Developer Insights

Top 13 (free) must read machine leaning books for beginners

Getting learners to read textbooks and use other teaching aids effectively can be tricky. Especially, when the books are just too dreary.

In this post, we’ve compiled great e-resources for you digital natives looking to explore the exciting world of Machine Learning and Neural Networks. But before you dive into the deep end, you need to make sure you’ve got the fundamentals down pat.

It doesn’t matter what catches your fancy, machine learning, artificial intelligence, or deep learning; you need to know the basics of math and stats—linear algebra, calculus, optimization, probability—to get ahead.

Top machine learning books to read for beginners

Matrix Computations

This 2013 edition by Golub and Van Loan, published by The Johns Hopkins University Press, teaches you about matrix analysis, linear systems, eigenvalues, discrete Poisson solvers, least squares, parallel LU, pseudospectra, Singular Value Decomposition, and much more.

This book is an indispensable tool for engineers and computational scientists. It has great reviews on Amazon, especially by users looking for problems, discussions, codes, solutions, and references in numerical linear algebra.

Free Book:Download here
A Probabilistic Theory of Pattern Recognition

Written by Devroye, Lugosi, and Györfi, this an excellent book for graduate students and researchers. The book covers various probabilistic techniques including nearest neighbour rules, feature extraction, Vapnik-Chervonenkis theory, distance measures, parametric classification, and kernel rules.

Amazon reviewers laud it for its nearly 500 problems and exercises.

Wikipedia says “The terms pattern recognition, machine learning, data mining and knowledge discovery in databases are hard to separate, as they largely overlap in their scope.”

No wonder, machine learning enthusiasts swear by this comprehensive, theoretical book on “nonparametric, distribution-free methodology in Pattern Recognition.”

Free Book:Download here
Advanced Engineering Mathematics

Erwin Kreyszig’s book beautifully covers the basics of applied math in a comprehensive and simplistic manner for engineers, computer scientists, mathematicians, and physicists.

It teaches you Fourier analysis, vector analysis, linear algebra, optimization, graphs, complex analysis, and differential and partial differential equations.

It has up-to-date and effective problem sets that ensure you understand the concepts clearly.
Probability and Statistics Cookbook

A collection of math and stats reference material from the University of California (Berkeley) and other sources put together by Matthias Vallentin, this cookbook is a must-have for learners.

There are no elaborate explanations but concise representations of key concepts. You can view it on GitHub, or download a PDF file using the link below.

Free Book:Download here
An Introduction to Statistical Learning (with applications in R)

This book written by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani is meant for non-math students.

For data scientists, this is a valuable addition because of its R labs.

The TOC includes linear regression, classification, resampling methods, linear model and regularization, tree-based methods, shrinkage approaches, clustering, support vector machines, and unsupervised learning.

With interesting real-world examples and attractive graphics, this is a great text for statistical tools and techniques.

Free Book:Download here
Probabilistic Programming and Bayesian Methods for Hackers

Cameron Davidson-Pilon describes Bayesian methods and probabilistic programming from math and computation perspectives.

The book discusses modeling Bayesian problems using Python’s PyMC, loss functions, the Law of Large Numbers, Markov Chain Monte Carlo, priors, and so lots more.

The content is open sourced. The print version has updated examples, EOC questions, and improved and extra sections.

Free Book:Download here
The Elements of Statistical Learning

Authors Trevor Hastie, Robert Tibshirani, and Jerome Friedman (all three are Stanford professors) discuss supervised learning, linear methods of regression and classification, kernel smoothing methods, regularization, model selection and assessment, additive trees, SVM, neural networks, random forests, nearest neighbors, unsupervised learning, ensemble methods, and more.

This book covers a broad range of topics is particularly useful for researchers interested in data mining and machine learning.

You need to know linear algebra and some stats before you can appreciate the text.

This is what one of the reviewers said about the book on Amazon: The Elements of Statistical Learning is a comprehensive mathematical treatment of machine learning from a statistical perspective.

Free Book:Download here
Bayesian Reasoning and Machine Learning

David Barber’s books is a comprehensive piece of writing on graphical models and machine learning.

Meant for final-year undergraduate and graduate students, this text has ample guidelines, examples,and exercises. The author also offers a MATLAB toolbox and a related website.

It covers inference in probabilistic models including belief networks, inference in trees,the junction tree algorithm, decision trees; learning in probabilistic models including Naive Bayes, hidden variables and missing data, supervised and unsupervised linear dimension reduction, Gaussian processes, and linear models; dynamic models including discrete- and continuous-state model Markov models, and distribution computation; and approximate inference.

Free Book:Download here
Information Theory, Inference, and Learning Algorithms

David MacKay exciting book discusses key concepts that form the core of machine learning, data mining, pattern recognition, bioinformatics, and cryptography.

Amazon reviewers find the illustrations, depth, and “esoteric” approach remarkable.

It is a great book on information theory and inference, which covers topics such as data compression, noisy-channel coding, probabilities, neural networks, and sparse graph codes.

Free Book:Download here
Deep Learning

This what Elon Musk, co-founder of Tesla Motors, has to say about this definitive text written by Ian Goodfellow, Yoshua Bengio, and Aaron Courville: “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.”

The authors talk about applied math and machine learning basics, deep networks and modern practices, and deep learning research.

For engineers interested in neural networks, this could well be their bible.

The book is highly recommended for people in academia, providing the required mathematical background to fully appreciate deep learning in its current state.

Free Book:Download here
Neural Networks and Deep Learning

Michael Nielsen’s free online book is a comprehensive text on the core concepts of deep learning and artificial neural networks.

The book has great interactive elements, but it does not provide solutions for the exercises. Laid out like a narrative, Nielsen holds onto core math and code to explain the key ideas.

He talks about back propagation, hyper parameter optimization, activation functions, neural networks as functional approximates, regularization, a little about convolution neural networks, etc.

The author includes valuable links to ongoing research and influential research papers and related tutorials.

Free Book:Download here
Supervised Sequence Labelling with Recurrent Neural Networks

Alex Graves discusses how to classify and transcribe sequential data, which is important in part-of-speech tagging, gesture, handwriting, and speech recognition, and protein secondary structure prediction.

He talks about the role of recurrent neural networks in sequence labeling.

Long short-term memory, a comparison of network architectures, hidden Markov model hybrids, connectionist temporal classification, multidimensional networks, and hierarchical sub sampling networks are other chapters in this book.

Free Book:Download here
Reinforcement Learning: An Introduction

Richard S. Sutton and Andrew G. Barto’s pioneering book onreinforcement learning covers the intellectual background, applications, algorithms, and the future of this exciting field. These University of Massachusetts Professors describe this artificial intelligence concept with clarity and simplicity.

This book includes interesting topics such as Markov decision processes, Monte Carlo methods, dynamic programming, temporal-difference learning, eligibility traces, and artificial neural networks.

Free Book:Download here

Summary

What’s better than getting educational resources that are free and authored by pioneers in the field?

Can’t think of a downside really…Especially for struggling students, these ebooks are a boon.

They don’t need to wait for the books to turn up at the library or swap with others;grab them and start learning!

So, what’s stopping you from picking up one of these excellent books and fashioning a successful career in data science, AI, or machine learning?

Developer Insights

Introduction to Naive Bayes Classification Algorithm in Python and R

Let's say you are given with a fruit which is yellow, sweet, and long and you have to check the class to which it belongs.Step 2: Draw the likelihood table for the features against the classes.

Name	Yellow	Sweet	Long	Total
Mango	350/800=P(Mango\|Yellow)	450/850	0/400	650/1200=P(Mango)
Banana	400/800	300/850	350/400	400/1200
Others	50/800	100/850	50/400	150/1200
Total	800=P(Yellow)	850	400	1200

Step 3: Calculate the conditional probabilities for all the classes, i.e., the following in our example:

Step 4: Calculate [latex]\displaystyle\max_{i}{P(C_i|x_1, x_2,\ldots, x_n)}[/latex]. In our example, the maximum probability is for the class banana, therefore, the fruit which is long, sweet and yellow is a banana by Naive Bayes Algorithm.In a nutshell, we say that a new element will belong to the class which will have the maximum conditional probability described above.

Variations of the Naive Bayes algorithm

There are multiple variations of the Naive Bayes algorithm depending on the distribution of [latex]P(x_j|C_i)[/latex]. Three of the commonly used variations are

Gaussian: The Gaussian Naive Bayes algorithm assumes distribution of features to be Gaussian or normal, i.e.,
[latex]\displaystyle P(x_j|C_i)=\frac{1}{\sqrt{2\pi\sigma_{C_i}^2}}\exp{\left(-\frac{(x_j-\mu_{C_j})^2}{2\sigma_{C_i}^2}\right)}[/latex]
Read more about it here.
Multinomial: The Multinomial Naive Bayes algorithm is used when the data is distributed multinomially, i.e., multiple occurrences matter a lot. You can read more here.
Bernoulli: The Bernoulli algorithm is used when the features in the data set are binary-valued. It is helpful in spam filtration and adult content detection techniques. For more details, click here.

Pros and Cons of Naive Bayes algorithm

Every coin has two sides. So does the Naive Bayes algorithm. It has advantages as well as disadvantages, and they are listed below:

Pros

It is a relatively easy algorithm to build and understand.
It is faster to predict classes using this algorithm than many other classification algorithms.
It can be easily trained using a small data set.

Cons

If a given class and a feature have 0 frequency, then the conditional probability estimate for that category will come out as 0. This problem is known as the "Zero Conditional Probability Problem." This is a problem because it wipes out all the information in other probabilities too. There are several sample correction techniques to fix this problem such as "Laplacian Correction."
Another disadvantage is the very strong assumption of independence class features that it makes. It is near to impossible to find such data sets in real life.

Naive Bayes with Python and R

Let us see how we can build the basic model using the Naive Bayes algorithm in R and in Python.

R Code

To start training a Naive Bayes classifier in R, we need to load the e1071 package.

library(e1071)

To split the data set into training and test data we will use the caTools package.

library(caTools)

The predefined function used for the implementation of Naive Bayes in R is called naiveBayes(). There are only a few parameters that are of use:

naiveBayes(formula, data, laplace = 0, subset, na.action = na.pass)

formula: The traditional formula [latex]Y\sim X_1+X_2+\ldots+X_n[/latex]
data: The data frame containing numeric or factor variables
laplace: Provides a smoothing effect
subset: Helps in using only a selection subset of the data based on some Boolean filter
na.action: Helps in determining what is to be done when a missing value in the data set is encountered

Let us take the example of the iris data set.

> library(e1071)

> library(caTools)



> data(iris)



> iris$spl=sample.split(iris,SplitRatio=0.7)

# By using the sample.split() we are creating a vector with values TRUE and FALSE and by setting

  the SplitRatio to 0.7, we are splitting the original Iris dataset of 150 rows to 70% training

  and 30% testing data. 

> train=subset(iris, iris$spl==TRUE)#the subset of iris dataset for which spl==TRUE

> test=subset(iris, iris$spl==FALSE)



> nB_model <- naiveBayes(train[,1:4], train[,5]) 



> table(predict(nB_model, test[,-5]), test[,5]) #returns the confusion matrix

                setosa versicolor virginica

  setosa         17          0         0

  versicolor      0         17         2

  virginica       0          0        14

Python Code

We will use the Python library scikit-learn to build the Naive Bayes algorithm.

>>> from sklearn.naive_bayes import GaussianNB

>>> from sklearn.naive_bayes import MultinomialNB

>>> from sklearn import datasets

>>> from sklearn.metrics import confusion_matrix

>>> from sklearn.model_selection import train_test_split



>>> iris = datasets.load_iris()

>>> X = iris.data

>>> y = iris.target



# Split the data into a training set and a test set

>>> X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)

>>> gnb = GaussianNB()

>>> mnb = MultinomialNB()



>>> y_pred_gnb = gnb.fit(X_train, y_train).predict(X_test)

>>> cnf_matrix_gnb = confusion_matrix(y_test, y_pred_gnb)



>>> print(cnf_matrix_gnb)

[[16 0 0]

 [ 0 18 0]

 [ 0 0 11]]



>>> y_pred_mnb = mnb.fit(X_train, y_train).predict(X_test)

>>> cnf_matrix_mnb = confusion_matrix(y_test, y_pred_mnb)



>>> print(cnf_matrix_mnb)

[[16 0 0]

 [ 0 0 18]

 [ 0 0 11]]

Applications

The Naive Bayes algorithm is used in multiple real-life scenarios such as

Text classification: It is used as a probabilistic learning method for text classification. The Naive Bayes classifier is one of the most successful known algorithms when it comes to the classification of text documents, i.e., whether a text document belongs to one or more categories (classes).
Spam filtration: It is an example of text classification. This has become a popular mechanism to distinguish spam email from legitimate email. Several modern email services implement Bayesian spam filtering.
Many server-side email filters, such as DSPAM, SpamBayes, SpamAssassin, Bogofilter, and ASSP, use this technique.
Sentiment Analysis: It can be used to analyze the tone of tweets, comments, and reviews—whether they are negative, positive or neutral.
Recommendation System: The Naive Bayes algorithm in combination with collaborative filtering is used to build hybrid recommendation systems which help in predicting if a user would like a given resource or not.

Conclusion

This article is a simple explanation of the Naive Bayes Classification algorithm, with an easy-to-understand example and a few technicalities.Despite all the complicated math, the implementation of the Naive Bayes algorithm involves simply counting the number of objects with specific features and classes. Once these numbers are obtained, it is very simple to calculate probabilities and arrive at a conclusion.Hope you are now familiar with this machine learning concept you most like would have heard of before.

Developer Insights

Simple Guide to Neural Networks and Deep Learning in Python

Step 2: Import required libraries.

from numpy import*

import numpy as numpy

import keras

from keras.layers import Dense

from keras.models import Sequential

from keras.utils import np_utils

from sklearn.preprocessing import LabelEncoder

Step 3: Load data from the training set.

X= numpy.genfromtxt("Iris_Data.txt",delimiter= ",", usecols=(0,1,2,3))

t= numpy.genfromtxt("Iris_Data.txt",delimiter= ",", usecols= (4), dtype= None)

Here, X is an array of input feature vectors and t is an array containing their corresponding target values. dtype= None changes the default data type of numpy array (i.e float) to contents of each column, individually.

Step 4: Since target values t are in string format, it has to be assigned numerical labels an

Developer Insights

Top programming languages used in IoT

In recent times, the Internet of Things is ubiquitous and is now a popular domain in the developer community. According to research by Statista, there are 6.21 million developers working in IoT and 5.36 million developers planning to work in IoT in next 6 months.

If you want to get started in IoT and are wondering which programming language to start with, here is a list of 11 popular programming languages used in IoT.

C

C, the language that was first developed to program telephone switches, is a reliable and reasonable choice for embedded system development. It is impressive because of its proximity to machine language.

It is a procedural language and the code is compiled and not interpreted. The code written in C is more reliable and scalable, and processor independence makes it a strong contender for IoT development. Because C is not platform independent, it enables IoT developers for code reuse, which can run on most of the systems.With the help of pointers, accessing and modifying addresses is easy in C.

C++

C++ is a middle-level programming language with imperative, object-oriented, and generic programming features with low-level memory manipulation.

C++ is designed with a bias toward system programming, embedded programming, resource-constrained devices and large systems. The design highlights of C++ are

Performance
Efficiency
Flexibility of use

C++ is a popular choice among the embedded developers coding for Linux systems. Linux programming expertise, particularly with C++, is crucial for developing efficient, scalable, and secure IoT application. Here are a few features that make C++ a popular choice among IoT developers:

Data hiding
Stronger typing/ checking
Multi-peripheral transparency using classes
Templates (as always if used carefully)
Initialisation lists

Go

Go is an open source programming language developed at Google. It combines the benefits of compiled language that is performance and security with that of aspeed of dynamic language.

It supports concurrent input, output, and processing on many different channels. Coordination of an entire fleet of sensors and actuators is possible when used correctly. The biggest risk is that the different channels don’t necessarily know about one another. If a programmer isn’t careful enough, a system could behave unpredictably because of a lack of coordination between channels.

In GO, gathering and sending data to various sensors and actuators is made easy by adding explicit hash table type.

The biggest advantage of GO is its ability to sort an entire network of sensors and making use of related IoT programming related devices. Go is now available on a wide variety of processors and platforms.

JavaScript

JavaScript is a scripting language with syntax similar to C. Initially, it was mainly used to create web pages, but now JavaScript is widely used in web servers, mobile apps, and IoT systems. JavaScript is good at event-driven applications; this allows every device to listen to various other events and respond to the concerned events. It has a garbage collector which eliminates freeing up of memory.

In your project, if you want to use the Apache server on a Raspberry Pi to collect data from a network of Arduino-based sensors, JavaScript would be good to start with.

Here are 10 reasons why JavaScript is used in IoT.

Python

The language, which was developed during a holiday break, went on to become the most preferred language for web development and started gaining popularity in embedded controls and IoT. Python is an interpreted language which can be either submitted for runtime compilation or run through one of theseveral pre-compilers so that compact executable code may be distributed.

The greatest benefit that Python offers to developers is readability with elegant syntax, without compromising on the size. Python’s clean syntax is apt for database arrangement. If your app requires data to be arranged in a database format and use tables for data control, Python is the best choice.

Python supports a huge number of libraries and modules, so you can get more stuff done with less code. It’s handy in more powerful edge devices, gateways, and also the cloud.

Rust

Rust is an open source, general-purpose, multi-paradigm, compiled programming language sponsored by Mozilla. Rust shares many of Go’s qualitiesand solves race condition problems of Go. Rust includes functions that eliminate race conditions for highly concurrent programs, making it a less risky language. Because of its ability to handle concurrent programming, Rust is now popular among IoT developers.

Rust is a safe, concurrent and practical language, supporting functional and imperative-procedural paradigms. It maintains these goals without a garbage collector. This makes Rust a useful language for the following use cases:

Embedding in other languages
Programming with specific space and time requirements
Writing low-level code, like device drivers and operating systems

Rust is an improvement on current languages by having a number of compile-time safety checks which produce no runtime overhead and eliminates all data races.

Java

Java is an object-oriented language, and there are very few hardware dependencies built into the compiler, which makes it incredibly portable.

The biggest concern in IoT is security; with the Generic Connection Framework 8 (GCF 8), the Access Point API in Java provides the latest security standards and the highest levels of networked encryption and authentication which ensure data privacy.

All the object references in Java are implicit pointers which cannot be manipulated by application code. This automatically rules out the potential risk of memory access violations which can inevitably cause an application to stop all of a sudden.

Connectivity at the application level of the IoT system is also easily handled in Java with a comprehensive set of APIs, both standard and freely available through open source projects.

Assembly language

Assembly language is a low-level programming language and is specific to a particular type of computer architecture. In contrast, to other high-level programming languages, Assembly language is not portable across multiple architectures.

Assembly language is also called symbolic machine code. It is converted into executable machine code by a utility program.

Assembly language is best if you want to make your IoT project compact, minimal, and optimal.

ParaSail

ParaSail stands for Parallel Specification and Implementation Language. It is a compiled, object-oriented language with syntax similar to Java, Python, C#, and Ada.

ParaSail is a language that you must consider if you have a requirement for parallel processing in your IoT system.

ParaSail is capable of both specifying and implementing parallel applications. It supports both implicit and explicit parallelism. Every ParaSail expression is defined to have parallel evaluation semantics.

R

R is a programming language and environment for statistical computing and graphics. It is widely used by statisticians and data miners for building statistical software and data analysis.

Here are a few statistical and graphical techniques implemented by R and its libraries:

Linear and nonlinear modeling
Time-series analysis
Classical statistical tests
Clustering
Classification and others

R is easily extensible through functions and extensions. It has stronger object-oriented programming facilities than most statistical computing languages. Extending R is also made easy with its lexical scoping rules.

B#

B# was designed as a very small and efficient embedded control language. The embedded virtual machine (EVM) that allows B# to run on a variety of different hardware platforms only takes 24 kb of memory. B# is lean enough for 8-bit MCUs.

The syntax of B# looks a bit like C#, with support for the real-time control functions that are critical to make things happen in the real world. The B# code when coupled with a compact virtual machine could be easily ported and reused across multiple hardware platforms.

B# supports writing portable interrupt handlers and device addressing registers in a uniform way.

It also supports modern object-oriented features such as

Namespaces
Abstract and concrete classes
Interfaces
Delegates

In addition to these, B# caters to the embedded systems programmer with

Efficient boxing/unboxing conversions
Multi-threading statements
Device addressing registers
Deterministic memory defragmenter
Field properties
Interrupt handlers

Each of these features is directly supported by the constructs of B# and its underlying virtual machine. This helps to create, use, and reuse more portable and decoupled software components across different embedded systems applications.

If your project is going to live on embedded hardware platforms that aren’t as big and complex as a Raspberry Pi, then B# is the best option available.

Forth

Forth has been around since 1970 and is designed and optimized for embedded system programming. It is an imperative stack-based language and environment. It includes features such as

Structured programming
Concatenative programming
Extensibility
Reflection

Forth features both interactive execution of commands and the ability to compile sequences of commands for later execution.

Forth is used in the Open Firmware boot loader, in space applications such as spacecrafts, and other embedded systems where interaction with hardware is involved.

HiveQL

HiveQL is based on SQL but does not strictly follow the full SQL-92 standard. It runs on the Hadoop map-reduce framework but hides the complexities from the developers.

HiveQL does not offer extensions in SQL, including multi-table inserts and create table as select. It only offers basic support for indexes. Here are few things which HiveQL can do easily:

Create and manage tables and partitions
Evaluate functions
Support various Relational, Arithmetic, and Logical Operators
Download the contents of a table to a local directory or result of queries to HDFS directory

Pig Latin

Pig Latin is the language of Apache Pig, which is a high-level platform for creating programs that run on Apache Hadoop. Pig Latin can be extended using User Defined Functions (UDFs) which you can write in Java, Python, JavaScript, Ruby, or Groovy and then call directly from the language.

Here are a couple of key properties of Pig Latin:

Ease of programming. With Pig Latin, it is easy to achieve parallel execution of simple and “embarrassingly parallel” data analysis tasks. Complex tasks with multiple interrelated data transformations are explicitly encoded as data flow sequences; this makes them easy to write, understand, and maintain.
Optimization opportunities. The way in which tasks are encoded permits the system to optimize their execution automatically, allowing you to focus on semantics rather than efficiency.

Extensibility. Users can create their own functions to do special-purpose processing.

Julia

Julia is a high-level, high-performance dynamic programming language for numerical analysis and computational science. Julia language provides

A sophisticated compiler
An extensive mathematical function library
Numerical accuracy
Distributed parallel execution

What makes Julia’s design unique is its type system with parametric polymorphism, and it types in a fully dynamic programming language. It also has multiple dispatch as its core programming paradigm. Julia allows concurrent, parallel, and distributed computing and direct calling of C and Fortran libraries without any glue code.

Julia’s LLVM-based JIT compiler combined with the language’s design allows it to often match the performance of C.

It does not impose any particular style of parallelism on the user. Instead, Julia provides flexibility to the user by providing a number of key building blocks for distributed computation through which it supports various styles of parallelism.

Developer Insights

Practical Guide to Clustering Algorithms and Evaluation in R

Introduction

Clustering algorithms are a part of unsupervised machine learning algorithms. As there is no target variable, the model is trained using input variables to discover intrinsic groups or clusters.

Because we don’t have labels for the data, these groups are formed based on similarity between data points. This tutorial covers clustering concepts, techniques, and applications across domains like healthcare, retail, and manufacturing.

We’ll also walk through examples in R, using real-world data from a water treatment plant to apply our knowledge practically.

Types of Clustering Techniques
Distance Calculation for Clustering
K-Means Clustering
Choosing the Best K in K-Means
Hierarchical Clustering
Evaluation Methods in Cluster Analysis
Clustering in R – Water Treatment Plants

Types of Clustering Techniques

Common clustering algorithms include K-Means, Fuzzy C-Means, and Hierarchical Clustering. Depending on the data type (numeric, categorical, mixed), the algorithm may vary. Clustering techniques can be classified as:

Soft Clustering – Observations are assigned to clusters with probabilities.
Hard Clustering – Observations belong to only one cluster.

We’ll focus on K-Means and Hierarchical Clustering in this guide.

Distance Calculation for Clustering

Distance metrics are used to measure similarity between data points. Common metrics include:

Euclidean Distance – Suitable for numeric variables.
Manhattan Distance – Measures horizontal and vertical distances.
Hamming Distance – Used for categorical variables.
Gower Distance – Handles mixed variable types.
Cosine Similarity – Common in text analysis.

K-Means Clustering

K-Means partitions data into k non-overlapping clusters. The process includes:

Randomly assign k centroids.
Assign observations to the nearest centroid.
Recalculate centroids.
Repeat until convergence.

Clustering minimizes within-cluster variation using squared Euclidean distance.

Choosing the Best K in K-Means

Methods for selecting k include:

Cross Validation
Elbow Method
Silhouette Method
X-Means Clustering

Hierarchical Clustering

This method creates a nested sequence of clusters using two approaches:

Agglomerative – Bottom-up merging.
Divisive – Top-down splitting.

Dendrograms visualize the clustering hierarchy. A horizontal cut across the dendrogram reveals the number of clusters.

Evaluation Methods

Clustering evaluation is divided into:

Internal Measures – Based on compactness and separation (e.g., SSE, Scatter Criteria).
External Measures – Based on known labels (e.g., Rand Index, Precision-Recall).

Clustering in R – Water Treatment Plants

The water treatment dataset from the UCI repository is used to demonstrate hierarchical and k-means clustering.

# Load and preprocess data
library(data.table)
library(ggplot2)
library(fpc)

water_data <- read.table("water-treatment.data.txt", sep = ",", header = F, na.strings = "?")
setDT(water_data)

# Impute missing values
for(i in colnames(water_data)[-1]) {
  set(water_data, which(is.na(water_data[[i]])), i, median(water_data[[i]], na.rm = TRUE))
}

# Scale numeric features
scaled_wd <- scale(water_data[,-1, with = FALSE])

Next, hierarchical clustering is performed using Euclidean distance and Ward's method. A dendrogram is plotted, and clusters are determined via horizontal cuts. PCA is used to visualize clusters.

# Hierarchical clustering
d <- dist(scaled_wd, method = "euclidean")
h_clust <- hclust(d, method = "ward.D2")
plot(h_clust, labels = water_data$V1)

# Cut dendrogram
rect.hclust(h_clust, k = 4)
groups <- cutree(h_clust, k = 4)

Principal components are used for cluster visualization:

# PCA for visualization
pcmp <- princomp(scaled_wd)
pred_pc <- predict(pcmp)[,1:2]
comp_dt <- cbind(as.data.table(pred_pc), cluster = as.factor(groups), Labels = water_data$V1)

ggplot(comp_dt, aes(Comp.1, Comp.2)) +
  geom_point(aes(color = cluster), size = 3)

Then, k-means clustering is applied and similarly visualized using PCA components. The clustering consistency is confirmed visually.

# K-means clustering
kclust <- kmeans(scaled_wd, centers = 4, iter.max = 100)

ggplot(comp_dt, aes(Comp.1, Comp.2)) +
  geom_point(aes(color = as.factor(kclust$cluster)), size = 3)

Developer Insights

13 Free Training Courses on Machine Learning and Artificial Intelligence

Introduction

When the world’s smartest companies such as Microsoft, Google, Alphabet Inc., and Baidu are investing heavily in Artificial Intelligence (AI), the world is going to sit up and take notice. Chinese Internet giant Baidu spent USD1.5 billion on research and development.

And as proof of China’s strong focus on AI and Machine Learning, Sinovation Ventures, a venture capital firm, invested USD0.1 billion in “25 AI-related startups” in the last three years in China and the U.S.

Research shows that although genuine intelligence may still be a bit far off, AI and Machine Learning technologies are still expected to reign in 2017. Try reading up on Microsoft Project Oxford, IBM Watson, Google Deep Mind, and Baidu Minwa, and you’ll understand what I am trying to get at.

In 2015, Gartner’s Hype Cycle for Emerging Technologies introduced Machine Learning (ML), and the graph showed (Figure 1) that it would reach a plateau in 2 to 5 years. Big players such as Facebook and Amazon are increasingly exploiting the advantages of this concept, which is derived from artificial intelligence and statistics, to extract meaning from huge amounts of (big) data.

Research predicts that the AI market will grow to about USD37 billion by 2025; in 2015 it was about USD645 million!

Source: Gartner

Difference between Machine Learning and Artificial Intelligence

Artificial Intelligence (AI) and ML are not interchangeable terms. ML is sort of a subset of AI, which is a part of computer science trying to develop “machines capable of intelligent behavior.” Then, what is Machine Learning (ML)? “The science of getting computers to act without being explicitly programmed,” says Stanford. So you get that difference? You need both AI and ML experts to make smart machines that are truly intelligent.

Machine learning challenge, ML challenge

Why are Machine Learning and Artificial Intelligence “Hot”?

"Machine learning is a core, transformative way by which we’re rethinking everything we’re doing” — Sundar Pichai, Google CEO

The pervasive commercial success of machine learning/artificial intelligence is visible everywhere—from Amazon recommending what movies you might like to see to self-driving Google cars that can tell a tree from a pedestrian.

AI/ML has changed how data-driven business leaders make decisions, gage their businesses, study human behavior, and view predictive analytics. If your organization needs to unleash the benefits of this extraordinary field, you need the right minds—quants and translators.

With breakthroughs such as parallel computation that’s cheap, Big Data, and improved algorithms, utilitarian AI is what the world is moving toward. The increased need to handle huge amounts of data and the number of IoT connected devices that define the world today reinforce the importance of machine learning.

AI/ML, with tons of potential, is a great career choice for engineers or data mining/ pattern recognition enthusiasts out there. Also, Machine Learning is integral to data science, which is touted as the sexiest job of the 21st century by the Harvard Business Review.

An Evans Data Corp. study found that 36% of the 500 developers surveyed use elements of ML in their Big Data or other analytical projects. CEO Janel Garvin said, “Machine learning includes many techniques that are rapidly being adopted at this time and the developers who already work with Big Data and advanced analytics are in an excellent position to lead the way.”

She added: “We are seeing more and more interest from developers in all forms of cognitive computing, including pattern recognition, natural language recognition, and neural networks and we fully expect that the programs of tomorrow are going to be based on these nascent technologies of today.”

So, for people who have a degree in Computer Science, Machine Learning, Operational Research, or Statistics, the world could well be their oyster for some time to come, right?

List of Courses

I’ve put together (and agonized a bit over what to add and what not to) a few free top ML and AI courses that will help you become the next ML expert Google or Apple hires. Of course, it is hard work, but if you are willing to pursue something, you’ll discover ways like these to succeed.

Machine Learning Courses

1. Machine Learning by Andrew Ng

Co-founder of Coursera, Andrew Ng, takes this 11-week course. He is an Associate Professor at Stanford University and the Chief Scientist at Baidu. As an applied machine learning class, it talks about the best machine learning techniques and statistical pattern recognition, and teaches you how to implement learning algorithms.

Broadly, it covers supervised and unsupervised learning, linear and logistic regression, regularization, and Naïve Bayes. He uses Octave and MatLab. The course is rich in case studies and recent practical applications. Students are expected to know the basics of probability, linear algebra, and computer science. The course has rave reviews from the users.

Go to Course: Start learning

2. Udacity’s Intro to Machine Learning

A part of Udacity’s Data Analyst Nanodegree, this approximately 10-week course teaches all you need to know to handle data sets using machine learning techniques to extract useful insights. Instructors Sebastian Thrun and Katie Malone will expect the beginners to know basic statistical concepts and Python.

This course teaches you everything from clustering to decision trees, from ML algorithms such as Adaboost to SVMs. People also recommend you take the foundational Intro to Data Science course which deals with Data Manipulation, Data Analysis, Data Communication with Information Visualization, and Data at Scale.

Go to Course: Start learning

3. EdX’s Learning from Data (Introductory Machine Learning)

Yaser S. Abu-Mostafa, Professor of Electrical Engineering and Computer Science at the California Institute of Technology, will teach you the basic theoretical principles, algorithms, and applications of Machine Learning.

The course requires an effort of 10 to 20 hours per week and lasts 10 weeks. They have another 5-week-course, Machine Learning for Data Science and Analytics, where newbies can learn more about algorithms.

Go to Course: Start learning

4. Statistical Machine Learning

Your instructor of the series of video lectures (on YouTube) in Advanced Machine Learning is Larry Wasserman, Professor in the Department of Statistics and in the Machine Learning Department at the Carnegie Mellon University.

The prerequisites for this course are his lectures on Intermediate Statistics and Machine Learning (10-715) intended for PhD students. If you can’t access these courses, you need to ensure you have the required math, computer science, and stats skills.

Go to Course: Start learning

5. Coursera’s Neural Networks for Machine Learning

Emeritus Distinguished Professor Gregory Hinton, who also works at Google’s Mountain View facility, from the University of Toronto teaches this 16-week advanced course offered by Coursera.

A pioneer in the field of deep learning, Hinton’s lecture videos on YouTube talk about the application of neural networks in image segmentation, human motion, modeling language, speech and object recognition, and so on. Students are expected to be comfortable with calculus and have requisite experience in Python programming.

Go to Course: Start learning

6. Google’s Deep Learning

Udacity offers this amazing free course which “takes machine learning to the next level.” Google’s 3-month course is not for beginners. It talks about the motivation for deep learning, deep neural networks, convolutional networks, and deep models for text and sequences.

Course leads Vincent Vanhoucke and Arpan Chakraborty expect the learners to have programming experience in Python and some GitHub experience and to know the basic concepts of ML and statistics, linear algebra, and calculus. The TensorFlow (Google’s own deep learning library) course has an added advantage of being self-paced.

Go to Course: Start learning

7. Kaggle R Tutorial on Machine Learning

DataCamp offers this interactive learning experience that’ll help you ace competitions. They also have an Introduction to R course for free.

Go to Course: Start learning

8. EdX’s Principles of Machine Learning

A part of the Microsoft Professional Program Certificate in Data Science, this 6-week course is an intermediate level one. It teaches you how to build and work with machine learning models using Python, R, and Azure Machine Learning.

Instructors, Dr. Steve Elston and Cynthia Rudin talk about classification, regression in machine learning, supervised models, non-linear modeling, clustering, and recommender systems. To add a verified certificate, you’ll need to pay.

9. Coursera’s Machine Learning Specialization

The University of Washington has created five courses, with practical case studies, to teach you the basics of Machine Learning. This 6-week course which requires between 5 and 8 hours of study a week, will cover ML foundations, classification, clustering, regression, recommender systems and dimensionality reduction, and project using deep learning.

Amazon’s Emily Fox and Carlos Guestrin are the instructors, and they expect the learners to have basic math and programming skills along with a working knowledge of Python. Course access is free though getting a valid certificate is not.

Go to Course: Start learning

Artificial Intelligence Courses

1. EdX's Artificial Intelligence

This exciting course from EdX talks about AI applications such as Robotics and NLP, machine learning (branch of AI) algorithms, data structures, games, and constraint satisfaction problems. It lasts 12 weeks and is an advanced-level tutorial from Columbia University.

Go to Course: Start learning

2. Udacity’s Intro to Artificial Intelligence

The course is expected to teach you AI’s “representative applications.” It is a part of its Machine Learning Engineer Nanodegree Program. Instructors Sebastian Thrun and Peter Norvig will take you through the fundamentals of AI, which include Bayes networks, statistics, and machine learning, and AI applications such as NLP, robotics, and image processing. Students are expected to know linear algebra and probability theory.

Go to Course: Start learning

3. Artificial Intelligence: Principles and Techniques

This Stanford course talks about how AI uses math tools to deal with complex problems such as machine translation, speech and face recognition, and autonomous driving. You can access the comprehensive lecture outline—machine learning concepts; tree search, dynamic programming, heuristics; game playing; Markov decision processes; constraint satisfaction problems; Bayesian networks; and logic— and assignments.

Go to Course: Start learning

4. Udacity's Artificial Intelligence for Robotics by Georgia Tech

Offered by Udacity, this course talks about programming a robotic car the way Stanford and Google do it. It is a part of the Deep Learning Nanodegree Foundation course. Sebastian Thrun will talk about localization, Kalman and Particle filters, PID control, and SLAM. Strong grasp of math concepts such as linear algebra and probability, knowledge of Python, and programming experience are good-to-have skills.

Go to Course: Start learning

Summary

In this post, a few of the listed courses are meant to help you get started in the exciting and fast-growing field of Machine Learning and Artificial Intelligence. Others take you through slightly more advanced aspects. The courses listed are free and the only thing stopping you from getting the most out of them will be a lack of commitment.

These world-class courses, which focus on a specific area of learning, are great stepping stones to lucrative and amazing careers in machine learning, data science, and so much more. If you don’t want the Baxters of the world to make you obsolete, you best teach them just who the master is.

So once you identify your learning goals, and assuming you have reliable access to technological requirements, be self-disciplined, build a study plan, set time limits, stay on schedule, work effectively with others, and, most of all, find ways to stay motivated.

Authors

Meet our Authors

Get to know the experts behind our content. From industry leaders to tech enthusiasts, our authors share valuable insights, trends, and expertise to keep you informed and inspired.

Ruehie Jaiya Karri

Kumari Trishya

Hiring Strategies

Forecasting Tech Hiring Trends For 2023 With 6 Experts

2023 is here, and it is time to look ahead. Start planning your tech hiring needs as per your business requirements, revamp your recruiting processes, and come up with creative ways to land that perfect “unicorn candidate”!

Right? Well, jumping in blindly without heeding what this year holds for you can be a mistake. So before you put together your plans, ask yourselves this—What are the most important 2023 recruiting trends in tech hiring that you should be prepared for? What are the predictions that will shape this year?

We went around and posed three important questions to industry experts that were on our minds. And what they had to say certainly gave us some food for thought!

Before we dive in, allow me to introduce you to our expert panel of six, who had so much to say from personal experience!

Meet the Expert Panel

Radoslav Stankov has more than 20 years of experience working in tech. He is currently Head of Engineering at Product Hunt. Enjoys blogging, conference speaking, and solving problems.

Mike “Batman” Cohen is the Founder of Wayne Technologies, a Sourcing-as-a-Service company providing recruitment data and candidate outreach services to enhance the talent acquisition journey.

Pamela Ilieva is the Director of International Recruitment at Shortlister, a platform that connects employers to wellness, benefits, and HR tech vendors.

Brian H. Hough is a Web2 and Web3 software engineer, AWS Community Builder, host of the Tech Stack Playbook™ YouTube channel/podcast, 5-time global hackathon winner, and tech content creator with 10k+ followers.

Steve O'Brien is Senior Vice President, Talent Acquisition at Syneos Health, leading a global team of top recruiters across 30+ countries in 24+ languages, with nearly 20 years of diverse recruitment experience.

Patricia (Sonja Sky) Gatlin is a New York Times featured activist, DEI Specialist, EdTechie, and Founder of Newbies in Tech. With 10+ years in Higher Education and 3+ in Tech, she now works part-time as a Diversity Lead recruiting STEM professionals to teach gifted students.

Overview of the upcoming tech industry landscape in 2024

Continued emphasis on remote work and flexibility: As we move into 2024, the tech industry is expected to continue embracing remote work and flexible schedules. This trend, accelerated by the COVID-19 pandemic, has proven to be more than a temporary shift. Companies are finding that remote work can lead to increased productivity, a broader talent pool, and better work-life balance for employees. As a result, recruiting strategies will likely focus on leveraging remote work capabilities to attract top talent globally.

Rising demand for AI and Machine Learning Skills: Artificial Intelligence (AI) and Machine Learning (ML) continue to be at the forefront of technological advancement. In 2024, these technologies are expected to become even more integrated into various business processes, driving demand for professionals skilled in AI and ML. Companies will likely prioritize candidates with expertise in these areas, and there may be an increased emphasis on upskilling existing employees to meet this demand.

Increased focus on cybersecurity: With the digital transformation of businesses, cybersecurity remains a critical concern. The tech industry in 2024 is anticipated to see a surge in the need for cybersecurity professionals. Companies will be on the lookout for talent capable of protecting against evolving cyber threats and ensuring data privacy.

Growth in cloud computing and edge computing: Cloud computing continues to grow, but there is also an increasing shift towards edge computing – processing data closer to where it is generated. This shift will likely create new job opportunities and skill requirements, influencing recruiting trends in the tech industry.

Sustainable technology and green computing: The global emphasis on sustainability is pushing the tech industry towards green computing and environmentally friendly technologies. In 2024, companies may seek professionals who can contribute to sustainable technology initiatives, adding a new dimension to tech recruiting.

Emphasis on soft skills: While technical skills remain paramount, soft skills like adaptability, communication, and problem-solving are becoming increasingly important. Companies are recognizing the value of these skills in fostering innovation and teamwork, especially in a remote or hybrid work environment.

Diversity, Equity, and Inclusion (DEI): There is an ongoing push towards more diverse and inclusive workplaces. In 2024, tech companies will likely continue to strengthen their DEI initiatives, affecting how they recruit and retain talent.

6 industry experts predict the 2023 recruiting trends

#1 We've seen many important moments in the tech industry this year...

Rado: In my opinion, a lot of those will carry over. I felt this was a preparation year for what was to come...

Mike: I wish I had the crystal ball for this, but I hope that when the market starts picking up again...

Pamela: Quiet quitting has been here way before 2022, and it is here to stay if organizations and companies...

Pamela Ilieva, Director of International Recruitment, Shortlister

Also, read: What Tech Companies Need To Know About Quiet Quitting

Brian: Yes, absolutely. In the 2022 Edelman Trust Barometer report...

Steve: Quiet quitting in the tech space will naturally face pressure as there is a redistribution of tech talent...

Patricia: Quiet quitting has been around for generations—people doing the bare minimum because they are no longer incentivized...

Patricia Gatlin, DEI Specialist and Curator, #blacklinkedin

#2 What is your pro tip for HR professionals/engineering managers...

Rado: Engineering managers should be able to do "more-with-less" in the coming year.

Radoslav Stankov, Head of Engineering, Product Hunt

Mike: Well first, (shameless plug), be in touch with me/Wayne Technologies as a stop-gap for when the time comes.

Mike “Batman” Cohen, Founder of Wayne Technologies

It's in the decrease and increase where companies find the hardest challenges...

Pamela: Remain calm – no need to “add fuel to the fire”!...

Brian: We have to build during the bear markets to thrive in the bull markets.

Companies can create internal hackathons to exercise creativity...

Also, read: Internal Hackathons - Drive Innovation And Increase Engagement In Tech Teams

Steve: HR professionals facing a hiring freeze will do well to “upgrade” processes, talent, and technology aggressively during downtime...

Steve O'Brien, Senior Vice President, Talent Acquisition at Syneos Health

Patricia: Talk to hiring managers in all your departments. Ask, what are the top 3-5 roles they are hiring for in the new year?...

Also, watch: 5 Recruiting Tips To Navigate The Hiring Freeze With Shalini Chandra, Senior TA, HackerEarth

#3 What top 3 skills would you like HR professionals/engineering managers to add to their repertoire in 2023 to deal with upcoming challenges?

6 industry experts predict the 2023 recruiting trends

Rado: Prioritization, team time, and environment management.

I think "prioritization" and "team time" management are obvious. But what do I mean by "environment management"?

A productive environment is one of the key ingredients for a productive team. Look at where your team wastes most time, which can be automated. For example, end-to-end writing tests take time because our tools are cumbersome and undocumented. So let's improve this.

Mike: Setting better metrics/KPIs, moving away from LinkedIn, and sharing more knowledge.

Metrics/KPIs: Become better at setting measurable KPIs and accountable metrics. They are not the same thing—it's like the Square and Rectangle. One fits into the other but they're not the same. Hold people accountable to metrics, not KPIs. Make sure your metrics are aligned with company goals and values, and that they push employees toward excellence, not mediocrity.
Freedom from LinkedIn: This is every year, and will probably continue to be. LinkedIn is a great database, but it is NOT the only way to find candidates, and oftentimes, not even the most effective/efficient. Explore other tools and methodologies!
Join the conversation: I'd love to see new names of people presenting at conferences and webinars. And also, see new authors on the popular TA content websites. Everyone has things they can share—be a part of the community, not just a user of. Join FB groups, write and post articles, and comment on other people's posts with more than 'Great article'. It's a great community, but it's only great because of the people who contribute to it—be one of those people.

Pamela: Resilience, leveraging data, and self-awareness.

Resilience: A “must-have” skill for the 21st century due to constant changes in the tech industry. Face and adapt to challenges. Overcome them and handle disappointments. Never give up. This will keep HR people alive in 2023.
Data skills: Get some data analyst skills. The capacity to transfer numbers into data can help you be a better HR professional, prepared to improve the employee experience and show your leadership team how HR is leveraging data to drive business results.
Self-awareness: Allows you to react better to upsetting situations and workplace challenges. It is a healthy skill to cultivate – especially as an HR professional.

Also, read: Diving Deep Into The World Of Data Science With Ashutosh Kumar

Brian: Agility, resourcefulness, and empathy.

Agility: Allows professionals to move with market conditions. Always be as prepared as possible for any situation to come. Be flexible based on what does or does not happen.
Resourcefulness: Allows professionals to do more with less. It also helps them focus on how to amplify, lift, and empower the current teams to be the best they can be.
Empathy: Allows professionals to take a more proactive approach to listening and understanding where all workers are coming from. Amid stressful situations, companies need empathetic team members and leaders alike who can meet each other wherever they are and be a support.

Steve: Negotiation, data management, and talent development.

Negotiation: Wage transparency laws will fundamentally change the compensation conversation. We must ensure we are still discussing compensation early in the process. And not just “assume” everyone’s on the same page because “the range is published”.
Data management and predictive analytics: Looking at your organization's talent needs as a casserole of indistinguishable components and demands will not be good enough. We must upgrade the accuracy and consistency of our data and the predictions we can make from it.

Also, read: The Role of Talent Intelligence in Optimizing Recruitment

Talent development: We’ve been exploring the interplay between TA and TM for years. Now is the time to integrate your internal and external talent marketplaces. To provide career experiences to people within your organization and not just those joining your organization.

Patricia: Technology, research, and relationship building.

Technology: Get better at understanding the technology that’s out there. To help you speed up the process, track candidate experience, but also eliminate bias. Metrics are becoming big in HR.
Research: Honestly, read more books. Many great thought leaders put out content about the “future of work”, understanding “Gen Z”, or “quiet quitting.” Dedicate work hours to understanding your ever-changing field.
Relationship Building: Especially in your immediate communities. Most people don’t know who you are or what exactly it is that you do. Build your personal brand and what you are doing at your company to impact those closest to you. Create a referral funnel to get a pipeline going. When people want a job you and your company ought to be top of mind. Also, tell the stories of the people that work there.

Hiring Strategies

7 Tech Recruiting Trends To Watch Out For In 2024

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

6 Tech Recruiting Trends To Watch Out For In 2022

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.

Trend #2—Have impactful employer branding

98% of recruiters believe promoting company culture helps sourcing efforts as seen in our 2021 State Of Developer Recruitment report.

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.

Recommended read: What NOT To Do When Recruiting Fresh 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.

—Swetha Harikrishnan, Sr. HR Director, HackerEarth

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.

Trend #6—Conduct remote interviews

With 32.5% of companies planning to stay remote, remote interviewing is here to stay.

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.

—Narayani Gurunathan, CEO, PlaceNet Consultants

Recruiting Tech Talent Just Got Easier With HackerEarth

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.

Our tech recruiting platform enables you to:

Tap into a 6 million-strong developer community
Host custom hackathons to engage talent and boost your employer brand
Create online assessments to evaluate 80+ tech skills
Use dev-friendly IDEs and proctoring for reliable evaluations
Benchmark candidates against a global community
Conduct live coding interviews with FaceCode, our collaborative coding interview tool
Guide upskilling journeys via our Learning and Development platform
Integrate seamlessly with all leading ATS systems
Access 24/7 support with a 95% satisfaction score

Recommended read: The A-Zs Of Tech Recruiting - A Guide

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.

Ready to streamline your tech recruiting? Try HackerEarth for Enterprises today.

Developer Insights

Code In Progress - The Life And Times Of Developers In 2021

Developers. Are they as mysterious as everyone makes them out to be? Is coding the only thing they do all day? Good coders work around the clock, right?

While developers are some of the most coveted talent out there, they also have the most myths being circulated. Most of us forget that developers too are just like us. And no, they do not code all day long.

We wanted to bust a lot of these myths and shed light on how the programming world looks through a developer’s lens in 2021—especially in the wake of a global pandemic. This year’s edition of the annual HackerEarth Developer Survey is packed with developers’ wants and needs when choosing jobs, major gripes with the WFH scenario, and the latest market trends to watch out for, among others.

Our 2021 report is bigger and better, with responses from 25,431 developers across 171 countries. Let’s find out what makes a developer tick, shall we?

Developer Survey

“Good coders work around the clock.” No, they don’t.

Busting the myth that developers spend the better part of their day coding, 52% of student developers said that they prefer to code for a maximum of 3 hours per day.

When not coding, devs swear by their walks as a way to unwind. When we asked devs the same question last year, they said they liked to indulge in indoor games like foosball. In 2021, going for walks has become the most popular method of de-stressing. We’re chalking it up to working from home and not having a chance to stretch their legs.

Staying ahead of the skills game

Following the same trend as last year, students (39%) and working professionals (44%) voted for Go as one of the most popular programming languages that they want to learn. The other programming languages that devs are interested in learning are Rust, Kotlin, and Erlang.

Programming languages that students are most skilled at are HTML/CSS, C++, and Python. Senior developers are more comfortable working with HTML/CSS, SQL, and Java.

How happy are developers

Employees from middle market organizations had the highest 'happiness index' of 7.2. Experienced developers who work at enterprises are marginally less happy in comparison to people who work at smaller companies.

However, happiness is not a binding factor for where developers work. Despite scoring the least on the happiness scale, working professionals would still like to work at enterprise companies and growth-stage startups.

What works when looking for work

Student devs (63%), who are just starting in the tech world, said a good career growth curve is a must-have. Working professionals can be wooed by offers of a good career path (69%) and compensation (68%).

One trend that has changed since last year is that at least 50% of students and working professionals alike care a lot more about ESOPs and positive Glassdoor reviews now than they did in 2020.

To know more about what developers want, download your copy of the report now!

We went a step further and organized an event with our CEO, Sachin Gupta, Radoslav Stankov, Head of Engineering at Product Hunt, and Steve O’Brien, President of Talent Solutions at Job.com to further dissect the findings of our survey.

Tips straight from the horse’s mouth

Steve highlighted how the information collated from the developer survey affects the recruiting community and how they can leverage this data to hire better and faster.

The insight where developer happiness is correlated to work hours didn’t find a significant difference between the cohorts. Devs working for less than 40 hours seemed marginally happier than those that clocked in more than 60 hours a week.

“This is an interesting data point, which shows that devs are passionate about what they do. You can increase their workload by 50% and still not affect their happiness. From a work perspective, as a recruiter, you have to get your hiring manager to understand that while devs never say no to more work, HMs shouldn’t overload the devs. Devs are difficult to source and burnout only leads to killing your talent pool, which is something that you do not want,” says Steve.

Roughly 45% of both student and professional developers learned how to code in college was another insight that was open to interpretation.

“Let’s look at it differently. Less than half of the surveyed developers learned how to code in college. There’s a major segment of the market today that is not necessarily following the ‘college degree to getting a job’ path. Developers are beginning to look at their skillsets differently and using various platforms to upskill themselves. Development is not about pedigree, it’s more about the potential to demonstrate skills. This is an interesting shift in the way we approach testing and evaluating devs in 2021.”

Rado contextualized the data from the survey to see what it means for the developer community and what trends to watch out for in 2021.

Node.js and AngularJS are the most popular frameworks among students and professionals.

“I was surprised by how many young students wanted to learn AngularJS, given that it’s more of an enterprise framework. Another thing that stood out to me was that the younger generation wants to learn technologies that are not necessarily cool like ExtJS (35%). This is good because people are picking technologies that they enjoy working with instead of just going along with what everyone else is doing. This also builds a more diverse technology pool.” — Rado

22% of devs say ‘Zoom Fatigue’ is real and directly affects productivity.

“Especially for younger people who still haven’t figured out a routine to develop their skills, there is something I’d like you to try out. Start using noise-canceling headphones. They help keep distractions to a minimum. I find clutter-free working spaces to be an interesting concept as well.”

The last year and a half have been a doozy for developers everywhere, with a lot of things changing, and some things staying the same. With our developer survey, we wanted to shine the spotlight on skill-based hiring and market trends in 2021—plus highlight the fact that developers too have their gripes and happy hours.

Uncover many more developer trends for 2021 with Steve and Rado below:

View all

Hiring Strategies

Best Pre-Employment Assessments: Optimizing Your Hiring Process for 2024

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:

HackerEarth:

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.
SHL:

Provides a broad selection of assessments, including skill tests, personality assessments, and cognitive ability tests. They offer customizable solutions and cater to various industries.
Pymetrics:

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.

Hiring Strategies

Tech Layoffs: What To Expect In 2024

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.

Causes for layoffs in the tech industry

Why are tech employees suffering so much?

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.

Also, read: 6 Steps To Create A Detailed Recruiting Budget (Template Included)

Changes in demand

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.

Also, read: 4 Images That Show What Developers Think Of Layoffs In Tech

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:

Use tech layoffs tracker

Layoff trackers like thelayoff.com and layoffs.fyi provide up-to-date information on 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.

Also, read: What Recruiters Can Focus On During A Tech Hiring Freeze

Non-essential expense reduction

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! :)

Hiring Tools

What is Headhunting In Recruitment?: Types & How Does It Work?

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.

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Developer Insights

Top 13 (free) must read machine leaning books for beginners

Top machine learning books to read for beginners

Matrix Computations

A Probabilistic Theory of Pattern Recognition

Advanced Engineering Mathematics

Probability and Statistics Cookbook

An Introduction to Statistical Learning (with applications in R)

Probabilistic Programming and Bayesian Methods for Hackers

The Elements of Statistical Learning

Bayesian Reasoning and Machine Learning

Information Theory, Inference, and Learning Algorithms

Deep Learning

Neural Networks and Deep Learning

Supervised Sequence Labelling with Recurrent Neural Networks

Reinforcement Learning: An Introduction

Summary

Introduction to Naive Bayes Classification Algorithm in Python and R

Variations of the Naive Bayes algorithm

Pros and Cons of Naive Bayes algorithm

Pros

Cons

Naive Bayes with Python and R

R Code

Python Code

Applications

Conclusion

Simple Guide to Neural Networks and Deep Learning in Python

Top programming languages used in IoT

C

C++

Go

JavaScript

Python

Rust

Java

Assembly language

ParaSail

R

B#

Forth

HiveQL

Pig Latin

Julia

Practical Guide to Clustering Algorithms and Evaluation in R

Introduction

Table of Contents

Types of Clustering Techniques

Distance Calculation for Clustering

K-Means Clustering

Choosing the Best K in K-Means

Hierarchical Clustering

Evaluation Methods

Clustering in R – Water Treatment Plants

13 Free Training Courses on Machine Learning and Artificial Intelligence

Introduction

Difference between Machine Learning and Artificial Intelligence

Why are Machine Learning and Artificial Intelligence “Hot”?

List of Courses

Machine Learning Courses

1. Machine Learning by Andrew Ng

2. Udacity’s Intro to Machine Learning

3. EdX’s Learning from Data (Introductory Machine Learning)

4. Statistical Machine Learning

5. Coursera’s Neural Networks for Machine Learning

6. Google’s Deep Learning

7. Kaggle R Tutorial on Machine Learning

8. EdX’s Principles of Machine Learning

9. Coursera’s Machine Learning Specialization

Artificial Intelligence Courses

1. EdX's Artificial Intelligence

2. Udacity’s Intro to Artificial Intelligence

3. Artificial Intelligence: Principles and Techniques

4. Udacity's Artificial Intelligence for Robotics by Georgia Tech

Summary

Technical Screening Guide: All You Need To Know

Explore HackerEarth’s top products for Hiring & Innovation

Meet our Authors

Forecasting Tech Hiring Trends For 2023 With 6 Experts

Meet the Expert Panel