HackerEarth is committed to honoring its users’ rights to data privacy and protection. We have a privacy-conscious culture, and GDPR is an opportunity for us to strengthen this further. Being GDPR-ready has been of the highest priority this past year, and our product and legal teams have devoted a lot of extra hours to adhere to its requirements, give users more control over their data, and explain what we do with the data. (PS: To further our crusade toward data protection, we are also in the process of the getting the ISO 27001 certification.)
What is GDPR?
General Data Protection Regulation (GDPR), which will go into effect on May 25, 2018, replaces the 1995 Data Protection Directive. Designed to give EU citizens more control over their data, it aims to use one all-encompassing privacy and security law to safeguard personal data. Regardless of their location, relevant controllers or processors dealing with EU residents’ personal data are required to update or craft new policies ahead of the date or be prepared for penalties.
What is personal data?
Article 4 in GDPR definition states that ‘Personal data’ means any information relating to an identified or identifiable natural person (‘data subject’); an identifiable natural person is one who can be identified, directly or indirectly, in particular by reference to an identifier such as a name, an identification number, location data, an online identifier or to one or more factors specific to the physical, physiological, genetic, mental, economic, cultural or social identity of that natural person.Both personally identifiable information (PII) and information which can be cross-referenced with other information to identify a person is included in the definition. Examples of sensitive PII include medical information, biometric information, social security ID, license number, birth date, etc. The personal data collected should be pseudonymized and/or encrypted.
How is HackerEarth getting ready for GDPR?
In our efforts to get the organization ready for sustainable compliance, HackerEarth has taken several steps—from raising awareness in the organization about the principles of GDPR and our data protection policy to training employees to responsibly handle user data and auditing.Also, to make sure our sub-processors do no breach the regulation, we are assessing our third-party service providers and partners and fine-tuning the contracts.
Product preparation
We have assessed HackerEarth Sprint, our innovation management software, and HackerEarth Recruit, our Technical Recruitment software, against the requirements of the GDPR and have implemented features that will help users achieve compliance.Our application teams strongly believe in letting the end users exercise their rights with respect to privacy. We are working to give you more control over the data you store in our systems. These provisions may vary based on your requirement, product characteristics, and mutually agreed upon statement of work. Our teams are working on these features and enhancements, which will be rolled out in phases.How HackerEarth enables customers to be GDPR compliant:
We have revised our privacy policy and terms of service.
We are encrypting all data in transit and at rest.
We are identifying and creating multiple delete profile use cases, including administrators having the control to delete users.
HackerEarth is also taking care of many more such features to ensure the customers are compliant and users have complete control over their data.
Process preparation
Based on our data flows and data handling practices, we have revised our privacy policy and added further information on the personal information we collect, why we collect it, how we will use it, how long we will store it, and so on. Moreover, we are reviewing our databases to make sure we have only the latest and most accurate information.We have put together a glossary of the terms and information on when HackerEarth acts as a data processor or a data controller. Additionally, we have appointed internal privacy champions for all our teams.
What happens in the event of a data breach?
In case a personal data breach occurs, we will send breach notifications in accordance with our internal incident response policy.We will notify our customers within 72 hours of us discovering the breach. We will notify users through our blogs and social media for general incidents. We will notify the concerned party through email (using the primary email address) for incidents specific to an individual user or an organization.
We have a whole series of blogs planned, with more updates and information to come. Please feel free to ask questions and share your concerns with us at vr-gdpr@hackerearth.com.
This is a series of blogs dedicated to different data visualization techniques used in various domains of machine learning. Data Visualization is a critical step for building a powerful and efficient machine learning model. It helps us to better understand the data, generate better insights for feature engineering, and, finally, make better decisions during modeling and training of the model.
For this blog, we will use the seaborn and matplotlib libraries to generate the visualizations. Matplotlib is a MATLAB-like plotting framework in python, while seaborn is a python visualization library based on matplotlib. It provides a high-level interface for producing statistical graphics. In this blog, we will explore different statistical graphical techniques that can help us in effectively interpreting and understanding the data. Although all the plots using the seaborn library can be built using the matplotlib library, we usually prefer the seaborn library because of its ability to handle DataFrames.
We will start by importing the two libraries. Here is the guide to installing the matplotlib library and seaborn library. (Note that I’ll be using matplotlib and seaborn libraries interchangeably depending on the plot.)
### Importing necessary library
import random
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
Simple Plot
Let’s begin by plotting a simple line plot which is used to plot a mathematical. A line plot is used to plot the relationship or dependence of one variable on another. Say, we have two variables ‘x’ and ‘y’ with the following values:
To plot the relationship between the two variables, we can simply call the plot function.
### Creating a figure to plot the graph.
fig, ax = plt.subplots()
ax.plot(x, y)
ax.set_xlabel('X data')
ax.set_ylabel('Y data')
ax.set_title('Relationship between variables X and Y')
plt.show() # display the graph
### if %matplotlib inline has been invoked already, then plt.show() is automatically invoked and the plot is displayed in the same window.
Fig. 1. Line Plot between X and Y
Here, we can see that the variables ‘x’ and ‘y’ have a sinusoidal relationship. Generally, .plot() function is used to find any mathematical relationship between the variables.
Histogram
A histogram is one of the most frequently used data visualization techniques in machine learning. It represents the distribution of a continuous variable over a given interval or period of time. Histograms plot the data by dividing it into intervals called ‘bins’. It is used to inspect the underlying frequency distribution (eg. Normal distribution), outliers, skewness, etc.
Let’s assume some data ‘x’ and analyze its distribution and other related features.
### Let 'x' be the data with 1000 random points.
x = np.random.randn(1000)
Let’s plot a histogram to analyze the distribution of ‘x’.
plt.hist(x)
plt.xlabel('Intervals')
plt.ylabel('Value')
plt.title('Distribution of the variable x')
plt.show()
Fig 2. Histogram showing the distribution of the variable ‘x’.
The above plot shows a normal distribution, i.e., the variable ‘x’ is normally distributed. We can also infer that the distribution is somewhat negatively skewed. We usually control the ‘bins’ parameters to produce a distribution with smooth boundaries. For example, if we set the number of ‘bins’ too low, say bins=5, then most of the values get accumulated in the same interval, and as a result they produce a distribution which is hard to predict.
plt.hist(x, bins=5)
plt.xlabel('Intervals')
plt.ylabel('Value')
plt.title('Distribution of the variable x')
plt.show()
Fig 3. Histogram with low number of bins.
Similarly, if we increase the number of ‘bins’ to a high value, say bins=1000, each value will act as a separate bin, and as a result the distribution seems to be too random.
plt.hist(x, bins=1000)
plt.xlabel('Intervals')
plt.ylabel('Value')
plt.title('Distribution of the variable x')
plt.show()
Fig. 4. Histogram with a large number of bins.
Kernel Density Function
Before we dive into understanding KDE, let’s understand what parametric and non-parametric data are.
Parametric Data: When the data is assumed to have been drawn from a particular distribution and some parametric test can be applied to it
Non-Parametric Data: When we have no knowledge about the population and the underlying distribution
Kernel Density Function is the non-parametric way of representing the probability distribution function of a random variable. It is used when the parametric distribution of the data doesn’t make much sense, and you want to avoid making assumptions about the data.
The kernel density estimator is the estimated pdf of a random variable. It is defined as
Similar to histograms, KDE plots the density of observations on one axis with height along the other axis.
### We will use the seaborn library to plot KDE.
### Let's assume random data stored in variable 'x'.
fig, ax = plt.subplots()
### Generating random data
x = np.random.rand(200)
sns.kdeplot(x, shade=True, ax=ax)
plt.show()
Fig 5. KDE plot for the random variable ‘x’.
Distplot combines the function of the histogram and the KDE plot into one figure.
### Generating a random sample
x = np.random.random_sample(1000)
### Plotting the distplot
sns.distplot(x, bins=20)
Fig 6. Displot for the random variable ‘x’.
So, the distplot function plots the histogram and the KDE for the sample data in the same figure. You can tune the parameters of the displot to only display the histogram or kde or both. Distplot comes in handy when you want to visualize how close your assumption about the distribution of the data is to the actual distribution.
Scatter Plot
Scatter plots are used to determine the relationship between two variables. They show how much one variable is affected by another. It is the most commonly used data visualization technique and helps in drawing useful insights when comparing two variables. The relationship between two variables is called correlation. If the data points fit a line or curve with a positive slope, then the two variables are said to show positive correlation. If the line or curve has a negative slope, then the variables are said to have a negative correlation.
A perfect positive correlation has a value of 1 and a perfect negative correlation has a value of -1. The closer the value is to 1 or -1, the stronger the relationship between the variables. The closer the value is to 0, the weaker the correlation.
For our example, let’s define three variables ‘x’, ‘y’, and ‘z’, where ‘x’ and ‘z’ are randomly generated data and ‘y’ is defined as We will use a scatter plot to find the relationship between the variables ‘x’ and ‘y’.
### Let's define the variables we want to find the relationship between.
x = np.random.rand(500)
z = np.random.rand(500)
### Defining the variable 'y'
y = x * (z + x)
fig, ax = plt.subplots()
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_title('Scatter plot between X and Y')
plt.scatter(x, y, marker='.')
plt.show()
Fig 7. Scatter plot between X and Y.
From the figure above we can see that the data points are very close to each other and also if we fit a curve, along with the points, it will have a positive slope. Therefore, we can infer that there is a strong positive correlation between the values of the variable ‘x’ and variable ‘y’.
Also, we can see that the curve that best fits the graph is quadratic in nature and this can be confirmed by looking at the definition of the variable ‘y’.
Joint Plot
Jointplot is seaborn library specific and can be used to quickly visualize and analyze the relationship between two variables and describe their individual distributions on the same plot.
Let’s start with using joint plot for producing the scatter plot.
### Defining the data.
mean, covar = [0, 1], [[1, 0,], [0, 50]]
### Drawing random samples from a multivariate normal distribution.
### Two random variables are created, each containing 500 values, with the given mean and covariance.
data = np.random.multivariate_normal(mean, covar, 500)
### Storing the variables in a dataframe.
df = pd.DataFrame(data=data, columns=['X', 'Y'])
### Joint plot between X and Y
sns.jointplot(df.X, df.Y, kind='scatter')
plt.show()
Fig 8. Joint plot (scatter plot) between X and Y.
Next, we can use the joint point to find the best line or curve that fits the plot.
sns.jointplot(df.X, df.Y, kind='reg')
plt.show()
Fig 9. Using joint plot to plot the regression line that best fits the data points.
Apart from this, jointplot can also be used to plot ‘kde’, ‘hex plot’, and ‘residual plot’.
PairPlot
We can use scatter plot to plot the relationship between two variables. But what if the dataset has more than two variables (which is quite often the case), it can be a tedious task to visualize the relationship between each variable with the other variables.
The seaborn pairplot function does the same thing for us and in just one line of code. It is used to plot multiple pairwise bivariate (two variable) distribution in a dataset. It creates a matrix and plots the relationship for each pair of columns. It also draws a univariate distribution for each variable on the diagonal axes.
### Loading a dataset from the sklearn toy datasets
from sklearn.datasets import load_linnerud
### Loading the data
linnerud_data = load_linnerud()
### Extracting the column data
data = linnerud_data.data
Sklearn stores data in the form of a numpy array and not data frames, thereby storing the data in a dataframe.
### Creating a dataframe
data = pd.DataFrame(data=data, columns=diabetes_data.feature_names)
### Plotting a pairplot
sns.pairplot(data=data)
Fig 10. Pair plot showing the relationships between the columns of the dataset.
So, in the graph above, we can see the relationships between each of the variables with the other and thus infer which variables are most correlated.
Conclusion
Visualizations play an important role in data analysis and exploration. In this blog, we got introduced to different kinds of plots used for data analysis of continuous variables. Next week, we will explore the various data visualization techniques that can be applied to categorical variables or variables with discrete values. Next, I encourage you to download the iris dataset or any other dataset of your choice and apply and explore the techniques learned in this blog.
Have anything to say? Feel free to comment below for any questions, suggestions, and discussions related to this article. Till then, Sayōnara.
The meteoric rise of artificial intelligence in the last decade has spurred a huge demand for AI and ML skills in today’s job market. ML-based technology is now used in almost every industry vertical from finance to healthcare. In this blog, we have compiled a list of best frameworks and libraries that you can use to build machine learning models.
1) TensorFlow Developed by Google, TensorFlow is an open-source software library built for deep learning or artificial neural networks. With TensorFlow, you can create neural networks and computation models using flowgraphs. It is one of the most well-maintained and popular open-source libraries available for deep learning. The TensorFlow framework is available in C++ and Python. Other similar deep learning frameworks that are based on Python include Theano, Torch, Lasagne, Blocks, MXNet, PyTorch, and Caffe. You can use TensorBoard for easy visualization and see the computation pipeline. Its flexible architecture allows you to deploy easily on different kinds of devices On the negative side, TensorFlow does not have symbolic loops and does not support distributed learning. Further, it does not support Windows.
2)Theano Theano is a Python library designed for deep learning. Using the tool, you can define and evaluate mathematical expressions including multi-dimensional arrays. Optimized for GPU, the tool comes with features including integration with NumPy, dynamic C code generation, and symbolic differentiation. However, to get a high level of abstraction, the tool will have to be used with other libraries such as Keras, Lasagne, and Blocks. The tool supports platforms such as Linux, Mac OS X, and Windows.
3) Torch The Torch is an easy to use open-source computing framework for ML algorithms. The tool offers an efficient GPU support, N-dimensional array, numeric optimization routines, linear algebra routines, and routines for indexing, slicing, and transposing. Based on a scripting language called Lua, the tool comes with an ample number of pre-trained models. This flexible and efficient ML research tool supports major platforms such as Linux, Android, Mac OS X, iOS, and Windows.
4) Caffe Caffe is a popular deep learning tool designed for building apps. Created by Yangqing Jia for a project during his Ph.D. at UC Berkeley, the tool has a good Matlab/C++/ Python interface. The tool allows you to quickly apply neural networks to the problem using text, without writing code. Caffe partially supports multi-GPU training. The tool supports operating systems such as Ubuntu, Mac OS X, and Windows.
5) Microsoft CNTK Microsoft cognitive toolkit is one of the fastest deep learning frameworks with C#/C++/Python interface support. The open-source framework comes with powerful C++ API and is faster and more accurate than TensorFlow. The tool also supports distributed learning with built-in data readers. It supports algorithms such as Feed Forward, CNN, RNN, LSTM, and Sequence-to-Sequence. The tool supports Windows and Linux.
6) Keras Written in Python, Keras is an open-source library designed to make the creation of new DL models easy. This high-level neural network API can be run on top of deep learning frameworks like TensorFlow, Microsoft CNTK, etc. Known for its user-friendliness and modularity, the tool is ideal for fast prototyping. The tool is optimized for both CPU and GPU.
7) SciKit-Learn SciKit-Learn is an open-source Python library designed for machine learning. The tool based on libraries such as NumPy, SciPy, and matplotlib can be used for data mining and data analysis. SciKit-Learn is equipped with a variety of ML models including linear and logistic regressors, SVM classifiers, and random forests. The tool can be used for multiple ML tasks such as classification, regression, and clustering. The tool supports operating systems like Windows and Linux. On the downside, it is not very efficient with GPU.
8)Accord.NET Written in C#, Accord.NET is an ML framework designed for building production-grade computer vision, computer audition, signal processing and statistics applications. It is a well-documented ML framework that makes audio and image processing easy. The tool can be used for numerical optimization, artificial neural networks, and visualization. It supports Windows.
9)Spark MLIib Apache Spark’s MLIib is an ML library that can be used in Java, Scala, Python, and R. Designed for processing large-scale data, this powerful library comes with many algorithms and utilities such as classification, regression, and clustering. The tool interoperates with NumPy in Python and R libraries. It can be easily plugged into Hadoop workflows.
10) Azure ML Studio Azure ML studio is a modern cloud platform for data scientists. It can be used to develop ML models in the cloud. With a wide range of modeling options and algorithms, Azure is ideal for building larger ML models. The service provides 10GB of storage space per account. It can be used with R and Python programs.
11) Amazon Machine Learning Amazon Machine Learning (AML) is an ML service that provides tools and wizards for creating ML models. With visual aids and easy-to-use analytics, AML aims to make ML more accessible to developers. AML can be connected to data stored in Amazon S3, Redshift, or RDS.
Machine learning frameworks come with pre-built components that are easy to understand and code. A good ML framework thus reduces the complexity of defining ML models. With these open-source ML frameworks, you build your ML models easily and quickly.
Know an ML framework that should be on this list? Share them in comments below.
Data visualization is helping companies worldwide to identify patterns, predict outcomes, and improve business returns. Visualization is an important aspect of data analysis. Simply put, data visualization conveys outcomes of tabular or spatial data in a visual format. Images have the power to capture attention and convey ideas clearly. This aids decision making and drives action for improvements.
With the use of the right tools, you can sketch a convincing visual story from your raw data. Here are some free and open source tools for data visualization:
If you know Javascript, then you can use this open source tool to make rich data visualizations. Candela is an open-source suite of interoperable web visualization components.
Charted is a free data visualization tool that lets you create line graphs or bar charts from CSV files and Google spreadsheets. The toll comes with integrated components including LineUp component, UpSet component, OnSet component, Vega visualizations, and GeoJS geospatial visualizations. The tool does not store the data or manipulate it. Focused purely on visualization, it comes with basic features to create a line or stacked charts with labels and notes.
Datawrapper is a mobile-friendly data visualization tool that lets you create charts and reports in seconds. The free version of the tool meant for a single user supports 10,000 monthly chart views. Using the tool, you can create different types of visualization such as bar chart, split chart, stacked chart, dot plot, arrow plot, area chart, scatter plot, symbol map, and choropleth map. You don’t need coding or designing skills to use the tool.
Google’s data visualization tool is free and easy to set up if you have a Gmail account. You can connect it easily with Google products such as Google AdWords, Google Analytics, YouTube Analytics, and Google Sheets.
Another simple and free data visualization tool by Google is the Google chart tool. The tool comes with interactive charts and data tools for visualization.
The leaflet is an open-source JavaScript library that allows you to make mobile-friendly interactive maps. The tool has a lot of plugins for adding features and works well on various desktop and mobile platforms.
MyHeatMap is a free tool to view your geographic data interactively. The free version of the tool offers only public maps and you can add only 20 data points for each of those free maps. The tool makes it easy to understand the data with color-coded heat maps. You can also switch between data sets within the same map.
This free tool lets you turn your spreadsheet into a map. You can upload your CSV file or Google sheet to create an interactive online map in seconds. The tool can be used to explain data like customer demographics by zip codes.
Palladio is a free tool designed to visualize complex historical data. It comes with features like map view, graph view, list view and gallery view. You can use the tool to visualize data in CSV, TAB, or TSV files. With the graph view, you can visualize the relationship between dimensions of your data. The data is displayed as nodes connected by lines. The list view, on the other hand, allows you to arrange data to make customized lists. The tool also has a gallery view to display data within a grid.
RawGraphs is an open-source platform that helps you visualize TSV, CSV, DSV, or JSON data. The free tool is simple to use and helps in converting data to charts.
Tableau Public is a free business intelligence tool that allows users to create and share interactive charts, graphs, maps, and app. The free version of the tool comes with 10 GB of storage. You can connect it to data sources like Google Sheets, Microsoft Excel, Text files, JSON files, Spatial files, Web Data Connectors, OData, and statistical files such as SAS (*.sas7bdat), SPSS (*.sav), and R (*.rdata, *.rda).
A timeline is a free tool that allows you to create timelines for reports. You can connect your Google Drive account to create a timeline from Google Spreadsheet using the templates given in the tool. Using JSON, you can create custom installations.
Chartist.js is a free data visualization that allows you to create responsive charts fast and easy.
The tool offers great flexibility and is customizable. You can even use CSS animations and transitions to your SVG elements.
ColorBrewer is a free tool that can be used to make your maps better in terms of color schemes. The tool makes it easy to differentiate colors on a complex map.
D3.JS is a free JavaScript library that helps you create images using data. The tool enables you to connect arbitrary data to a Document Object Model (DOM), and then apply data-driven transformations to the document. With DOM programming API, programmers can access documents as objects.
Plotly is an open-source tool that allows you to compose, edit and share interactive data visualizations. You can use the tool to create D3.js charts and maps by uploading CSV files or connecting to the SQL database. You can also create charts with R or Python.
Polymaps is a free javascript library for creating dynamic, interactive maps in browsers. You can use the tool to get a display of multi-zoom datasets over maps. The tool uses scalable vector graphics (SVG) to display images, thus enabling you to define the design using CSS.
The Weave is a free data visualization platform that is ADA-compliant. The tool comes with a full keyboard and assistive device navigation and complete screen reader support. The tool also automatically gives descriptions of the images in real time.
Dygraphs is an open-source charting library based on JavaScript. This free tool can be used to analyze dense data sets. The tool is highly customizable and works well in all browsers. The tool offers strong support for error bars/ confidence intervals.
Apart from these, there are many data visualization tools that offer a free trial for a limited time GanttPro, a project management tool, for instance, helps you create charts for projects for free during their 15-day trial period.
GanttPro
Data visualization is crucial for accurate data analysis. With the right tools in hand, you can easily summarize and explain complex data to your stakeholders. By leveraging actionable insights generated from data, companies can make big profits and savings. Just how big are we talking about? Netflix saved around $1 billion in 2017 with its ML algorithm that recommends personalized TV shows and movies to subscribers. When used right, data analysis and visualization have the power to change the way people live their lives.
Know a great open source tool for data visualization? Share it in comments below.
“We didn’t do anything wrong, but somehow, we lost,” said Nokia’s CEO Stephen Elop in his speech soon after Nokia’s announcement of being acquired by Microsoft in September 2013. The mobile giant that was once valued at $222 billion at its peak was acquired for just $7.2 billion by Microsoft that year. Failure to adapt to new trends in smartphone technology drove the legendary mobile company from market domination to sell-off.
The biggest lesson for from Nokia’s collapse in the mobile industry is to adapt to new tech trends before your consumers abandon you. Fast innovation is the key to keeping up with new technologies. By discerning the latest trends, companies can leverage the right technology and adapt in time to succeed. Here are the top tech trends that will define the IT landscape in 2018:
1) The incredible AI
Artificial intelligence (AI) is going to get better and smarter in 2018. Thanks to artificial intelligence, your everyday appliances from the security system to entertainment console will become more automated and smarter. Using software algorithms and sensors, artificial intelligence based devices will be able to do more complex actions and will play a significant role in several domains including healthcare, smart cars, and personal security. From making the financial sector more accurate and secure to powering smart personal assistants like Siri, you will see more of artificial intelligence based technology in your everyday life.
2) Planet of the robots
With more research and investment in robotics, you will also see more of intelligent drones and robots in 2018. According to IDC’s FutureScape: Worldwide Robotics 2017 Predictions report, 45% of the 200 leading global e-commerce and omnichannel commerce companies will deploy robotics systems in their order fulfillment, warehousing, and delivery operations. 2018 is going to witness the emergence of robotics in fields including agriculture, manufacturing, and medicine. Lifelike robots like Sophia could even play the role of human companions and help take care of children, the elderly and people with special needs. Sophia who looks like the late actor Audrey Hepburn was even awarded full citizenship of Saudi Arabia. Those worried about a robotic invasion can put their fears to rest. Robotics of the future will be designed to help people achieve more.
3) Mixed reality: The rise of immersive experience
With the digital revolution, today we have access to more content than ever before. Earlier, augmented reality (AR) and virtual reality (VR) defined the way people interacted with the digital world. The coming days will see the emergence of mixed reality (MR) that combines both augmented reality and virtual reality. The mixed reality technology enables users to interact in an environment where both physical and digital objects co-exist. 2018 will witness more application of the mixed reality technology in fields ranging from simulation-based learning, military training, aviation, healthcare to interactive product content management. According to the research firm Reportbuyer.com, the global mixed reality market size is expected to touch $2.8 billion by 2023.
4) Blockchain: The force awakens
The blockchain is the fundamental technology behind cryptocurrencies like bitcoin. The exponential rise of bitcoins has rekindled everyone’s interest in blockchain technology. The technology provides a secure way of sharing encrypted data on anything, from money to medical records, between companies, people, and institutions. Blockchain has the potential to revolutionize the financial sector and the world economy. In 2018, companies are going to invest heavily in developing their blockchain and fintech capabilities. As we move toward a digital, technologically advanced financial world, blockchain will play a crucial role in making our financial systems faster, more secure and efficient.
5) The age of machine learning
Businesses will increasingly leverage on machine learning to gain a competitive advantage in 2018. Machine learning deals with the technology that enables computers to learn explicitly without being programmed. The technology can be used to analyze large volumes of data and predict patterns. In the coming days, machine learning will be widely used in fields including data security, personal security, financial trading, healthcare, personalized marketing and online search.
6) IoT: Unchained
IoT is here to stay and thrive. According to Business Insider, the business spends on IoT solutions will reach $6 trillion by 2021. Internet of Things (IoT) is a network of devices that is embedded with software and sensors that enable these devices to connect and exchange data. The technology is applied in verticals including wearables, connected cars, connected homes, connected cities and industrial internet. 2018 will see the rise of “digital twins,” the next step in IoT-based technology. Companies will rely on the technology to predict problems through data analysis and simulations. Another disruptive technology that will emerge will be based on a combination of IoT and Blockchain technologies. The technology will be applied in domains including warehousing, healthcare, and financial sectors. There will be connected devices everywhere.
As the waves of new and emerging technologies hit the world, it is a good idea to start learning more about these new and upcoming tech domains. In today’s high-tech era, leveraging the right technology can mean the difference between success and failure. Consumers are quick to punish those who don’t innovate fast.
Nokia did not do anything wrong except miss out on the latest trends in mobile technology. While the competitors quickly caught on the demand for smarter phones, Nokia was left far behind. In the end, it is the lack of innovation and the failure to adapt to emerging technologies that force companies out of business. The lesson for everyone is to adapt and innovate before it is too late.
When it comes to landing a high-paying job, the thing that counts the most is the practical skills you possess. In a dynamic and global industry like software, mastering the right skills at the right time is crucial. Here is a list of the most highly paid tech skills that you can pick up this New Year to boost your resume and your bank balance:
1) Data science skills
The demand for talented professionals in data science is on the rise. From the banking sector to retail, companies across sectors are vying to recruit candidates with talent in data science. Learning programming languages, like R, and SAS in data science is must-do. R language is the most in-demand data science skill that is useful for advanced statistical analysis and visualization. According to Glassdoor, Data Scientists earn close to $110,000 a year.
2) Mobile application development skills
You can make big money with your ideas for mobile apps. Don’t worry if you are new to the field. For those with a passion to build new products, app development projects are a great way to learn and build the skill portfolio. To be successful in the field, developers need to master programming languages like Java, Python, Swift, C++, C#, and, Objective C. Experience in UI and UX will give you an added edge.
With languages like C++, C#, and Objective C, you can create all types of mobile apps. Swift is more popular for building iOS apps. Additionally, learning programming languages like Java and Python can also help in automation and data crawling. As a language that can be run on any platform, Java is one of the most sought-after programming language used by more than 90% of Fortune 500 companies to build apps. If you know basic Java programming, you can quite easily create Android-based apps. The best way to find highly paid jobs in Java is by participating in hackathons and challenges conducted by recruiting companies. Find out recruiters hire Java developers here.
3) Data visualization skills
With the growth of machine learning and big data, the demand for skills in data analysis is expected to grow. This includes expertise in languages like SQL. Structured Query Language (SQL) is a specialized programming language designed for managing enterprise database. Knowledge of SQL can land you a high paid job as a business analyst or data analyst. Moreover, you can easily create summarize large volumes of business data. Learn more about SQL here.
4) SaaS and cloud computing skills
As more companies shift to cloud, the demand for professionals with skills in cloud computing and software as a service (SaaS) will grow. With SaaS programming skills, you can make over $50 an hour as a freelancer. To profit from the reap the benefits, add specific skills like Amazon Web Services, Microsoft Hyper-V, and VMware to your list for the year. Also, you can gain experience with DevOps, containers, cloud stack, and IPv6 to find well-paid software projects.
5) Cybersecurity skills
Protecting the database, networks, and applications from cyber attacks is not an easy job. Every company strives for a robust system to prevent costly security breaches. Skills in secure software development, intrusion detection, and attack mitigation are quite coveted. To become an expert, you can study about firewalls and scripting languages, and get a deep understanding of networks and operating systems. As a specialist in cybersecurity, you not only make big bucks, you also contribute effectively to building a safer world.
These 5 tech skills can make you richer this New Year. The key to a successful career in IT is to adapt to new technology and make learning new skills, a habit.
Want to add new programming skills to your resume? Get started with our practice section to learn the latest programming languages and practice your skills with interesting problem sets:
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The last couple of years transformed how the world works and the tech industry is no exception. Remote work, a candidate-driven market, and automation are some of the tech recruiting trends born out of the pandemic.
While accepting the new reality and adapting to it is the first step, keeping up with continuously changing hiring trends in technology is the bigger challenge right now.
What does 2024 hold for recruiters across the globe? What hiring practices would work best in this post-pandemic world? How do you stay on top of the changes in this industry?
The answers to these questions will paint a clearer picture of how to set up for success while recruiting tech talent this year.
7 tech recruiting trends for 2024
Recruiters, we’ve got you covered. Here are the tech recruiting trends that will change the way you build tech teams in 2024.
Trend #1—Leverage data-driven recruiting
Data-driven recruiting strategies are the answer to effective talent sourcing and a streamlined hiring process.
Talent acquisition leaders need to use real-time analytics like pipeline growth metrics, offer acceptance rates, quality and cost of new hires, and candidate feedback scores to reduce manual work, improve processes, and hire the best talent.
The key to capitalizing on talent market trends in 2024 is data. It enables you to analyze what’s working and what needs refinement, leaving room for experimentation.
Having a strong employer brand that supports a clear Employer Value Proposition (EVP) is crucial to influencing a candidate’s decision to work with your company. Perks like upskilling opportunities, remote work, and flexible hours are top EVPs that attract qualified candidates.
A clear EVP builds a culture of balance, mental health awareness, and flexibility—strengthening your employer brand with candidate-first policies.
Trend #3—Focus on candidate-driven market
The pandemic drastically increased the skills gap, making tech recruitment more challenging. With the severe shortage of tech talent, candidates now hold more power and can afford to be selective.
Competitive pay is no longer enough. Use data to understand what candidates want—work-life balance, remote options, learning opportunities—and adapt accordingly.
Recruiters need to think creatively to attract and retain top talent.
Trend #4—Have a diversity and inclusion oriented company culture
Diversity and inclusion have become central to modern recruitment. While urgent hiring can delay D&I efforts, long-term success depends on inclusive teams. Our survey shows that 25.6% of HR professionals believe a diverse leadership team helps build stronger pipelines and reduces bias.
McKinsey’s Diversity Wins report confirms this: top-quartile gender-diverse companies see 25% higher profitability, and ethnically diverse teams show 36% higher returns.
It's refreshing to see the importance of an inclusive culture increasing across all job-seeking communities, especially in tech. This reiterates that D&I is a must-have, not just a good-to-have.
Trend #5—Embed automation and AI into your recruitment systems
With the rise of AI tools like ChatGPT, automation is being adopted across every business function—including recruiting.
Manual communication with large candidate pools is inefficient. In 2024, recruitment automation and AI-powered platforms will automate candidate nurturing and communication, providing a more personalized experience while saving time.
Remote interviews expand access to global talent, reduce overhead costs, and increase flexibility—making the hiring process more efficient for both recruiters and candidates.
Trend #7—Be proactive in candidate engagement
Delayed responses or lack of updates can frustrate candidates and impact your brand. Proactive communication and engagement with both active and passive candidates are key to successful recruiting.
As recruitment evolves, proactive candidate engagement will become central to attracting and retaining talent. In 2023 and beyond, companies must engage both active and passive candidates through innovative strategies and technologies like chatbots and AI-powered systems. Building pipelines and nurturing relationships will enhance employer branding and ensure long-term hiring success.
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Recruiting qualified tech talent is tough—but we’re here to help. HackerEarth for Enterprises offers an all-in-one suite that simplifies sourcing, assessing, and interviewing developers.
Staying ahead of tech recruiting trends, improving hiring processes, and adapting to change is the way forward in 2024. Take note of the tips in this article and use them to build a future-ready hiring strategy.
The first part of this blog stresses the importance of asking the right technical interview questions to assess a candidate’s coding skills. But that alone is not enough. If you want to hire the crème de la crème of the developer talent out there, you have to look for a well-rounded candidate.
Honest communication, empathy, and passion for their work are equally important as a candidate’s technical knowledge. Soft skills are like the cherry on top. They set the best of the candidates apart from the rest.
Re-examine how you are vetting your candidates. Identify the gaps in your interviews. Once you start addressing these gaps, you find developers who have the potential to be great. And those are exactly the kind of people that you want to work with!
Let’s get to it, shall we?
What constitutes a good interview question?
An ideal interview should reveal a candidate’s personality along with their technical knowledge. To formulate a comprehensive list of questions, keep in mind three important characteristics.
Questions are open-ended – questions like, “What are some of the programming languages you’re comfortable with,” instead of “Do you know this particular programming language” makes the candidate feel like they’re in control. It is also a chance to let them reply to your question in their own words.
They address the behavioral aspects of a candidate – ensure you have a few questions on your list that allow a candidate to describe a situation. A situation where a client was unhappy or a time when the developer learned a new technology. Such questions help you assess if the candidate is a good fit for the team.
There is no right or wrong answer – it is important to have a structured interview process in place. But this does not mean you have a list of standard answers in mind that you’re looking for. How candidates approach your questions shows you whether they have the makings of a successful candidate. Focus on that rather than on the actual answer itself.
Designing a conversation around these buckets of interview questions brings you to my next question, “What should you look for in each candidate to spot the best ones?”
Hire GREAT developers by asking the right questions
Before we dive deep into the interview questions, we have to think about a few things that have changed. COVID-19 has rendered working from home the new normal for the foreseeable future. As a recruiter, the onus falls upon you to understand whether the developer is comfortable working remotely and has the relevant resources to achieve maximum productivity.
#1 How do you plan your day?
Remote work gives employees the option to be flexible. You don’t have to clock in 9 hours a day as long as you get everything done on time. A developer who hasn’t always been working remotely, but has a routine in place, understands the pitfalls of working from home. It is easy to get distracted and having a schedule to fall back on ensures good productivity.
#2 Do you have experience using tools for collaboration and remote work?
Working from home reduces human interaction heavily. There is no way to just go up to your teammate’s desk and clarify issues. Virtual communication is key to getting work done. Look for what kind of remote working tools your candidate is familiar with and if they know what collaborative tools to use for different tasks.
Value-based interview questions to ask
We went around and spoke to our engineering team, and the recruiting team to see what questions they abide by; what they think makes any candidate tick.
The result? – a motley group of questions that aim to reveal the candidate’s soft skills, in addition to typical technical interview questions and test tasks.
#3 Please describe three recent projects that you worked on. What were the most interesting and challenging parts?
This is an all-encompassing question in that it lets the candidate explain at length about their work ethic—thought process, handling QA, working with a team, and managing user feedback. This also lets you dig enough to assess whether the candidate is taking credit for someone else's work or not.
#4 You’ve worked long and hard to deliver a complex feature for a client and they say it’s not what they asked for. How would you take it?
A good developer will take it in their stride, work closely with the client to find the point of disconnect, and sort out the issue. There are so many things that could go wrong or not be to the client’s liking, and it falls on the developer to remain calm and create solutions.
#5 What new programming languages or technologies have you learned recently?
While being certified in many programming languages doesn't guarantee a great developer, it still is an important technical interview question to ask. It helps highlight a thirst for knowledge and shows that the developer is eager to learn new things.
#6 What does the perfect release look like? Who is involved and what is your role?
Have the developer take you through each phase of a recent software development lifecycle. Ask them to explain their specific role in each phase in this release. This will give you an excellent perspective into a developer’s mind. Do they talk about the before and after of the release? A skilled developer would. The chances of something going wrong in a release are very high. How would the developer react? Will they be able to handle the pressure?
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#7 Tell me about a time when you had to convince your lead to try a different approach?
As an example of a behavioral interview question, this is a good one. The way a developer approaches this question speaks volumes about how confident they are expressing their views, and how succinct they are in articulating those views.
#8 What have you done with all the extra hours during the pandemic?
Did you binge-watch your way through the pandemic? I’m sure every one of us has done this. Indulge in a lighthearted conversation with your candidate. This lets them talk about something they are comfortable with. Maybe they learned a new skill or took up a hobby. Get to know a candidate’s interests and little pleasures for a more rounded evaluation.
Over to you! Now that you know what aspects of a candidate to focus on, you are well-equipped to bring out the best in each candidate in their interviews. A mix of strong technical skills and interpersonal qualities is how you spot good developers for your team.
If you have more pressing interview questions to add to this list of ours, please write to us at contact@hackerearth.com.
The minute a developer position opens up, recruiters feel a familiar twinge of fear run down their spines. They recall their previous interview experiences, and how there seems to be a blog post a month that goes viral about bad developer interviews.
While hiring managers, especially the picky ones, would attribute this to a shortage of talented developers, what if the time has come to rethink your interview process? What if recruiters and hiring managers put too much stock into bringing out the technical aspects of each candidate and don’t put enough emphasis on their soft skills?
A report by Robert Half shows that 86% of technology leaders say it’s challenging to find IT talent. Interviewing developers should be a rewarding experience, not a challenging one. If you don’t get caught up in asking specific questions and instead design a simple conversation to gauge a candidate’s way of thinking, it throws up a lot of good insight and makes it fun too.
Asking the right technical interview questions when recruiting developers is important but so is clear communication, good work ethic, and alignment with your organization’s goals.
Let us first see what kind of technical interview questions are well-suited to revealing the coding skills and knowledge of any developer, and then tackle the behavioral aspects of the candidate that sets them apart from the rest.
Recruit GREAT developers by asking the right questions
Here are some technical interview questions that you should ask potential software engineers when interviewing.
#1 Write an algorithm for the following
Minimum Stack - Design a stack that provides 4 functions - push(item), pop, peek, and minimum, all in constant order time complexity. Then move on to coding the actual solution.
Kth Largest Element in an array - This is a standard problem with multiple solutions of best time complexity orders where N log(K) is a common one and O(N) + K log(N) is a lesser-known order. Both solutions are acceptable, not directly comparable to each other, and better than N log(N), which is sorting an array and fetching the Kth element.
Top View of a Binary Tree - Given a root node of the binary tree, return the set of all elements that will get wet if it rains on the tree. Nodes having any nodes directly above them will not get wet.
Internal implementation of a hashtable like a map/dictionary - A candidate needs to specify how key-value pairs are stored, hashing is used and collisions are handled. A good developer not only knows how to use this concept but also how it works. If the developer also knows how the data structure scales when the number of records increases in the hashtable, that is a bonus.
Algorithms demonstrate a candidate’s ability to break down a complex problem into steps. Reasoning and pattern recognition capabilities are some more factors to look for when assessing a candidate. A good candidate can code his thought process of the algorithm finalized during the discussion.
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#2 Formulate solutions for the below low-level design (LLD) questions
What is LLD? In your own words, specify the different aspects covered in LLD.
Design a movie ticket booking application like BookMyShow. Ensure that your database schema is tailored for a theatre with multiple screens and takes care of booking, seat availability, seat arrangement, and seat locking. Your solution does not have to extend to the payment option.
Design a basic social media application. Design database schema and APIs for a platform like Twitter with features for following a user, tweeting a post, seeing your tweet, and seeing a user's tweet.
Such questions do not have a right or wrong answer. They primarily serve to reveal a developer’s thought process and the way they approach a problem.
What do you understand by HLD? Can you specify the difference between LLD and HLD?
Design a social media application. In addition to designing a platform like Twitter with features for following a user, tweeting a post, seeing your tweet, and seeing a user's tweet, design a timeline. After designing a timeline where you can see your followers’ tweets, scale it for a larger audience. If you still have time, try to scale it for a celebrity use case.
Design for a train ticket booking application like IRCTC. Incorporate auth, features to choose start and end stations, view available trains and available seats between two stations, save reservation of seats from start to end stations, and lock them till payment confirmation.
How will you design a basic relational database? The database should support tables, columns, basic field types like integer and text, foreign keys, and indexes. The way a developer approaches this question is important. A good developer designs a solution around storage and memory management.
Here’s a pro-tip for you. LLD questions can be answered by both beginners and experienced developers. Mostly, senior developers can be expected to answer HLD questions. Choose your interview questions set wisely, and ask questions relevant to your candidate’s experience.
#4 Have you ever worked with SQL? Write queries for a specific use case that requires multiple joins.
Example: Create a table with separate columns for student name, subject, and marks scored. Return student names and ranks of each student. The rank of a student depends on the total of marks in all subjects.
Not all developers would have experience working with SQL but some knowledge about how data is stored/structured is useful. Developers should be familiar with simple concepts like joins, retrieval queries, and the basics of DBMS.
#5 What do you think is wrong with this code?
Instead of asking developer candidates to write code on a piece of paper (which is outdated, anyway), ask them to debug existing code. This is another way to assess their technical skills. Place surreptitious errors in the code and evaluate their attention to detail.
Now that you know exactly what technical skills to look for and when questions to ask when interviewing developers, the time has come to assess the soft skills of these candidates. Part 2 of this blog throws light on the how and why of evaluating candidates based on their communication skills, work ethic, and alignment with the company’s goals.
In today's competitive talent market, attracting and retaining top performers is crucial for any organization's success. However, traditional hiring methods like relying solely on resumes and interviews may not always provide a comprehensive picture of a candidate's skills and potential. This is where pre-employment assessments come into play.
What is Pre-Employement Assessment?
Pre-employment assessments are standardized tests and evaluations administered to candidates before they are hired. These assessments can help you objectively measure a candidate's knowledge, skills, abilities, and personality traits, allowing you to make data-driven hiring decisions.
By exploring and evaluating the best pre-employment assessment tools and tests available, you can:
Improve the accuracy and efficiency of your hiring process.
Identify top talent with the right skills and cultural fit.
Reduce the risk of bad hires.
Enhance the candidate experience by providing a clear and objective evaluation process.
This guide will provide you with valuable insights into the different types of pre-employment assessments available and highlight some of the best tools, to help you optimize your hiring process for 2024.
Why pre-employment assessments are key in hiring
While resumes and interviews offer valuable insights, they can be subjective and susceptible to bias. Pre-employment assessments provide a standardized and objective way to evaluate candidates, offering several key benefits:
Improved decision-making:
By measuring specific skills and knowledge, assessments help you identify candidates who possess the qualifications necessary for the job.
Reduced bias:
Standardized assessments mitigate the risks of unconscious bias that can creep into traditional interview processes.
Increased efficiency:
Assessments can streamline the initial screening process, allowing you to focus on the most promising candidates.
Enhanced candidate experience:
When used effectively, assessments can provide candidates with a clear understanding of the required skills and a fair chance to showcase their abilities.
Types of pre-employment assessments
There are various types of pre-employment assessments available, each catering to different needs and objectives. Here's an overview of some common types:
1. Skill Assessments:
Technical Skills: These assessments evaluate specific technical skills and knowledge relevant to the job role, such as programming languages, software proficiency, or industry-specific expertise. HackerEarth offers a wide range of validated technical skill assessments covering various programming languages, frameworks, and technologies.
Soft Skills: These employment assessments measure non-technical skills like communication, problem-solving, teamwork, and critical thinking, crucial for success in any role.
2. Personality Assessments:
These employment assessments can provide insights into a candidate's personality traits, work style, and cultural fit within your organization.
3. Cognitive Ability Tests:
These tests measure a candidate's general mental abilities, such as reasoning, problem-solving, and learning potential.
4. Integrity Assessments:
These employment assessments aim to identify potential risks associated with a candidate's honesty, work ethic, and compliance with company policies.
By understanding the different types of assessments and their applications, you can choose the ones that best align with your specific hiring needs and ensure you hire the most qualified and suitable candidates for your organization.
Leading employment assessment tools and tests in 2024
Choosing the right pre-employment assessment tool depends on your specific needs and budget. Here's a curated list of some of the top pre-employment assessment tools and tests available in 2024, with brief overviews:
A comprehensive platform offering a wide range of validated skill assessments in various programming languages, frameworks, and technologies. It also allows for the creation of custom assessments and integrates seamlessly with various recruitment platforms.
Provides a broad selection of assessments, including skill tests, personality assessments, and cognitive ability tests. They offer customizable solutions and cater to various industries.
Utilizes gamified assessments to evaluate cognitive skills, personality traits, and cultural fit. They offer a data-driven approach and emphasize candidate experience.
Wonderlic:
Offers a variety of assessments, including the Wonderlic Personnel Test, which measures general cognitive ability. They also provide aptitude and personality assessments.
Harver:
An assessment platform focusing on candidate experience with video interviews, gamified assessments, and skills tests. They offer pre-built assessments and customization options.
Remember: This list is not exhaustive, and further research is crucial to identify the tool that aligns best with your specific needs and budget. Consider factors like the types of assessments offered, pricing models, integrations with your existing HR systems, and user experience when making your decision.
Choosing the right pre-employment assessment tool
Instead of full individual tool reviews, consider focusing on 2–3 key platforms. For each platform, explore:
Target audience: Who are their assessments best suited for (e.g., technical roles, specific industries)?
Types of assessments offered: Briefly list the available assessment categories (e.g., technical skills, soft skills, personality).
Key features: Highlight unique functionalities like gamification, custom assessment creation, or seamless integrations.
Effectiveness: Briefly mention the platform's approach to assessment validation and reliability.
User experience: Consider including user reviews or ratings where available.
Comparative analysis of assessment options
Instead of a comprehensive comparison, consider focusing on specific use cases:
Technical skills assessment:
Compare HackerEarth and Wonderlic based on their technical skill assessment options, focusing on the variety of languages/technologies covered and assessment formats.
Soft skills and personality assessment:
Compare SHL and Pymetrics based on their approaches to evaluating soft skills and personality traits, highlighting any unique features like gamification or data-driven insights.
Candidate experience:
Compare Harver and Wonderlic based on their focus on candidate experience, mentioning features like video interviews or gamified assessments.
Additional tips:
Encourage readers to visit the platforms' official websites for detailed features and pricing information.
Include links to reputable third-party review sites where users share their experiences with various tools.
Best practices for using pre-employment assessment tools
Integrating pre-employment assessments effectively requires careful planning and execution. Here are some best practices to follow:
Define your assessment goals:
Clearly identify what you aim to achieve with assessments. Are you targeting specific skills, personality traits, or cultural fit?
Choose the right assessments:
Select tools that align with your defined goals and the specific requirements of the open position.
Set clear expectations:
Communicate the purpose and format of the assessments to candidates in advance, ensuring transparency and building trust.
Integrate seamlessly:
Ensure your chosen assessment tool integrates smoothly with your existing HR systems and recruitment workflow.
Train your team:
Equip your hiring managers and HR team with the knowledge and skills to interpret assessment results effectively.
Interpreting assessment results accurately
Assessment results offer valuable data points, but interpreting them accurately is crucial for making informed hiring decisions. Here are some key considerations:
Use results as one data point:
Consider assessment results alongside other information, such as resumes, interviews, and references, for a holistic view of the candidate.
Understand score limitations:
Don't solely rely on raw scores. Understand the assessment's validity and reliability and the potential for cultural bias or individual test anxiety.
Look for patterns and trends:
Analyze results across different assessments and identify consistent patterns that align with your desired candidate profile.
Focus on potential, not guarantees:
Assessments indicate potential, not guarantees of success. Use them alongside other evaluation methods to make well-rounded hiring decisions.
Choosing the right pre-employment assessment tools
Selecting the most suitable pre-employment assessment tool requires careful consideration of your organization's specific needs. Here are some key factors to guide your decision:
Industry and role requirements:
Different industries and roles demand varying skill sets and qualities. Choose assessments that target the specific skills and knowledge relevant to your open positions.
Company culture and values:
Align your assessments with your company culture and values. For example, if collaboration is crucial, look for assessments that evaluate teamwork and communication skills.
Candidate experience:
Prioritize tools that provide a positive and smooth experience for candidates. This can enhance your employer brand and attract top talent.
Budget and accessibility considerations
Budget and accessibility are essential factors when choosing pre-employment assessments:
Budget:
Assessment tools come with varying pricing models (subscriptions, pay-per-use, etc.). Choose a tool that aligns with your budget and offers the functionalities you need.
Accessibility:
Ensure the chosen assessment is accessible to all candidates, considering factors like language options, disability accommodations, and internet access requirements.
Additional Tips:
Free trials and demos: Utilize free trials or demos offered by assessment platforms to experience their functionalities firsthand.
Consult with HR professionals: Seek guidance from HR professionals or recruitment specialists with expertise in pre-employment assessments.
Read user reviews and comparisons: Gain insights from other employers who use various assessment tools.
By carefully considering these factors, you can select the pre-employment assessment tool that best aligns with your organizational needs, budget, and commitment to an inclusive hiring process.
Remember, pre-employment assessments are valuable tools, but they should not be the sole factor in your hiring decisions. Use them alongside other evaluation methods and prioritize building a fair and inclusive hiring process that attracts and retains top talent.
Future trends in pre-employment assessments
The pre-employment assessment landscape is constantly evolving, with innovative technologies and practices emerging. Here are some potential future trends to watch:
Artificial intelligence (AI):
AI-powered assessments can analyze candidate responses, written work, and even resumes, using natural language processing to extract relevant insights and identify potential candidates.
Adaptive testing:
These assessments adjust the difficulty level of questions based on the candidate's performance, providing a more efficient and personalized evaluation.
Micro-assessments:
Short, focused assessments delivered through mobile devices can assess specific skills or knowledge on-the-go, streamlining the screening process.
Gamification:
Engaging and interactive game-based elements can make the assessment experience more engaging and assess skills in a realistic and dynamic way.
Conclusion
Pre-employment assessments, when used thoughtfully and ethically, can be a powerful tool to optimize your hiring process, identify top talent, and build a successful workforce for your organization. By understanding the different types of assessments available, exploring top-rated tools like HackerEarth, and staying informed about emerging trends, you can make informed decisions that enhance your ability to attract, evaluate, and hire the best candidates for the future.
Layoffs in the IT industry are becoming more widespread as companies fight to remain competitive in a fast-changing market; many turn to layoffs as a cost-cutting measure. Last year, 1,000 companies including big tech giants and startups, laid off over two lakhs of employees. But first, what are layoffs in the tech business, and how do they impact the industry?
Tech layoffs are the termination of employment for some employees by a technology company. It might happen for various reasons, including financial challenges, market conditions, firm reorganization, or the after-effects of a pandemic. While layoffs are not unique to the IT industry, they are becoming more common as companies look for methods to cut costs while remaining competitive.
The consequences of layoffs in technology may be catastrophic for employees who lose their jobs and the firms forced to make these difficult decisions. Layoffs can result in the loss of skill and expertise and a drop in employee morale and productivity. However, they may be required for businesses to stay afloat in a fast-changing market.
This article will examine the reasons for layoffs in the technology industry, their influence on the industry, and what may be done to reduce their negative impacts. We will also look at the various methods for tracking tech layoffs.
What are tech layoffs?
The term "tech layoff" describes the termination of employees by an organization in the technology industry. A company might do this as part of a restructuring during hard economic times.
In recent times, the tech industry has witnessed a wave of significant layoffs, affecting some of the world’s leading technology companies, including Amazon, Microsoft, Meta (formerly Facebook), Apple, Cisco, SAP, and Sony. These layoffs are a reflection of the broader economic challenges and market adjustments facing the sector, including factors like slowing revenue growth, global economic uncertainties, and the need to streamline operations for efficiency.
Each of these tech giants has announced job cuts for various reasons, though common themes include restructuring efforts to stay competitive and agile, responding to over-hiring during the pandemic when demand for tech services surged, and preparing for a potentially tough economic climate ahead. Despite their dominant positions in the market, these companies are not immune to the economic cycles and technological shifts that influence operational and strategic decisions, including workforce adjustments.
This trend of layoffs in the tech industry underscores the volatile nature of the tech sector, which is often at the mercy of rapid changes in technology, consumer preferences, and the global economy. It also highlights the importance of adaptability and resilience for companies and employees alike in navigating the uncertainties of the tech landscape.
Yes, the market is always uncertain, but why resort to tech layoffs?
Various factors cause tech layoffs, including company strategy changes, market shifts, or financial difficulties. Companies may lay off employees if they need help to generate revenue, shift their focus to new products or services, or automate certain jobs.
In addition, some common reasons could be:
Financial struggles
Currently, the state of the global market is uncertain due to economic recession, ongoing war, and other related phenomena. If a company is experiencing financial difficulties, only sticking to pay cuts may not be helpful—it may need to reduce its workforce to cut costs.
The tech industry is constantly evolving, and companies would have to adjust their workforce to meet changing market conditions. For instance, companies are adopting remote work culture, which surely affects on-premises activity, and companies could do away with some number of tech employees at the backend.
Restructuring
Companies may also lay off employees as part of a greater restructuring effort, such as spinning off a division or consolidating operations.
Automation
With the advancement in technology and automation, some jobs previously done by human labor may be replaced by machines, resulting in layoffs.
Mergers and acquisitions
When two companies merge, there is often overlap in their operations, leading to layoffs as the new company looks to streamline its workforce.
But it's worth noting that layoffs are not exclusive to the tech industry and can happen in any industry due to uncertainty in the market.
Will layoffs increase in 2024?
It is challenging to estimate the rise or fall of layoffs. The overall state of the economy, the health of certain industries, and the performance of individual companies will play a role in deciding the degree of layoffs in any given year.
But it is also seen that, in the first 15 days of this year, 91 organizations laid off over 24,000 tech workers, and over 1,000 corporations cut down more than 150,000 workers in 2022, according to an Economic Times article.
The COVID-19 pandemic caused a huge economic slowdown and forced several businesses to downsize their employees. However, some businesses rehired or expanded their personnel when the world began to recover.
So, given the current level of economic uncertainty, predicting how the situation will unfold is difficult.
What types of companies are prone to tech layoffs?
Tech layoffs can occur in organizations of all sizes and various areas.
Following are some examples of companies that have experienced tech layoffs in the past:
Large tech firms
Companies such as IBM, Microsoft, Twitter, Better.com, Alibaba, and HP have all experienced layoffs in recent years as part of restructuring initiatives or cost-cutting measures.
Market scenarios are still being determined after Elon Musk's decision to lay off employees. Along with tech giants, some smaller companies and startups have also been affected by layoffs.
Startups
Because they frequently work with limited resources, startups may be forced to lay off staff if they cannot get further funding or need to pivot due to market downfall.
Small and medium-sized businesses
Small and medium-sized businesses face layoffs due to high competition or if the products/services they offer are no longer in demand.
Companies in certain industries
Some sectors of the technological industry, such as the semiconductor industry or automotive industry, may be more prone to layoffs than others.
Companies that lean on government funding
Companies that rely significantly on government contracts may face layoffs if the government cuts technology spending or contracts are not renewed.
How to track tech layoffs?
You can’t stop tech company layoffs, but you should be keeping track of them. We, HR professionals and recruiters, can also lend a helping hand in these tough times by circulating “layoff lists” across social media sites like LinkedIn and Twitter to help people land jobs quicker. Firefish Software put together a master list of sources to find fresh talent during the layoff period.
Because not all layoffs are publicly disclosed, tracking tech industry layoffs can be challenging, and some may go undetected. There are several ways to keep track of tech industry layoffs:
In addition, they aid in identifying trends in layoffs within the tech industry. It can reveal which industries are seeing the most layoffs and which companies are the most affected.
Companies can use layoff trackers as an early warning system and compare their performance to that of other companies in their field.
News articles
Because many news sites cover tech layoffs as they happen, keeping a watch on technology sector stories can provide insight into which organizations are laying off employees and how many individuals have been affected.
Social media
Organizations and employees frequently publish information about layoffs in tech on social media platforms; thus, monitoring companies' social media accounts or following key hashtags can provide real-time updates regarding layoffs.
Online forums and communities
There are online forums and communities dedicated to discussing tech industry news, and they can be an excellent source of layoff information.
Government reports
Government agencies such as the Bureau of Labor Statistics (BLS) publish data on layoffs and unemployment, which can provide a more comprehensive picture of the technology industry's status.
How do companies reduce tech layoffs?
Layoffs in tech are hard – for the employee who is losing their job, the recruiter or HR professional who is tasked with informing them, and the company itself. So, how can we aim to avoid layoffs? Here are some ways to minimize resorting to letting people go:
Salary reductions
Instead of laying off employees, businesses can lower the salaries or wages of all employees. It can be accomplished by instituting compensation cuts or salary freezes.
Implementing a hiring freeze
Businesses can halt employing new personnel to cut costs. It can be a short-term solution until the company's financial situation improves.
Businesses might search for ways to cut or remove non-essential expenses such as travel, training, and office expenses.
Reducing working hours
Companies can reduce employee working hours to save money, such as implementing a four-day workweek or a shorter workday.
These options may not always be viable and may have their problems, but before laying off, a company owes it to its people to consider every other alternative, and formulate the best solution.
Tech layoffs to bleed into this year
While we do not know whether this trend will continue or subside during 2023, we do know one thing. We have to be prepared for a wave of layoffs that is still yet to hit. As of last month, Layoffs.fyi had already tracked 170+ companies conducting 55,970 layoffs in 2023.
So recruiters, let’s join arms, distribute those layoff lists like there’s no tomorrow, and help all those in need of a job! :)
In today’s fast-paced world, recruiting talent has become increasingly complicated. Technological advancements, high workforce expectations and a highly competitive market have pushed recruitment agencies to adopt innovative strategies for recruiting various types of talent. This article aims to explore one such recruitment strategy – headhunting.
What is Headhunting in recruitment?
In headhunting, companies or recruitment agencies identify, engage and hire highly skilled professionals to fill top positions in the respective companies. It is different from the traditional process in which candidates looking for job opportunities approach companies or recruitment agencies. In headhunting, executive headhunters, as recruiters are referred to, approach prospective candidates with the hiring company’s requirements and wait for them to respond. Executive headhunters generally look for passive candidates, those who work at crucial positions and are not on the lookout for new work opportunities. Besides, executive headhunters focus on filling critical, senior-level positions indispensable to companies. Depending on the nature of the operation, headhunting has three types. They are described later in this article. Before we move on to understand the types of headhunting, here is how the traditional recruitment process and headhunting are different.
How do headhunting and traditional recruitment differ from each other?
Headhunting is a type of recruitment process in which top-level managers and executives in similar positions are hired. Since these professionals are not on the lookout for jobs, headhunters have to thoroughly understand the hiring companies’ requirements and study the work profiles of potential candidates before creating a list.
In the traditional approach, there is a long list of candidates applying for jobs online and offline. Candidates approach recruiters for jobs. Apart from this primary difference, there are other factors that define the difference between these two schools of recruitment.
AspectHeadhuntingTraditional RecruitmentCandidate TypePrimarily passive candidateActive job seekersApproachFocused on specific high-level rolesBroader; includes various levelsScopeproactive outreachReactive: candidates applyCostGenerally more expensive due to expertise requiredTypically lower costsControlManaged by headhuntersManaged internally by HR teams
All the above parameters will help you to understand how headhunting differs from traditional recruitment methods, better.
Types of headhunting in recruitment
Direct headhunting: In direct recruitment, hiring teams reach out to potential candidates through personal communication. Companies conduct direct headhunting in-house, without outsourcing the process to hiring recruitment agencies. Very few businesses conduct this type of recruitment for top jobs as it involves extensive screening across networks outside the company’s expanse.
Indirect headhunting: This method involves recruiters getting in touch with their prospective candidates through indirect modes of communication such as email and phone calls. Indirect headhunting is less intrusive and allows candidates to respond at their convenience.Third-party recruitment: Companies approach external recruitment agencies or executive headhunters to recruit highly skilled professionals for top positions. This method often leverages the company’s extensive contact network and expertise in niche industries.
How does headhunting work?
Finding highly skilled professionals to fill critical positions can be tricky if there is no system for it. Expert executive headhunters employ recruitment software to conduct headhunting efficiently as it facilitates a seamless recruitment process for executive headhunters. Most software is AI-powered and expedites processes like candidate sourcing, interactions with prospective professionals and upkeep of communication history. This makes the process of executive search in recruitment a little bit easier. Apart from using software to recruit executives, here are the various stages of finding high-calibre executives through headhunting.
Identifying the role
Once there is a vacancy for a top job, one of the top executives like a CEO, director or the head of the company, reach out to the concerned personnel with their requirements. Depending on how large a company is, they may choose to headhunt with the help of an external recruiting agency or conduct it in-house. Generally, the task is assigned to external recruitment agencies specializing in headhunting. Executive headhunters possess a database of highly qualified professionals who work in crucial positions in some of the best companies. This makes them the top choice of conglomerates looking to hire some of the best talents in the industry.
Defining the job
Once an executive headhunter or a recruiting agency is finalized, companies conduct meetings to discuss the nature of the role, how the company works, the management hierarchy among other important aspects of the job. Headhunters are expected to understand these points thoroughly and establish a clear understanding of their expectations and goals.
Candidate identification and sourcing
Headhunters analyse and understand the requirements of their clients and begin creating a pool of suitable candidates from their database. The professionals are shortlisted after conducting extensive research of job profiles, number of years of industry experience, professional networks and online platforms.
Approaching candidates
Once the potential candidates have been identified and shortlisted, headhunters move on to get in touch with them discreetly through various communication channels. As such candidates are already working at top level positions at other companies, executive headhunters have to be low-key while doing so.
Assessment and Evaluation
In this next step, extensive screening and evaluation of candidates is conducted to determine their suitability for the advertised position.
Interviews and negotiations
Compensation is a major topic of discussion among recruiters and prospective candidates. A lot of deliberation and negotiation goes on between the hiring organization and the selected executives which is facilitated by the headhunters.
Finalizing the hire
Things come to a close once the suitable candidates accept the job offer. On accepting the offer letter, headhunters help finalize the hiring process to ensure a smooth transition.
The steps listed above form the blueprint for a typical headhunting process. Headhunting has been crucial in helping companies hire the right people for crucial positions that come with great responsibility. However, all systems have a set of challenges no matter how perfect their working algorithm is. Here are a few challenges that talent acquisition agencies face while headhunting.
Common challenges in headhunting
Despite its advantages, headhunting also presents certain challenges:
Cost Implications: Engaging headhunters can be more expensive than traditional recruitment methods due to their specialized skills and services.
Time-Consuming Process: While headhunting can be efficient, finding the right candidate for senior positions may still take time due to thorough evaluation processes.
Market Competition: The competition for top talent is fierce; organizations must present compelling offers to attract passive candidates away from their current roles.
Although the above mentioned factors can pose challenges in the headhunting process, there are more upsides than there are downsides to it. Here is how headhunting has helped revolutionize the recruitment of high-profile candidates.
Advantages of Headhunting
Headhunting offers several advantages over traditional recruitment methods:
Access to Passive Candidates: By targeting individuals who are not actively seeking new employment, organisations can access a broader pool of highly skilled professionals.
Confidentiality: The discreet nature of headhunting protects both candidates’ current employment situations and the hiring organisation’s strategic interests.
Customized Search: Headhunters tailor their search based on the specific needs of the organization, ensuring a better fit between candidates and company culture.
Industry Expertise: Many headhunters specialise in particular sectors, providing valuable insights into market dynamics and candidate qualifications.
Conclusion
Although headhunting can be costly and time-consuming, it is one of the most effective ways of finding good candidates for top jobs. Executive headhunters face several challenges maintaining the g discreetness while getting in touch with prospective clients. As organizations navigate increasingly competitive markets, understanding the nuances of headhunting becomes vital for effective recruitment strategies. To keep up with the technological advancements, it is better to optimise your hiring process by employing online recruitment software like HackerEarth, which enables companies to conduct multiple interviews and evaluation tests online, thus improving candidate experience. By collaborating with skilled headhunters who possess industry expertise and insights into market trends, companies can enhance their chances of securing high-caliber professionals who drive success in their respective fields.
