DEV Community: HackerEarth

Building Future-Ready Tech Teams

HackerEarth — Thu, 30 Sep 2021 13:23:00 +0000

For the third year in a row, there is a massive increase in the skills gap across industries, with nearly 80% of organizations saying that their teams lack the necessary skills; as stated by the Global Knowledge IT Skills and Salary report. The ever-increasing skills gap is the bane of IT leaders who aim to build future-ready tech teams. The age of digital disruption dictates the requirements of the current and future workforce and the existing workforce is unprepared to address the emerging trends and developments of the future.

Hiring their way out of this problem is not an option, say recruiters. There is a dire need for upskilled personnel and every organization needs to identify and provide ample opportunities for its employees to grow and upskill themselves.

Flexible, scalable, and innovative teams are the future of tech. The sooner companies throw traditional practices out of the window the better. Future-proofing teams dictates the success of any organization and ensures it stays relevant, in 2021.

"Digital transformation and innovation are happening at such a rapid pace. Companies are feeling the pressure too, which is why they must be building future-proofed teams and ever-ready workforces. Ensuring your team can scale with the changing times, will ensure your teams are agile, scalable, and ready for any market or industry demands. The onus is on business leaders to give their teams the skills they need to keep up and stay ahead. Future-proofing is a team-sport, meaning everyone can, and should, play an active role in learning, growing, and innovating. From taking online courses to attending virtual events like hackathons and conferences, there is an abundance of opportunities available to ensure companies always continue to stay ahead of the curve."

– Brian H. Hough, Founder of Airblock Technologies

Traits of a future-ready workforce

A finely balanced workforce consisting of both people and technology.
Tech teams that are characterized by continual learning integrated with their flow of work.
Future-ready tech teams have a repertoire of skills that will come into use 5-10 years from now.
The ability of organizations to address the skills-gap challenge by assessing the current lack of skills, and predicting skills needed for the future will help them in future-proofing their tech teams. Although it’s hard to accurately predict future demands; due to the fast-paced advancements in technology, there is a set of skills that will never go out of style.

Soft skills. They are overwhelmingly hard to find and the pressing need for these soft skills is tied to employees’ abilities to learn and adapt to change. This agility is becoming increasingly important – perhaps even more than functional or technical skills.

“Communication is the key in every company. It is even more important in the remote company of the future. Remote companies need 10x the process early on, and it pays out later though. A very underrated skill is communicating your progress and status with the rest of your teammates and keeping your project management system tight.”

– Radoslav Stankov, Head of Engineering at Product Hunt

How can you identify and address the challenges of building future-ready tech teams?

Future-proofing activities have led companies to ideate newer strategies and morph their team structures to meet real-time disruptions and demands. Three important areas that need attention are:

The organizational skills gap

The skills gap refers to the mismatch between the skills that employers are looking for in employees, and the skills those employees possess. Persistent skills shortage affects the business objectives of a company, and a stop-gap solution is not the answer. It is difficult to pinpoint any one reason for this. However, a few well-informed guesses would include a lack of qualified applicants and a lack of learning investment in existing employees. Acknowledging that tech teams are falling behind, and identifying the wide skills gap across the organization is the first step.

Learning and development programs

Nearly 39% of decision-makers attributed skills gaps to a lack of training investment two years ago. In 2021, 74% of organizations say reskilling their workforce is crucial to their success over the next 12–18 months. Organizations need to step up and provide suitable learning and development opportunities for their employees, which have the potential to transform market volatility into growth. Forward-thinking companies also encourage and enable employees to apply their skills and interests in different ways.

L&D programs are the need of the hour when it comes to closing the skills chasm. Internal upskilling of teams is an effective way to future-proof your workforce and provide an improved employee experience.

Leveraging technology

This one is a no-brainer. Technology, being one of the major reasons for the skills gap challenge, is also key to bridging it, and enabling rapid up- and re-skilling. Equipping employees with the right digital tools to work with today, and encouraging the use of the newest technologies and tools to keep up with the trends of tomorrow must be a part of any plan to build future-ready tech teams.

No crystal ball can predict what the future of work is going to look like. What should we do?

Join the community – Facebook Groups, conferences, webinars, etc. – Don’t just watch/read, contribute! It’ll force you to branch out and learn new things to create the content.
Demo 2 products every month – this will keep you on the front end of the technology evolution. Even if you have no budget and don’t need anything, always be exploring what’s out there.
A/B test and iterate – if things are going “well” – give yourself a pat on the back, and then iterate to find an even better way to do it – and continue doing this, forever. You will NEVER find “the right” way to do things, only better ways. And there are ALWAYS better ways.”

Into the future

As organizations head into an uncertain, post-crisis landscape, it is imperative to create more integrated and resilient systems that can fuel their teams to do better. Empowering employees to learn new skills and following a culture of continuous improvement will pave the way for building future-ready tech teams.

“If you chase all the trends not only will you not keep up but you’ll likely lose sight of your core priorities. Technology should equip and enable you to serve your customers more effectively and expand the value you create for them. Technology isn’t self-justifying and you shouldn’t follow just any new trend. “Future-proofing” requires a deep understanding of your potential futures! What is your market, where do you have product/market fit, and what are your core competencies as a company? Focusing on the core drivers for your business will enable you to select a set of innovative technologies to keep an eye on and through that focus do a much better job avoiding change fatigue and avoid getting distracted with the latest shiny “innovation.”

– Steve O’Brien, President of Staffing at Job.com

Organizations that invest in their current and future workforces invest in themselves. The key to truly succeed is to constantly experiment, fail, learn, grow—and not be afraid to start the process anew when the world invariably changes again.

3 Habits Of Highly Productive Developers

HackerEarth — Thu, 30 Sep 2021 13:17:19 +0000

👨‍💻Hey Developers, here's 3 tips to help you stay productive.

💡Wayne Brewer, our Social Media Specialist, goes over his 3 main takeaways from the blog: Lessons In Productivity From Radoslav Stankov.

🔗Click here to watch now: https://www.youtube.com/watch?v=77pg5Cz6qOA&t=1s&ab_channel=HackerEarth

Lessons In Productivity From Radoslav Stankov (Product Hunt)

HackerEarth — Mon, 27 Sep 2021 12:26:45 +0000

As developers, we deal with a myriad of problems every day. From debugging to deploying, there is a lot that goes on in our workplaces – some of it really exciting, but most of it mundane, grunt work. Productivity in such instances can always take a hit.

Some days ago, I had the opportunity to sit down with Radoslav Stankov, Engineering Head, Product Hunt, on the latest episode of Codecast (go check it out already if you haven’t). Rado leads one of the best Engineering teams at Product Hunt and I thought who better to chat about productivity than someone who has been doing this for 18+ years. Much to my pleasure, Rado was kind enough to share some habits he has developed over the years to maximize his productivity on the podcast.

While there’s a lot that we spoke about during our hour-long conversation, I am distilling the essence of his ‘tips’ for maximizing productivity below. To all the developers grappling with this problem – I hope this helps!

Radoslav’s four-point path to maximize productivity

Lesson 1: Deplete your EGO

Everybody has limited mental resources and this stands true for developers as well. Every decision from your morning coffee, to the infinite lunch plans, consumes your mental resource. Rado states that if a certain problem takes longer than usual for him to solve, he revisits it the next day when productivity is usually on his side. This usually happens when people use up their available willpower on one task. To put it in simple terms willpower is to developers what RAM is to computers, the better it is managed, the higher will the productivity be. So before you start your day, make sure you don’t have a million lunch plans to go through.

Lesson 2: Know your tools

Hitting CTRL+S 381414234 times before compiling will not help you run the program better, but on a serious note, you need to know your way in and out of the tools you use. Reducing the time taken for monotonous tasks often results in a lot of leftover time which can be directed towards other tasks. A lot of developers often waste a lot of time navigating their way through tools and the best way to get over this is to just pick a tool you use and start understanding how it can be used to make your work easier. Ensuring uniformity across the org when it comes to tools often becomes the most important factor while introducing developers to new tools.

Lesson 3: Reiterate the impact you make

Developers often don’t acknowledge the importance of the impact they make and this is subjective to different organizations. A lot of developers in MNCs often don’t feel like they make an impact because of the size of the organization which ultimately affects their productivity. This isn’t usually the case with startups where developers are given more responsibility and can directly see the impact of the code they ship.

Lesson 4: Journal your progress (even if it’s not on paper)

Journaling is the most underrated hack to productivity. Period.

Developer journals could be a mix of quick links, tips, notes, doodles, and anything that you wish to come back to (or not) in the future. It is an excellent way to not just keep track of the things you need to address in the future, but also to reflect on the mistakes you’ve made in the past.
Some of the best developers fail to document the progress they have made and often find it difficult to see how far they have come from when they started. This majorly hampers their productivity and is one of the biggest mistakes that developers can make early on in their careers. Rado states that journaling is what really helps him during the lows of his career and going through his previous entries often gives him a morale boost. There’s a bunch of free resources out there that’ll help you do this and the only thing stopping you from writing is well…YOU!

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Go big, or go home, right?

As Ernest Hemmingway said, one should not mistake motion for action. Action has a discernable impact; motion just is. Each of us in the developer community wants to make an impact with the code we write. Real change is but seeing something you worked hard at, make life simpler for someone else. For us to achieve that, it is important that we focus on our mental resources, and make the best of the time we had. Remember when Steve Jobs wore the same black turtleneck every day – one less decision to make, one more ounce of mental strength preserved.

I hope you learned as much from Rado as I did. I have learned a lot from my conversations with the stalwarts of the coding world on Codecast, and I hope you will find some inspiration from these greats. Do listen, and if there are other ways that you use to maximize your productivity please do let me know!

Why Jupyter Notebooks are the future of data science interviews

HackerEarth — Thu, 26 Aug 2021 06:33:42 +0000

Problem:

There’s a port somewhere in the world that wants to maximize profits.

Approach:

Said port hires a data scientist to look at the numerous variables affecting ship movement and operational efficiency - factors that affect profitability in the long run.

The data scientist looks at how many ships enter the port on a daily basis, where they are loaded and unloaded, the size of ships coming in versus the length of the docks where they are anchored, the time lost when a ship of the wrong size enters a dock and then has to re-dock correctly, the number of port employees required to unload a single ship by length and type of cargo, the future plans for the port and the predicted volume of ships entering.

Then they begin their analysis.

Conclusion:

Our data hero announces that the port will have to hire at a rate of 3% every year to keep up with increasing volume. They also help the authorities set up a system that helps ships navigate to the correct dock and alerts authorities in advance when a ship is approaching. This leads to increased efficiency overall, better communication between the docks and the ships; thus decreasing time lost in re-docking, and increases profits for the port.

Accounting for seasonal variations in traffic, and the time and effort needed to train the staff in using the new navigation system, the data scientist predicts that the port can look at a probable profit increase of 20% in 3 years.

The key word here is ‘probable’.

Let’s read that first part again. The solution seems so simple, right? That simple solution, however, requires months of data crunching and historical analysis to create operational models for the future.

The end result in this scenario is a probability and not a number written in stone, because several factors (trade wars, a pandemic, oil prices, consumer demand) can affect the port’s operations. These are factors one cannot guarantee, or foresee, but a good data scientist is expected to account for all of these and still come up with a reliable prediction.

This is why good data scientists are so in-demand across the tech sector. Also, why assessing and hiring good data scientists is so hard.

Data scientists are not the same as generalist programmers.

Assessing a data scientist is not the same as assessing another developer. The above example would have helped you understand the difference between the problems that a data scientist works on and those that a programmer solves.

There are differences even in the skill sets required for a data scientist role, and those required by other developers as illustrated below:

Traditional IDEs, therefore, don’t cut it for data scientists

Most IDEs include a source code editor, debugger, and compiler. They work perfectly for tech assessments for programmers and developers. Not for data science and machine learning assignments though.

In many data science problems, the solution can be a simple prediction or a ‘Yes/No’ answer. Or, if we go back to the question we started this blog with, it can be a prediction about the probability of achieving the desired goal. Is it going to rain in Atlanta tomorrow? Yes. Will my company grow 5X in the next two years? Ummm, there’s a 20% chance of doing that given you do these 10 other things well.

As we have already established, arriving at this answer requires hours of logical analysis. When assessing a data scientist for a job, therefore, recruiters and hiring managers need to be able to understand the logical choices the candidate made while arriving at the seemingly simple conclusion. A traditional IDE is not enough here.

Hence, Jupyter Notebooks.

At HackerEarth, we have seen an increasing demand for Data Science and Machine Learning - a trend reflected in our year-end recruiter survey as well. To make data science assessments easier for recruiters, we have now integrated Jupyter Notebooks on our assessment platform, which helps recruiters get right inside the mind of the candidate they are trying to hire.

The Jupyter Notebook is an open-source web application that allows users to create and share documents containing live code, equations, visualizations, and narrative text. The easy-to-use, interactive data science environment provided by Jupyter works across several programming languages such as Python and R. Jupyter Notebooks not only work like an IDE, but also as a presentation or education tool, and are great for data science assessments where the candidate is required to answer questions in a visual format.

Here are some of the ways Jupyter Notebooks score over traditional IDEs:

Individual cells for better analysis

Jupyter Notebooks allow candidates to code using separate units or ‘cells’ that can be used independently of each other while writing code (denoted by red arrows in the image below). This makes it easier for candidates to compute how various data parameters work with each other and to add notes, or to partially write and test code.

This is essential for recruiters to understand the analytical approach taken by the candidate when solving a problem.

Interactive elements for better data visualization

The Notebook offers an interactive shell with embeddable graphics and tables, reusable cells, and some other presentation features which are relevant to the job at hand. This enables candidates to present their output in a graphical format if needed; something that a traditional IDE does not support.

Enhanced candidate experience

It is well known that candidates perform better when they are using a test environment they are familiar with. Notebooks are a preferred tool in the data science world. Using the Jupyter platform for an assessment ensures that your candidate is comfortable and ready, and is approaching a problem the way they would in real life.

Better data science assessments are made of these

When the candidate starts the assignment, they are given a choice to use the Monaco editor (IDE) or Jupyter Notebooks. The Notebooks use a dedicated machine to provide enough resources to each user. Thus by ensuring a dedicated machine for every assignment our candidates take, we affirm that the candidate has no restrictions and completely feels at home. This directly translates to better candidate output in the test, and an objective skill-based assessment process.

The most interesting bit about the Jupyter Notebook integration is the output section, which not only captures the final submission in CSV format but allows recruiters to review each and every step taken by the candidate as they solved the data problem before them.

So, even if a candidate gets a Yes/No prediction wrong, you can still review their work to see how they analyzed the data - the most crucial part of a data scientist’s role.

Find better candidates with Jupyter Notebooks. Thank us later!

While data science as a field dates back to 1962 when mathematician John W. Tukey predicted the effect of modern-day electronic computing on data analysis as an empirical science. However, it reached the modern-day tech hiring lexicon only in recent years.

The trends we have seen tell us that tech jobs in AI (Artificial Intelligence), ML (Machine Learning), and Data Science would be the most in-demand roles in the future. With growing opportunities for AI and ML specialists in industries as diverse as banking, fintech, public safety, and healthcare, there will be a surge in these roles in the coming days. Today, every business big or small needs BIG DATA, and with the advent of various technologies that allow easy application of data science, all businesses are looking at using data to make their solutions smarter, their operations more efficient, and their user experiences more personalized.

This predicted surge in hiring also underlines the need to objectively assess and hire the best data scientists in the market. Traditional modes of evaluation do not do justice to the skills and expectations associated with this role. With the Jupyter notebook support on our HackerEarth Assessments platform, however, you can now assess and hire the best data scientists out there, and improve your business pipeline.

HackerEarth Deep Learning Challenge - In association with Dataquest

HackerEarth — Fri, 26 Jul 2019 06:52:27 +0000

In association with Dataquest, we present this new data science competition. Participate & win up to 350 USD plus free courses from Dataquest! Register here: http://hck.re/t3WC80

Freedom from uncertainty : HackerEarth Machine Learning Challenge

HackerEarth — Wed, 10 Jul 2019 08:57:07 +0000

Hey folks, here's interesting ML Challenge - Freedom from uncertainty, published by HackerEarth. Participate & win up to 400 USD! Register here: http://hck.re/HxbSy8