.com/Sreemanto/5-data-formatting-task-from-my-last-Data-Science-Project/blob/main/Football_Data.xlsx">data</a> containing multiple sheets and we need to combine all of them together with sheet name as new column for identification<br />In actual scenario I had more than 30 sheets and hardcoding the sheet names was simply not an option. I am using pandas for 4+ years yet I was never aware of a method which directly fetches the sheet name.<br />This can be achieved by simple two liner</p>								</div>
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									<p>path = &#8216;Football_Data.xlsx&#8217;<br />sheetnames = pd.ExcelFile(path).sheet_names<br />sheetnames</p>								</div>
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									<p>Output : [‘Premier League’, ‘Serie A’, ‘La Liga’]<br />Leveraging this output we are going to run a loop and add sheets one after another by using<br />pandas.concat as we saw in the 1st task in the blog</p>								</div>
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									<p>df = pd.DataFrame()<br />for name in sheetnames:<br />    df1 = pd.read_excel(&#8216;Football_Data.xlsx&#8217;,sheet_name = name)<br />    df1[&#8216;Type&#8217;] = name<br />    df = pd.concat([df,df1])<br />df.reset_index(drop=True,inplace=True)</p>								</div>
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									<p>The resultant dataframe.</p>								</div>
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															<img loading="lazy" decoding="async" width="766" height="385" src="https://peopledecode.com/wp-content/uploads/2022/03/jjkjkj.png" class="attachment-full size-full wp-image-9201" alt="" srcset="https://peopledecode.com/wp-content/uploads/2022/03/jjkjkj.png 766w, https://peopledecode.com/wp-content/uploads/2022/03/jjkjkj-300x151.png 300w" sizes="(max-width: 766px) 100vw, 766px" />															</div>
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									<p>I never though this would be as simple as this.</p>								</div>
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									<h1><strong>5. Formatting a complex list to a required dataframe.</strong></h1><p>The question goes something like this</p>								</div>
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															<img loading="lazy" decoding="async" width="1400" height="511" src="https://peopledecode.com/wp-content/uploads/2022/03/aaaa.png" class="attachment-full size-full wp-image-9202" alt="" srcset="https://peopledecode.com/wp-content/uploads/2022/03/aaaa.png 1400w, https://peopledecode.com/wp-content/uploads/2022/03/aaaa-300x110.png 300w, https://peopledecode.com/wp-content/uploads/2022/03/aaaa-1024x374.png 1024w" sizes="(max-width: 1400px) 100vw, 1400px" />															</div>
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									<p>At this point you can pause reading the blog and take up this challenge. I was interviewing someone for a senior Data Engineer role and gave this problem to test the candidate’s python skill. So if you are able to solve this in 15 mins(yes you can google) you can consider your self to be at a very good place ad I will rate you 4.5/5 in Python.<br />Way I approach these kind of problems is by starting from an absolute baseline by firing the below query blindly</p>								</div>
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									<p>#sliced for the first shift by looking at the target dataframe<br />pd.DataFrame(availability[0:7])</p>								</div>
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															<img loading="lazy" decoding="async" width="227" height="330" src="https://peopledecode.com/wp-content/uploads/2022/03/sasadsadsa.png" class="attachment-full size-full wp-image-9203" alt="" srcset="https://peopledecode.com/wp-content/uploads/2022/03/sasadsadsa.png 227w, https://peopledecode.com/wp-content/uploads/2022/03/sasadsadsa-206x300.png 206w" sizes="(max-width: 227px) 100vw, 227px" />															</div>
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									<p>We are no way near the output dataframe but one realization came to my mind that we might have to go for a transposed version of this dataframe.</p>								</div>
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															<img loading="lazy" decoding="async" width="963" height="450" src="https://peopledecode.com/wp-content/uploads/2022/03/acvfrt.png" class="attachment-full size-full wp-image-9204" alt="" srcset="https://peopledecode.com/wp-content/uploads/2022/03/acvfrt.png 963w, https://peopledecode.com/wp-content/uploads/2022/03/acvfrt-300x140.png 300w" sizes="(max-width: 963px) 100vw, 963px" />															</div>
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									<p>Now compare this intermediate dataframe to the final resultant data frame. You will realize that only thing required from this data is last row and the index needs to be renamed from <strong>‘value’ </strong>to<strong> ‘shift 1’.</strong></p><p>So, at this point. I need to make sure.</p><ol><li>Get rid of the rest of the rows.</li><li>Renamed from <strong>‘value’ </strong>to<strong>‘shift 1’ </strong>before transposing</li><li>Get rid of the shift and day value.</li></ol><p>Let’s do that and see the results</p>								</div>
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									<p>pd.DataFrame(availability[0:7]).rename(columns={&#8216;value&#8217;:&#8217;shift&#8217; + &#8216;1&#8217;}).iloc[:,2:].T</p>								</div>
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															<img loading="lazy" decoding="async" width="276" height="69" src="https://peopledecode.com/wp-content/uploads/2022/03/sdfsdfsdfssff.png" class="attachment-full size-full wp-image-9205" alt="" />															</div>
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									<p>Now, we are somewhat close to our resultant dataframe. Next two tasks are</p><ol><li>Convert the 0,1,2… to Monday, Tuesday, Wednesday..</li><li>Run a loop for all the shifts by slicing every 7 rows from the starting dataframe.</li></ol><p>Step 1 goes as follows</p>								</div>
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									<p>import calendardf = pd.DataFrame(availability[0:7]).rename(columns={&#8216;value&#8217;:&#8217;shift&#8217; + &#8216;1&#8217;}).iloc[:,2:].T<br />df.columns = list(map(lambda x:calendar.day_name[int(x)],df.columns))<br />df</p>								</div>
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									<p>Does the above code ring any bells? 🙂</p>								</div>
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															<img loading="lazy" decoding="async" width="721" height="76" src="https://peopledecode.com/wp-content/uploads/2022/03/asacacasa.png" class="attachment-full size-full wp-image-9206" alt="" srcset="https://peopledecode.com/wp-content/uploads/2022/03/asacacasa.png 721w, https://peopledecode.com/wp-content/uploads/2022/03/asacacasa-300x32.png 300w" sizes="(max-width: 721px) 100vw, 721px" />															</div>
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									<p>Now. Putting all of it together</p>								</div>
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									<p>df = pd.DataFrame()for i in range(0,len(availability),7):<br />    <br />    df = pd.concat([df,pd.DataFrame(availability[i:i+7]).iloc[:,2:].T])<br />    <br />df.index = list(map(lambda x:&#8217;Shift &#8216;+str(x+1),df.reset_index(drop=True).index.values))df.columns = list(map(lambda x:calendar.day_name[int(x)],df.columns))df</p>								</div>
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															<img loading="lazy" decoding="async" width="748" height="174" src="https://peopledecode.com/wp-content/uploads/2022/03/sadsaxsaxsasadsa.png" class="attachment-full size-full wp-image-9207" alt="" srcset="https://peopledecode.com/wp-content/uploads/2022/03/sadsaxsaxsasadsa.png 748w, https://peopledecode.com/wp-content/uploads/2022/03/sadsaxsaxsasadsa-300x70.png 300w" sizes="(max-width: 748px) 100vw, 748px" />															</div>
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									<p>Resultant DataFrame</p><h1>Conclusion</h1><p>These tasks might not appear so difficult as we have libraries in place that does the job for us but if you are a beginner or an intermediate in python chances are you might have never used some of them before.</p><p>Thank for reading.</p>								</div>
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		<title>Just Enough Data Science</title>
		<link>https://peopledecode.com/just-enough-data-science/?utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=just-enough-data-science</link>
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		<dc:creator><![CDATA[master]]></dc:creator>
		<pubDate>Sun, 06 Mar 2022 12:41:37 +0000</pubDate>
				<category><![CDATA[Technical]]></category>
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					<description><![CDATA[Learning Timeline The first question that I am always asked is how much time does it take. I’ll answer it in two ways. Assuming you’re a working professional&#8211;&#160;Anything between 10–12 hrs a week stretched for continuous 18–20 weeks should be enough. Remember consistency is the key, remaining consistent can be a big challenge and this [&#8230;]]]></description>
										<content:encoded><![CDATA[		<div data-elementor-type="wp-post" data-elementor-id="9039" class="elementor elementor-9039" data-elementor-settings="{&quot;element_pack_global_tooltip_width&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;element_pack_global_tooltip_width_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;element_pack_global_tooltip_width_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;element_pack_global_tooltip_padding&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;top&quot;:&quot;&quot;,&quot;right&quot;:&quot;&quot;,&quot;bottom&quot;:&quot;&quot;,&quot;left&quot;:&quot;&quot;,&quot;isLinked&quot;:true},&quot;element_pack_global_tooltip_padding_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;top&quot;:&quot;&quot;,&quot;right&quot;:&quot;&quot;,&quot;bottom&quot;:&quot;&quot;,&quot;left&quot;:&quot;&quot;,&quot;isLinked&quot;:true},&quot;element_pack_global_tooltip_padding_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;top&quot;:&quot;&quot;,&quot;right&quot;:&quot;&quot;,&quot;bottom&quot;:&quot;&quot;,&quot;left&quot;:&quot;&quot;,&quot;isLinked&quot;:true},&quot;element_pack_global_tooltip_border_radius&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;top&quot;:&quot;&quot;,&quot;right&quot;:&quot;&quot;,&quot;bottom&quot;:&quot;&quot;,&quot;left&quot;:&quot;&quot;,&quot;isLinked&quot;:true},&quot;element_pack_global_tooltip_border_radius_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;top&quot;:&quot;&quot;,&quot;right&quot;:&quot;&quot;,&quot;bottom&quot;:&quot;&quot;,&quot;left&quot;:&quot;&quot;,&quot;isLinked&quot;:true},&quot;element_pack_global_tooltip_border_radius_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;top&quot;:&quot;&quot;,&quot;right&quot;:&quot;&quot;,&quot;bottom&quot;:&quot;&quot;,&quot;left&quot;:&quot;&quot;,&quot;isLinked&quot;:true}}" data-elementor-post-type="post">
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									<h1><strong>Learning Timeline</strong></h1><p><em>The first question that I am always asked is how much time does it take. I’ll answer it in two ways.</em></p><ol><li><strong>Assuming you’re a working professional</strong><strong>&#8211;</strong> Anything between 10–12 hrs a week stretched for continuous 18–20 weeks should be enough. Remember consistency is the key, remaining consistent can be a big challenge and this where most learners break.</li><li><strong> Assuming you’re a full-time learner &#8211;</strong>In this case, you don&#8217;t have the burden of working 6–8hrs a day and you can devote more time to learning. If you can manage 20+ hrs week 10–12 weeks you should be ready and if you are choosing a training program select one that fits this timeline.</li></ol><h1><strong>Data Science skillset</strong></h1><p>This is where there lies a big gap in information for the learners. I’ve seen 90% of data science course generally focuses on only three things i.e. <strong>Python, statistics, algorithms(including ML/DL)</strong> while this is certainly the essentials to of being a data scientists but we need more. Once you are hired your role actually fluctuates. For the outsiders, the tag <strong>“Data Scientists”</strong> misleads people and sometimes unfairly assumes that it&#8217;s only about predictive models. You will find me writing SQL queries, working in excel, building dashboards, and at times even documenting my work in PowerPoint slides.</p><p>Here’s the list of skills you should acquire before applying for a Data Science job:</p><ul><li><strong><em>Python</em></strong></li><li><strong><em>Stats &amp; ML</em></strong></li><li><strong><em>One Data Visualization tool &#8211; Power BI/Tableau</em></strong></li><li><strong><em>SQL</em></strong></li><li><strong><em>Exposure to at least one cloud platforms from AWS/Azure/GCP</em></strong></li><li><strong><em>Python web frameworks like Flask/Django (good to have)</em></strong></li></ul><p>One thing I’ve learned working in the Industry is Data Science is just a process where you use data to add value to the business. Sticking just to algorithms is sometimes is not enough. Additionally, the analysis you do will not be sitting in your local machine, it has to be deployed somewhere so explore #5 and #6 and learn how to deploy/productionize your ML models.</p><p>Why have I not mentioned R? R is cool but Python is now lightyears ahead of R. Also, out of every 10 jobs, you’ll find 8 of them would require Python. Python is a scripting language that goes beyond just Data Science. I cannot say the same for R. I recommend learning just one out of R and Python and hence the latter would be a wise pick.</p><h1><strong>Application</strong></h1><p>The best way to learn something is implementation. Don’t just limit your learning to exercise and assignments in online/offline classes. Make sure you are building a full-fledged project which explains to the recruiter your ability to work as a data scientist. I recommend building an end-to-end project, I will recommend one in the resource section. Avoid drafting projects like titanic datasets, house price prediction, etc.</p><p>When you work on a project always keep these three points in your mind:</p><ol><li><strong><em> What is the objective of the project?</em></strong><strong><em><br />2. What was your role in the project?<br />3. What impact will your project make to the business/clients/anybody that you are working for?</em></strong></li></ol><p>As a recruiter I am always asking what challenges has the candidate faced and how did he overcome them.</p><p><strong>Sample Project:</strong> <a href="https://www.youtube.com/watch?v=MpF9HENQjDo&amp;list=PL2zq7klxX5ASFejJj80ob9ZAnBHdz5O1t">https://www.youtube.com/watch?v=MpF9HENQjDo&amp;list=PL2zq7klxX5ASFejJj80ob9ZAnBHdz5O1t</a></p><p><em><strong>Tip: </strong></em><em>If you are watching the tutorial just try emulating the steps with a different dataset and also make sure to give credit to the actual creator</em>. Avoid Plagiarism.</p><h1><strong>How to combat the inexperience</strong></h1><p>This is by far the biggest challenge that people face. The harsh reality is there are very limited companies appoints fresher. The easiest route in today&#8217;s day is to get an internship at any firm and work your way to a full-time job. Managers will prefer in-house interns over external inexperienced candidates. That’s how I see people getting hired in my firm and others.</p><p><em><strong>But what to do if you’re already working in a non-data science or non-IT job with 5+ years of experience or even less?</strong></em></p><p>All those years of experience will be irrelevant when you go for a Data Science job. So before jumping ships please ask yourself the following two questions.</p><ol><li><em> Why do you want to be a data scientist?<br /></em><em>2. What is your expectation in terms of pay if you land a full-time job?</em></li></ol><p>Swapping from one domain to other has many disadvantages and you should have good reasons for that. I was teaching a candidate who had 5 years of experience as a java developer and was already earning something around 10 LPA which is very good in terms of Indian standards. When you learn a new skill you are starting fresh. So don’t expect high numbers.</p><p>I’m sure you can relate it to other professions. If you are a footballer and you want to play tennis you should not expect right away to play in the same field as the grand slam winners or earn like them. Data Science is no different.</p><p>That being said you can still make it to Data Science. I hear success stories every day of people transitioning from non-IT fields to Analytics and Machine Learning. I am one of them. I was a Civil Engineer with no coding experience and here I am working as a Data Scientist for one of the biggest consulting firms in the globe.</p><p><em><strong>So the main question If I’m a civil engineer with 5+ years of work ex. how do I utilize my experience?</strong></em></p><p>Step 1 — Fabricate your experience. Split it into 3+2. Sell a story like this. “I worked as full time … for the past three years and then I was heavily involved in analytics of raw materials/reinforcements/topography etc”</p><p>Step 2 — Back it up with working with some relevant projects. Here are few examples.<br />1. If you are working as transport/roadways designing a project where you’re trying to predict the vehicle density based on past data collected would be a nice one.<br />2. If you are involved in structural designing then predicting the compressive strength of the concrete based on features like cement, slag, ash, water, fine aggregates, etc.</p><p>I can go on with more examples but I hope you get the point. And for people belonging to any other industry, the aim is to show a project relevant to your current line of profession which can be used to make your current process more effective by increasing revenue or limiting losses, or by saving time.</p><p>Step 3 — Reach out to someone who is actively working in the field maybe someone like me and ask them to interview you. Ask them to grill with questions regarding the projects mentioned in step 2.</p><p><em><strong>Tip </strong></em><em>— Data cleaning is an integral aspect of Data Science. Talk to any data scientist they will tell you that 70–80% of their time goes into structuring their data. You can always express your love and desire to clean messy data.</em><em><br />This is something that does not need any experience and works well for beginners and experts and will certainly be appreciated by your interviewer.</em></p><h1><strong>Building your own brand</strong></h1><p>We’re in 2021 and every individual or business needs a professional presence. You should show your presence as well.</p><p>LinkedIn is a great way to help launch your career. It’s cheap and effective — if you have a strategy. Over the course of the last 3 years, I have grown my own brand a lot on LinkedIn and people reach out to me to check if I’m looking for a change or interested in taking up their job or be a part of their ventures.</p><p>This week my LinkedIn profile exploded and I also received job invites from people from my Network.</p><p>I’m at a place where I’m confident enough that if I make myself available to my network I will be getting a call from someone in my network. This is the modern way of job hunting.</p><p>The aim is to make people recognize you by your work. Engage with the community. Send connection requests to Data Scientists who are actively working in the field. Be polite and don’t randomly approach them to help with your questions or ask for a referral.</p><p><em><strong>Note:</strong></em><em> Building a good LinkedIn profile takes time it does not happen overnight so consistency is the key.</em></p><p>I also recommend all my mentees write blogs as they learn. Writing is the best way to validate your learning. And it also a trick to attract your recruiter to your point of strength. Wonder how? Let’s say you have written 5 blogs on Ensemble learning. Chances are you would be asked questions on the same. Projects and blogs are the 1st level of cross-questioning you can expect from a recruiter, they want to first test if you are confident about your own work and you should be able to answer them assuming you must be knowing about the topics you blog.</p><p>Lastly, do keep a track of all your exercises and projects in a GitHub repository. It acts like proof that you’ve actually worked on the project that you are talking about. Please note learning will be gradual and hence uploads in GitHub should also be timed.</p><p><strong>LinkedIn, Blogpost, and GitHub should be the 1st three things that go in your resume.</strong></p><h1><strong>Roadmap I recommend</strong></h1><p>Get yourself enrolled in a course. If possible offline. They say if you’re climbing a mountain, it’s better to do it with someone who has already done it. Keep in mind the following things before choosing a course.</p><ol><li><em> Trainers Profile — Always learn from some who has Industry Experience.<br />2. Course duration — Maximum 6 months.<br />3. Cost — Make sure you are not making a hole in the pocket.<br />4. Syllabus — Get the contents reviewed with someone who’s working in the field.</em></li></ol><p>I learned data science while working full time. I had opted for a course that at the time charged me 2.25 lacs INR (3025 USD). Back in the day’s resources and tutorials were very limited, so I don’t regret the decision. But, since I had invested big, I was determined to work hard and make something out of it. So I was able to remain consistent.</p><p>It is too much money for someone who is learning data science. I would recommend not investing anything more than 30k-35k INR (500–550 USD) in any course.</p><p> </p><p>Hope reading this blog was worth your time.</p><p>Good Luck on your journey to Data Scientist!</p>								</div>
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