IT certifications have always been playing a vital role in getting a job or required knowledge.

In an interview, if you have a certification, you have more advantages to get the job and I have experienced it personally.

There are lots of other channels as well to learn or to enhance the knowledge and skills these days but the thing which matters a lot is the certification, and no one can give a certified degree instead of an institute, and like the way things are evolving the demand of certification is getting increased as they need an expert for their work.

So having knowledge before going to ask for the job is much beneficial to you.

So today I am going to share the top 10 high-paying IT certifications in the world in 2022. So let’s begin.

Master in DevOps engineering (MDE) certification – This certification gives you entire information about DevOps and their related toolsets.

DevOps is just a process to be followed to achieve a high quality of software by continuous integration and continuous delivery, and their open-source tools help it to achieve the goal efficiently and effectively.

Basically, DevOps only direct the way but the major works are done by these toolsets and you can have the proper knowledge and skills by only getting trained in any institute and have the completion certification.

The demand for certification is getting higher to get a good job role in the IT sector.

DevOps is liable to do the planning, designing, coding, testing, deploying, and monitoring.

As DevOps has shown its capability the salary of candidates will be more in 2022 and will be continued.

Site reliability engineering (SRE) certification – SRE is also one of the important certifications. SRE is mainly focused on operations where the goal of SRE is to improve the reliability of software systems, through automation and continuous integration and delivery.

SRE has also open-source toolsets that cover during the certification. SRE has shown tremendous growth till now and getting used all over the world.

It’s expecting the demand of SRE would be consistent and will be on a high-paying salary list.

SRE is for those software engineers who want to work as an operation team.

DevSecOps certified professional certification – It had been forecasted to be achieved a growth of 33.7% during the period of 2017-2023. And even it has been seen the growth in the market.

So as per the result, it will dominate the market in 2022 as well.

The national average salary for a Devsecops Engineer is Rs 10,00,000 in India.

DevSecOps course is for security professionals who are willing to work in the security field like cyber security.

DevSecOp’s assumption is security is everyone’s priority and everyone should work by keeping security concerns in mind. DevSecOps also works in the collaboration with DevOps.

Docker Certified Associate (DCA) certification – Docker is a containerization tool that creates containers and allows to build, test and deploy applications.

This certification helps to learn how Docker is used to package and ship the app as well as how to create containers and so many things.

Docker has become the number 1 choice of all companies and its demand is high.

The average salary of Docker candidates in India is Rs 4,79, 074 to Rs 8,14,070, and in the USA $1,45000.

Being a Docker certified candidate is much important to get a job.

Certified Kubernetes Administrator (CKA) Certification – It has been seen CKA course is at the top to get the certification into. Kubernetes are much important to organize the containers. So Kubernetes certification is important as here you will learn so many things and most significantly how to integrate with Docker to work with.

Kubernetes has already shown its growth as it is in demand at all companies.

Kubernetes candidates can earn salaries up to 6 to 8 lakh in India and in USA between $92,500 and $147,500 per year as per a new report.

AIOps Certified Professional (AIOCP) certification – AIOps stands for artificial intelligence for operations drive automation to solve the issues by speed analyzing the root cause of the issue and taking care of the events with any human interruption.

AIOps is now trending to market and is achieving heights of success. So the growth of AIOps is getting really good and opening so many job roles in AI.

AIOps certification is very important to get into this job domain as certification can grow your chances more to pass the interview and to get the full knowledge.

Based on research the average salary of AIOps is 21 lakh per annum in India.

Master in artificial intelligence – The AI is future and there is no doubt the candidates who are trying to achieve mastery in AI studies have a great future.

Certification is playing a key role here to get your foot into the AI world.

Having good knowledge and the advantage to get the priority in an interview is not so bad. This is the advantage of certifications.

The average salary of AI in the USA is $164, 769 and in India Rs 9,01,800 per annum.

AI is the main driver of emerging technologies like big data, robotics, and IoT.

GitOps certification – GitOps is a set of practices to manage infrastructure and application configurations using Git.

Gitops uses Git as the main repository for managing all the information, documentation. It maintains infrastructure as code and keeps them too in Git.

Some developers believe Gitops is the future of DevOps that replace the Developer part with a single repository that grasps all the information needed by a developer. 

That’s why GitOps certification is important.

GitOps employee’s salary is also high according to experience. One candidate has 45 lakh per annum.

The job openings are also in good numbers to apply.

MlOps certification – Mlops is communication between data scientists and the operation or production team, it is deeply collaborative in nature, designed to eliminate waste automate as much as possible, and produce richer and consistent insights through machine learning.

Mlops is the major function of machine learning engineering.

Mlops Goals –

• faster experimentation and model development
• faster deployment of the updated model into production
• Quality assurance.

The salary for an MLOps Engineer in India is approx Rs 11,40,000 per annum.

It has been predicted to be more job openings in 2022 and the certification is a must to get the job as certification can give you an advantage during an interview.

Its shows at least you have such knowledge pertaining to this and you are trained.

DataOps certification – As per Andy Palmer “DataOps is a data management method that emphasizes communication, collaboration, integration, automation, and measurement of cooperation between data engineers, data scientists, and other data professionals”.

The aim of DataOps is to quickly deliver business value from data.

The DatOps engineer’s salary in India is Rs 7,78,290 and  $92,468 in the United States per annum.

It is predicted, to improve data quality and reduce time to insight, enterprises will increasingly embrace DataOps practices across the data life cycle in 2022.

The certification will play a vital role here to get the job as Dataops is new and also it has so many scenarios to cover so certification is a must.

And it has always been seen certifications always give an advantage during an interview. That means certification increase the status of your knowledge as well as your resume.

                      Training Place

I would like to tell you about one of the best places to get trained and certification in DevOps, DevSecOps, SRE, AIOps, MLOps, GitOps, AI, and Machine learning courses is DevOpsSchool. This Platform offers the best trainers who have good experience in DevOps and also they provide a friendly eco-environment where you can learn comfortably and free to ask anything regarding your course and they are always ready to help you out whenever you need, that’s why they provide pdf’s, video, etc. to help you.

They also provide real-time projects to increase your knowledge and to make you tackle the real face of the working environment. It will increase the value of yours as well as your resume. So do check this platform if you guys are looking for any kind of training in any particular course and tools.

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Check out 10 top rising cloud robotics start-ups

Cloud robotics is an area of robotics that attempts to entreat cloud technologies such as cloud computing, cloud storage, and other internet technologies. It is not an easy task to run a cloud robotics start-ups. But it is amazing to see the rising number of start-ups specializing in cloud robotics. These start-ups are seen to work on innovative technologies.

Here is the list of 10 cloud robotics start-ups that are working on the innovation of technologies and products.

Graphcore

It was founded in 2016. The company is situated in the United Kingdom. The company’s IPU accelerators and popular software together make the fastest and most ductile platform for current and future machine intelligence applications, which lowers the cost of AI in the cloud and datacentre, improves performance and efficiency by between 10x to 100x. Graphcore systems shine at both training and reasoning. The highly collateral computational resources along with graph software tools and libraries enable researchers to explore machine intelligence across a much wider front than current solutions. This technology lets recent success in deep learning evolve rapidly towards useful, general artificial intelligence.

Arctoris

The company was founded in 2016 and is situated in the United Kingdom. Arctoris serves academic research groups and biotech companies, enabling scientists to directly configure a broad range of cancer research experiments online, and collects their results and detailed methods report instantly. It is not only effective for screening but is created for fundamental research, surrounding a wide range of techniques to cover cellular and molecular biology. Arctoris enables researchers and entrepreneurs to create and implement cutting-edge cellular and molecular biology research experiments and offers a complete system to recognize, analyze and visualize research data.

INVOLI

The company was founded in 2017, Switzerland. INVOLI serves in building infrastructure that enables drone revolution, ensuring their safe consolidation into today’s aviation world and avoiding collisions with aircraft. It offers an online platform, which collects data from a network of scaled-down control towers gathering maximum information on the position of every flying object in the sky, manually or automatically. Its technology enables everybody to see the air traffic encompassing them and take the most appropriate measures. Thus, what INVOLI brings to the aviation world is intelligent air traffic instruments and automatic collision avoidance tools, allowing low-risk flight plans and avoidance strategies and thus creating a safer, smarter sky for all.

Salty Cloud, PBC (Public Benefit Company)

It was founded in March 2017 in, USA. The company offers next-generation security automation technology to assist organizations around the globe to protect their cyber adversaries. Today, the company provides three security & risk automation tools to the public. It continues to find ways to provide free versions of its tools for educational institutions, state & local governments, and non-profits. One such offering is Dorkbot, the web vulnerability scanning service.

Bright Machines

It was founded in 2018 in, USA. The company aims at making manufacturing smarter and more efficient. It merges intelligent software with flexible factory robots and machine learning to help the customers deliver the next-generation products

Soar Robotics

The company offers independent operations at scale for urban and industrial applications with seamless connectivity and cloud intelligence. It uses hyper-realistic visual and physical simulation environments to safely test hardware by recreating real-life scenarios in virtual worlds. It helps in executing custom applications for proprietary use cases.

Freedom Robotics

It was founded in 2018, headquartered in San Francisco. Freedom Robotics integrates development and management tools in its cloud-based robotics management software (RMS) to help in prototype, creating, operating, and scaling robot fleets.

Covariant

It was founded in 2017, headquartered in Berkeley, Calif.

Covariant is creating AI called the “Covariant Brain” to make robots smarter. Covariant is competing directly with other cloud robotics start-ups such as Kindred and RightHand Robotics. Its technique is to use a variety of AI methods to train its robots, including reinforcement learning. It merges that with an off-the-shelf robot arm, a suction gripper, and a simple 2D camera system.

Aitomation – Workflow Automation

This company leverages robots (physically and virtually) for automating specific tasks. It utilizes the power of cloud computing and process automation to create and modify cloud robotic solutions. The company develops software robotics solutions, lean strategies, artificial intelligence (AI), and low-code applications, as well as cloud services. It allows the automation of long workflows and processes that run across multiple systems and organizational extremities.

RosHub – Robotic Fleet Management

It is a US-based start-up. It develops connectivity solutions for robot fleets. The company is also involved in prototyping, testing, and applying robotic solutions in unstructured public spaces. It allows the link of an open-source ROS (robotics operating system) that is combined efficiently with the support of existing tools and APIs.

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Analytics Insight presents the top 10 Artificial Intelligence mergers and acquisitions in 2021

The sudden outbreak of the coronavirus pandemic has created a drastic effect on all types of businesses since March 2020. There are several Artificial Intelligence mergers and acquisitions that happened or yet to happen within hi-tech companies. The tech giants are always ready to acquire small companies that can help in earning better revenue and customer engagement with new products and services in the global market. This provides a competitive edge for the hi-tech companies in the tech-driven market across the world. Let’s explore the top 10 Artificial Intelligence mergers and acquisitions in 2021 for the welfare of society.

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Microsoft to Acquire RiskIQ

Recently, Microsoft has acquired a leader in global threat intelligence and attack surface management company known as RiskIQ in July 2021. Microsoft requires strengthening the cybersecurity of digital transformation in its hybrid work environment due to the pandemic. RiskIQ has an aim to help its customers detect and access the cybersecurity of the enterprise attack surface and the supply chain. Microsoft is focused on enhancing security tools for defense against multiple cyberattacks in recent times. The Microsoft security portfolio now consists of the attack surface as well as threat intelligence solutions of RiskIQ for better customer satisfaction.

Microsoft Acquires ReFirm Labs

Microsoft is very active in Artificial Intelligence acquisitions for providing better customer service in this tech-driven world in June 2021. The aim of Microsoft is to enhance IoT security and chip-to-cloud protection for the firmware with the help of ReFirm Labs. ReFirm Labs have started enriching firmware analysis and security capabilities from the intelligent edge as well as IoT. This acquisition has brought together top-notch expertise in firmware security and the Centrifuge firmware platform. The customers can easily discover, protect and assess cyber threats at the firmware as well as the network label.

AWS to Acquire Wickr

AWS has acquired Wickr in June 2021 to provide benefits to customers and partners with advanced security features that are not available with traditional communications services. Wickr is an innovative company to brings the industry’s most secure and end-to-end encrypted communication technology in this digital world. AWS required this acquisition due to the hybrid work environment amidst the pandemic. This acquisition will help organizations adapt to the new ecosystem and collaborate more to offer new products and services to customers and partners.

IBM acquired Turbonomic

IBM acquired Turbonomic in June 2021 to drive the future of AI-driven, cross-cloud application operations. The aim of this acquisition is to help customers in application performance and governance for any application hosting platform. Turbonomic is known for its Application Resource Management that provides an application-driven consumption model to assure performance and governance. Meanwhile, IBM is known for its hybrid cross-cloud management software and services.

Pax8 merged with RocketCyber

Pax8 merged with RocketCyber in January 2021 to enable Managed Service Providers for clients through industry-leading tools. Pax8 is known as the best place for IT to purchase Cloud products. It merged with RocketCyber to deliver continuous cybersecurity detection and response efficiently. Pax8 and RocketCyber together have an aim to build technology stacks against the rise of cyberattacks amidst the pandemic.  This merger will provide exceptional security and threat protection solutions to integrate with MSP’s technology stack. The threat intelligence software and threat detection applications enable MSP to deliver risk monitoring continuously.

SentinelOne Acquires Scalyr

SentinelOne acquired Scalyr in February 2021to ingest, correlate, search, and action data from any source by delivering the industry’s most advanced integrated XDR platform. SentinelOne is known as the autonomous cybersecurity platform company and Scalyr is known as a leading cloud-native and cloud-scale data analytics platform. This acquisition is essential for building an effective XDR with the SaaS platform of Scalyr. SentinelOne can provide autonomous protection by creating a real-time data lake for multiple structured and unstructured data.

Google’s WearOS merged with Samsung’s Tizen

Google and Samsung decided to merge their WearOS and Tizen in May 2021to significantly improve battery life and 30% faster loading time with smoother animations. This merger will also help in simplifying the life of developers to create one central smartwatch OS for the Android platform. It will bring a unified wearable platform with more choices for consumers. Developers can add a customized user experience and Android tools to this platform. There will be Google Maps, YoutTube Music app and Spotify integrated into Tizen.

Jamf Acquires Wandera

Being a leader in Apple Enterprise Management, Jamf has acquired Wandera in July 2021. Wandera is a leader in zero-trust cloud security and access and the acquisition will provide help to IT and security teams in protecting smart devices, confidential data as well as applications. Wandera has developed its solutions to generate a technical advantage to customers and Apple-best unified cloud security functionalities are a bonus. It will integrate with Cloud identity providers to ensure the security of the company resources.

Amazon Merged with MGM

Amazon merged with Metro Goldwyn Mayer in May 2021 with US$8.45 billion. Being a popular technology company, Amazon is focused on preserving the heritage of MGM and enhancing customer engagement with a catalog of films. Amazon follows four principles— customer obsession, operational commitment, passion for invention as well as long-term thinking. Amazon is the most customer-centric company with AWS and Artificial Intelligence-based products.

Accenture to Acquire Umlaut

Accenture announced in June 2021, that it has decided to acquire Umlaut to meet the accelerated demand to apply data and digital to design multiple products for leading companies worldwide. It has also expanded Industry X services to allow productivity and growth in core operations. Umlaut is an engineering consulting and services in Germany. This acquisition will add over 4,200 industry-leading engineers and consultants across the world to Industry X services. This will help companies to use digital technologies such as Artificial Intelligence, 5G, and many more to embed sustainability.

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Top 10 tools a data scientist should use in 2021

The work of a data scientist centers around the process of extraction of meaningful data from unstructured information and analyzing that data for necessary interpretation. This requires a lot of useful tools. The following are the top 10 most necessary tools that a data scientist needs to know about in 2021.

Python

Python is the most widely used programming language for data science and machine learning and one of the most popular languages overall. The Python open-source project’s website describes it as “an interpreted, object-oriented, high-level programming language with dynamic semantics,” as well as built-in data structures and dynamic typing and binding capabilities. The site also touts Python’s simple syntax, saying it’s easy to learn and its emphasis on readability reduces the cost of program maintenance. The multipurpose language can be used for a wide range of tasks, including data analysis, data visualization, AI, natural language processing, and robotic process automation. Developers can create web, mobile, and desktop applications in Python, too. In addition to object-oriented programming, it supports procedural, functional, and other types, plus extensions written in C or C++.

Jupyter Notebook

Jupyter Notebook is an open-source web application that enables interactive collaboration among data scientists, data engineers, mathematicians, researchers, and other users. It’s a computational notebook tool that can be used to create, edit and share code, as well as explanatory text, images, and other information. Jupyter users can add software code, computations, comments, data visualizations, and rich media representations of computation results to a single document, known as a notebook, which can then be shared with and revised by colleagues. As a result, notebooks “can serve as a complete computational record” of interactive sessions among the members of data science teams, according to Jupyter Notebook’s documentation. The notebook documents are JSON files that have version control capabilities. In addition, a Notebook Viewer service enables them to be rendered as static web pages for viewing by users who don’t have Jupyter installed on their systems.

Apache Spark

Apache Spark is an open-source data processing and analytics engine that can handle large amounts of data, upward of several petabytes, according to proponents. Spark’s ability to rapidly process data has fueled significant growth in the use of the platform since it was created in 2009, helping to make the Spark project one of the largest open-source communities among big data technologies. Due to its speed, Spark is well suited for continuous intelligence applications powered by near-real-time processing of streaming data. However, as a general-purpose distributed processing engine, Spark is equally suited for extract, transform and load uses and other SQL batch jobs. Spark initially was touted as a faster alternative to the MapReduce engine for batch processing in Hadoop clusters.

D3.js

Another open-source tool, D3.js is a JavaScript library for creating custom data visualizations in a web browser. Commonly known as D3, which stands for Data-Driven Documents, it uses web standards, such as HTML, Scalable Vector Graphics, and CSS, instead of its graphical vocabulary. D3’s developers describe it as a dynamic and flexible tool that requires a minimum amount of effort to generate visual representations of data. D3.js lets visualization designers bind data to documents via the Document Object Model and then use DOM manipulation methods to make data-driven transformations to the documents. First released in 2011, it can be used to design various types of data visualizations and supports features such as interaction, animation, annotation, and quantitative analysis. D3 includes more than 30 modules and 1,000 visualization methods, making it complicated to learn. In addition, many data scientists don’t have JavaScript skills. As a result, they may be more comfortable with a commercial visualization tool, like Tableau, leaving D3 to be used more by data visualization developers and specialists who are also members of data science teams.

TensorFlow

TensorFlow is an open-source machine learning platform developed by Google that’s particularly popular for implementing deep learning neural networks. The platform takes inputs in the form of tensors that are akin to NumPy multidimensional arrays and then uses a graph structure to flow the data through a list of computational operations specified by developers. It also offers an eager execution programming environment that runs operations individually without graphs, which provides more flexibility for research and debugging machine learning models. Google made TensorFlow open source in 2015, and Release 1.0.0 became available in 2017. TensorFlow uses Python as its core programming language and now incorporates the Keras high-level API for building and training models. Alternatively, a TensorFlow.js library enables model development in JavaScript, and custom operations can be built in C++.

Keras

Keras is a programming interface that enables data scientists to more easily access and use the TensorFlow machine learning platform. It’s an open-source deep-learning API and framework written in Python that runs on top of TensorFlow and is now integrated into that platform. Keras previously supported multiple back ends but was tied exclusively to TensorFlow starting with its 2.4.0 release in June 2020. As a high-level API, Keras was designed to drive easy and fast experimentation that requires less coding than other deep learning options. The goal is to accelerate the implementation of machine learning models, in particular, deep learning neural networks through a development process with “high iteration velocity,” as the Keras documentation puts it. The Keras framework includes a sequential interface for creating relatively simple linear stacks of layers with inputs and outputs, as well as a functional API for building more complex graphs of layers or writing deep learning models from scratch.

Xplenty

Xplenty, is data integration, ETL, and an ELT platform that can bring all the data sources together. It is a complete toolkit for building data pipelines. This elastic and scalable cloud platform can integrate, process, and prepare data for analytics on the cloud. It provides solutions for marketing, sales, customer support, and developers. Sales solution has the features to understand your customers, for data enrichment, centralizing metrics & sales tools, and for keeping your CRM organized. Its customer support solution will provide comprehensive insights, help you with better business decisions, customized support solutions, and features of automatic Upsell & Cross-Sell. Xplenty’s marketing solution will help you to build effective, comprehensive campaigns and strategies. Xplenty contains the features of data transparency, easy migrations, and connections to legacy systems.

IBM SPSS

IBM SPSS is a family of software for managing and analyzing complex statistical data. It includes two primary products: SPSS Statistics, a statistical analysis, data visualization, and reporting tool, and SPSS Modeler, a data science and predictive analytics platform with a drag-and-drop UI and machine learning capabilities. SPSS Statistics covers every step of the analytics process, from planning to model deployment, and enables users to clarify relationships between variables, create clusters of data points, identify trends and make predictions, among other capabilities. It can access common structured data types and offers a combination of a menu-driven UI, its command syntax, and the ability to integrate R and Python extensions, plus features for automating procedures and import-export ties to SPSS Modeler. Created by SPSS Inc. in 1968, initially with the name Statistical Package for the Social Sciences, the statistical analysis software was acquired by IBM in 2009, along with the predictive modeling platform, which SPSS had previously bought. While the product family is officially called IBM SPSS, the software is still usually known simply as SPSS.

PyTorch

An open-source framework used to build and train deep learning models based on neural networks, PyTorch, is touted by its proponents for supporting fast and flexible experimentation and a seamless transition to production deployment. The Python library was designed to be easier to use than Torch, a precursor machine learning framework that’s based on the Lua programming language. PyTorch also provides more flexibility and speed than Torch, according to its creators. First released publicly in 2017, PyTorch uses arraylike tensors to encode model inputs, outputs, and parameters. Its tensors are similar to the multidimensional arrays supported by NumPy, another Python library for scientific computing, but PyTorch adds built-in support for running models on GPUs. NumPy arrays can be converted into tensors for processing in PyTorch, and vice versa.

Knime

KNIME, for data scientists, will help them in blending tools and data types. It is an open-source platform. It will allow them to use the tools of their choice and expand them with additional capabilities. It is very useful for the repetitive and time-consuming aspects. Experiments and expands to Apache Spark and Big data. It can work with many data sources and different types of platforms.

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These Data Science Profiles can boost your career

With the exponential rise in data generation, most organizations are turning towards data science professionals to analyze and interpret the data, helping in predicting future scenarios. Data scientists are responsible for processing the data and using algorithms to collect, store and optimize it. According to the U.S. The Bureau of Labor Statistics reports the demand for data science skills will result in a 27.9% rise in employment opportunities by 2026. Here are the 10 high-paying data science profiles to choose from in 2021.

1. Data Scientists

Data Scientists has been named the number one among the data science careers opportunities in the U.S. by Glassdoor. It is a more technical position with roles to design the methods to store, gather and analyze large structured and unstructured data to help companies improve their strategic decisions.  The average salary of a data scientist is nearly $139,840.

2. Machine Learning Engineers

Machine learning engineers are responsible for creating algorithms needed for analyzing data. These engineers use many big data tools and programming frameworks to modify the raw data being captured by the pipelines. The role requires skills such as knowledge of software engineering, data science, deep learning, and artificial intelligence. Machine learning engineers are top among the data science profiles of 2021. The average salary of a machine learning engineer is about $114,826.

3. Machine Learning Scientist

The main role of a machine learning scientist is to research new data approaches and algorithms that are used in adaptive systems including unsupervised, supervised, and deep learning techniques. Machine learning scientists are also referred to as research scientists or research engineers which is one of the data science profiles to choose for 2021.  The average salary that a machine learning scientist draws is nearly $113,757.

4. Applications Architect

An application architect builds various applications for companies that almost every organization needs to run user interfaces efficiently.  They are also responsible for tracking the functions of various applications. This is one of the data science profiles which can earn you up to $113,757.

5. Enterprise Architect

An enterprise architect is responsible for aligning an organization’s strategy with the new technologies needed to carry out the objectives. This is one of the data science profiles that demands to have a complete understanding of the business and the technologies to design the systems architecture required to meet the needs of the organization. The average salary of an enterprise architect is nearly $110,663.

6. Data Architect

Data architects are the main masterminds behind designing the database framework of the company. They are responsible for designing, creating, and launching a company’s overall database architecture depending on the business requirements of the organization. This among the data science profiles requires also maintaining the data ecosystem of the company. The average salary of a data architect is up to $108,278.

7. Infrastructure Architect

Generally, the infrastructure architect checks for various databases, applications, and software for effective functioning purposes. They are responsible for making sure that the company has all the essential tools for analyzing big data. Infrastructure architect also makes to the list of data science profiles of 2021 with an average salary of $107,309.

8. Data Engineers

Data engineers maintain and create database architectures and frameworks. They are responsible for understanding what the business wants to achieve from the datasets and then developing algorithms according to that.  Data engineers also create dashboards, reports, and other data visualizations for different companies and stakeholders. The average salary of a data engineer is about $81,514 making it one of the data sciences profiles of 2021.

9. Business Intelligence Developer

Business Intelligence developers or analysts create efficient business models and strategic plans for the businesses. They define key performance indicators and data warehouses strategies. They also use advanced software tools to identify and mine business intelligence enabling the organization to make better-informed decisions. This is one of the data science profiles to choose with an average salary of about $81,514.

10. Data Analyst

As every industry relies on data, the role of data analysts comes into play to convert large data sets into a format that is suitable to analyze the data. They also have a crucial part in the industry of data science making it one of the data sciences profiles of 2021 to choose. They also ensure that all programs and systems are effectively running at an optimal level. The average salary of a data analyst is nearly $62,453.

Take away

Since data scientists are needed in almost all fields ranging from government security to dating apps, these industries run on huge amounts of data. Data science careers are in huge demand and this is not going to slow down any soon so choosing from these top 10 data science profiles can boost your career to a great extent.

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Data Science Coding Schools to help to launch a career in Data Science

In recent years, the scope for data science has been gaining momentum with large amounts of data being produced across the globe. This also increases the opportunities for data scientists and enthusiasts who can process and analyze data for new insights. The Bureau of Labour Statistics of the U.S estimates a 15% increase in the demand for trained data scientists over the next decade due to meet the data requirements. This means completing a data science course could benefit you to break into this highly in-demand field which can boost your career to a great extent. Data science courses can not only help you in getting a job but also ensures you stay in the field for a longer time. Here are the top 10 data science coding schools to help you launch a career in data science. Let’s quickly jump into the article to know more about them.

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1. Propulsion Academy

Learning mode: Online and Offline (Munish, Zurich)

Propulsion Academy provides opportunities to learn about the new technologies, tools, and techniques that are necessary to master all areas of data science from scratch. This academy offers data science courses such as Data Science Bootcamp full-time, Data Science Bootcamp part-time dealing with subjects like Back-End Development, Blockchain, Cryptography, CSS, R, Python, Machine Learning, Coding, and natural language processing. Students can apply this knowledge and skills in the real world with its industrial-based projects. The academy also offers career assistance in terms of jobs and careers.

2. NYC Data Science Academy

Learning mode: Online and Offline (NYC)

The NYC Data science academy promotes three courses: online Bootcamp, a Remote-Live Bootcamp, and a professional development course. The Online Bootcamp is of both full-time and part-time, remote Bootcamp is a full-time course and professional development is also a full-time course. The subjects of the academy include Data Science, Data Visualization, Git, Hadoop, Linux, Coding, and Python.

3. Data Science Retreat

Learning mode: Offline (Berlin)

Data science retreat offers full-time data science immersive courses. The subjects include Big Data, Data Science, Machine Learning, Python, Tensorflow, and many more. Once the courses are completed, graduates will have projects that are presented to prospective employers. Career coaching services include resume assistance, practice interviews, and LinkedIn updates.

4. Cambridge Spark Ltd

Learning mode: Offline (Cambridge, London)

Cambridge Spark Ltd offers many applied data science coding courses. The subjects include Data Science, Hadoop, Machine Learning, Python, Coding, etc. It has 14-months blended courses in Data Science and AI. Targeted at offices and currently working professionals, the program simulates real-world projects with Cambridge Spark’s K.A.T.E. (Knowledge assessment teaching engine) platform.

5. Hackwagon Academy

Learning mode: Offline (Singapore)

Hackwagon Academy offers many data science courses with unique subjects such as CSS, Cyber Security, Data Science, Data Visualization, HTML, Python, R, etc. There are three intensive courses provided by the academy: Data Science 101, Data Science 102, and Data Science 103. In Data Science 101, students learn about programming concepts, in 102, students learn about machine learning and python coding. In Data Science 103 they learn about end-to-end Analytics.

6. Galvanize

Learning mode: Online and Offline (Austin, Denver, Los Angeles, NYC, San Francisco)

Galvanize offers full-time courses, part-time courses, and online prep data science courses. The full-time courses run for 13 weeks. It also offers online programming courses. The different types of courses are Data Science Immersive, Data science Prep, Data Science Analytics Online immersive. The subjects include Big Data, CSS, Data Analytics, Data Mining, Data Science, Coding, Python, etc.

7. Ubiqum Code Academy

Learning mode: Online and Offline (Barcelona, Geneva, Madrid)

Ubiqum, Code Academy offers many immersive data science courses. They have campuses in Amsterdam, Barcelona, Berlin, Lisbon, and Madrid. The courses contain subjects such as AJAX, Algorithms, AWS, Big Data, Python coding, and many more. Students have both full-time and part-time courses and can learn data mining in both R language and Python.

8. Xccelerate

Learning mode: Offline (Hong Kong)

Xccelerate, offers many data science courses such as data science, machine learning, python crash courses both full time and part time. The subjects include AJAX, artificial intelligence, blockchain, and crypto, CSS Coding, Python, and many more.

9. BrainStation

Learning mode: Online and Offline (Boston, Chicago, London, Miami, NYC)

BrainStation, provides both full-time online and offline courses on the campuses. The courses the academy offers are Data Analytics Certification course, Data Science Diploma, and Coding. The subjects include A/B Testing, Agile, Algorithms, API, iOS Development, Python Programming, and many more. Students will be assisted in the career too after completing their courses such as helping them in adding LinkedIn portfolios and resumes, etc.

10. The Dev Masters

Learning mode: Offline (Irvine, Lacey, Los Angeles, San Diego, Santa Monica)

Dev Masters provides numerous data science courses such as Mastering Applied Data Science, Data Science for Professionals, Mastering Applied Data Science with Deep Learning, Coding, and many more. The subjects include Artificial Intelligence, Big Data, Data Science, Machine Learning, and Python coding. The academy is intensive along with part-time and full-time courses.

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Let’s Check on The Top 10 Highest Paying Jobs in Technology for 2021.

With the evolution of modern technology and wholesome digital transformation, there is enormous availability of the highest-paid jobs in technology. In the past career options were limited, there were only a few jobs in specific industries like medicine or IT. But now there are ample opportunities where one person can get certified even when they are still working.

Want to give your career a good start and to make sure about your skillset?

Here is the list of the top 10 highest-paid tech jobs for you.

1. Data Scientist

For any organization today, data is the most valuable asset. Therefore, the key responsibility of a data scientist stands to analyse and identify all data that are collected and then interpret the complex variables and forms of those data. For becoming a data scientist, one needs to have enough knowledge about machine learning, data visualization, deep learning, mathematics, computer science.

Job role for data scientists-

• Constructing data models
• Coding languages like Python and other analytical tools
• Understanding and working with machine learning algorithms
• Detecting business problems and providing solutions
• Data scientists can earn as much as $150,000 per annum

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2. Big Data Architect

Users generate over 2.5 quintillion bytes of data daily, but this data in its raw form is impractical and useless. Therefore, over 98% of organizations are investing in Big Data to identify and analyze these data and interpret them. Here stands the need for Big Data architects. Job role for Big Data architect- planning, designing, and managing large-scale deployments of Big Data applications from beginning to end. Skills required for this role are programming skills, data visualization skills, communication skills.

The average salary for a Big Data architect is $140,000 per annum.

3. IoT (Internet of Things) Solutions Architect

One of the fastest-growing technology today is the Internet of Things (IoT). It caters to swift and flawless data sharing by using AI and machine learning. The IoT solutions architect connects the gap between technical and non-technical roles. An IoT solutions architect is responsible for leading and participating in all activities related to architecture and design. An IoT architect should have skills such as hardware design and architecture, strong programming skills, knowledge of Machine Learning.

IoT Architects can earn an average of $130,000 per annum.

4. Software Architect

The main job of a software architect is optimizing the development process by making choices related to design and creating technical standards of coding, tools, and platforms. They are hired by the organization for testing and developing software for them. The job role for software architects is understanding customer demands and perform hands-on work to develop software prototypes. Mandatory skills to become a software architect are understanding software architecture, programming skills, strong analytical skills, and data modeling.

The demand for software architects is very high, with organizations paying well over $114,000 per annum.

5. Blockchain Engineer

This technology constitutes links of chains of information and data that keep getting added to a particular network. A blockchain engineer with the help of blockchain technology develops and implements architecture and solutions. Thorough understanding of Ripple, R3, Ethereum, Bitcoin, etc; understanding of consensus methodologies and the security protocol stacks, crypto libraries, and functions; strong programming skills are required for a blockchain engineer.

As a blockchain engineer, one can earn an average of $150,000 a year.

6. Artificial Intelligence (AI) Architect

The task of an AI architect is to define, identify, design, and build solutions that cater to growth and productivity for organizations through automation. To become an AI architect, skills such as deep knowledge of mathematics and statistics, Solid programming skills with knowledge of Python, R, and Torch, working knowledge of TensorFlow and similar technologies, understanding of technologies related to AI like Machine Learning, neural networks, and Deep Learning are required.

An average salary of an AI architect is $110,000 per annum.

7. Cloud Architect

Cloud computing is a mainstream technology that organizations and individuals across the world use on a daily basis. This resulted in openings for many new jobs as a cloud architect. The task of a cloud architect is to help an organization to develop cloud architecture, cloud strategy, coordinating its execution and application. To become a cloud architect, one needs to have a deep understanding of cloud application architecture, knowledge of Amazon Web Services (AWS), Microsoft Azure, or Google cloud platform, excellent communication skills.

A cloud architect can earn $107,000 per annum on an average in this field.

8. Full-Stack Developer

The task of a full-stack developer is to control and develop both the front-end and back-end of an organization. A full-stack developer must have knowledge about technologies like MongoDB and Node.js, coding and scripting, basics of database technology, fundamentals of web development and should know how to design and develop an API.

The average income of a full-stack developer is $106,000 per annum.

9. DevOps Engineer

The job of a DevOps engineer is processing tools and methodologies that are required to stabilize the needs through the life cycle of the software development process. A DevOps engineer designs and maintains a deployment structure, applies cloud services to allow mechanization in Python and Ruby. To become a DevOps engineer, a person must know coding and scripting, deployment and network operations, to use tools like Git and Jenkins, Using Linux or Unix system administration.

Their average annual salary ranges from $95,000 to $140,000.

10. Product Manager

A product manager is responsible to help the organization in identifying parameters for a product that is built by the engineering team. A product manager is required to lead the development process for a product from its conception to its launch. Product managers also identify the vision of a product and work thoroughly with other teams like sales, marketing, engineering, and IT. A product manager must have a thorough understanding of the concept of PLM, should have the knowledge to manage product tools PivotalTracker, JIRA, or Asana, must have analytical skills and strong time management skills to meet the deadline.

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Google wants Artificial Intelligence everywhere. Here are the top Google products are driven by AI

For the past few years, Google has been dominating the field of artificial intelligence. Google’s search engine has revolutionized the internet. From large-scale organizations to kids, Google’s search engine has provided every one of us with easier access to information. The company claims that its advancements in technology and enhanced customer service would not have been possible had it not invested in disruptive technologies like artificial intelligence, machine learning, deep learning, and others.

This article provides a list of the top 10 products manufactured by Google which are powered by artificial intelligence.

Google Search Engine

Artificial intelligence plays a major role in the systematic operation of Google’s search engine. Algorithms make up a crucial part of the search engine. The breakthroughs in deep learning technology have immensely helped the company in improving these algorithms and provide better search results for the users. Without AI, Google would not have been able to make these improvements to filter search patterns and avoid spam.

Google Assistant

Google Assistant is a voice assistant integrated into smartphones and tablets that are powered by Google. This voice assistant can search for anything, from music playlists, restaurants, best beaches, or hotels to multiple contacts on the phone and even call them in the hands-free mode. Google Assistant is also capable of finding locations using Google Maps.

Google Maps

Google Maps uses AI to track the driver’s route and estimate where they are headed and guides them through with no additional commands. It is a handy navigation system that is available on Android, iOS, and the web. Some features offered by this application are satellite imagery, 360-degree maps, indoor maps, and live traffic predictions in the route ahead. Other important features offered by Maps are nearby restaurants, gas stations, places to eat, ATMs, and other points of interest.

Google Translate

Google Translate allows its users to translate one language to another. It uses voice assistants to intercept the spoken language and translate it into the desired language. Users can also use their keypads to type their queries and get instant translations. Google’s advancements with AI and neural machine translations have improved the quality and reliability of these translations.

Google Drive

Google Drive uses the smart scheduling algorithm to assist the users in scheduling meetings based on the existing information about the users’ meetings and habits. The revamped interface of the application would also which files the users will most likely access, and categorize them according to different folders.

Google Ads

Earlier known as AdWords, Google Ads is a part of Google’s Marketing Suite of tools. This application allows businesses and users to advertise their products online. It offers complete control over the management and placement of the ads based on their target audiences. Google uses machine learning algorithms to analyze the profile of the customers’ behaviors and leverage the insights drawn from the data to reach the right individual.

YouTube

YouTube takes the power of artificial intelligence to manage the content on its platform. It automatically removes objectionable content, like racism or terrorism, from the platform. The company’s top priority is to protect its users from harmful content. The company’s AI researchers use trained ML algorithms to create additional effects on the uploaded videos, making the content even more interesting.

Gmail

Users primarily know that Gmail is an e-mail application, but Google has also integrated several AI and ML algorithms to enhance customer experience. One of the many AI integrated features used by Gmail is the smart reply. It analyzes the entire Gmail and proposes a reply, eliminating the need for typing. It uses AI to protect users from spam.

Google Photos

Photos use AI to suggest images and videos that the users can share with their friends and families. This application uses AI to generate cinematic moments to create a moving image from two different shots. Using AI has enabled bringing moments back to life, tapping into nostalgia, and facilitating happy customer experiences.

Google News

Google News application uses machine learning algorithms to automatically sort through headlines from thousands of possible news sites and provide the users with the best and the most preferred news. AI helps understand the people, places, and objects keep track of the developing news story, and presents it before the customers.

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Analytics Insight presents the top 10 Artificial Intelligence in education start-ups in India.

The sudden outbreak of the coronavirus pandemic has created havoc in the education sector worldwide. Students are uncertain about when the educational institutes will again commence their classes, especially in India. Almost all the educational institutes are closed since March 2020 due to the emergence of the corona waves and the increase in infected cases. But one of the most important sectors in India is the education sector. The future of India depends on smart and intelligent youth. Thus, there is a tremendous growth of educational start-ups that are focused on integrating Artificial Intelligence into education. It is essential for students to continue the learning process at home efficiently and effectively. Artificial Intelligence models are helping them to learn new things in multiple interactive ways at home. The edtech start-ups are initiating to help the rural students as well who lack proper facilities to achieve milestones. Let’s look at the top 10 Artificial Intelligence in education start-ups in India.

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Top 10 Artificial Intelligence in education start-ups in India

Eupheus

Eupheus, is focused on bringing 21^st-century learning solutions from the global market to the Indian market for the youth of India. It provides an opportunity to prepare for the life ahead with the implementation of Artificial Intelligence in its approach. The tech-driven tools are integrated with the curriculum from Pre-K to 12^th standard to provide valuable experience to students. It reinforces successful access to education throughout their school careers and professional life. In 2020, Eupheus raised US$4.3 million in Series A funding. It also provides curriculum e-books, materials, assessments, projects as well as tools made of Artificial Intelligence algorithms. There are multiple subjects like English, Mathematics, EVS, and many more. These are affiliated with state and central educational boards.

Practically

Practically, is one of the popular Artificial Intelligence in education startups, based in Hyderabad, that brings the learning experience in a practical mode. There is a self-learning app to score an A grade in classrooms with a better understanding of the concepts.  The edtech start-up believes in enabling a practical experience for students from class six to twelve while having engaging and interacting classes. There are more than 3.5 lakhs students enrolled in the start-up as well as over 18,000 teachers with a good profile. This start-up is trusted by more than 200 schools in India for its immersive video content. It provides an edge-of-the-seat experience with Augmented Reality and Virtual Reality education at any place at any time for the students to continue learning.

Mindler

Mindler, is focused on providing counseling sessions to students who are concerned with their career decisions. It is well-known that the beginning of the career is most essential to enhance the standard of living. The decisions must be taken with utmost calculations and research. The edtech start-up utilizes Artificial Intelligence and machine learning with strategic human intervention to kickstart the careers of students. It has an aim to minimize human bias in the career decision-making process by leveraging cutting-edge technology and research. It provides career counseling from class 8 to graduates.

Counting Well

Counting Well, is a popular edtech start-up, based in Bengaluru, providing master maths from home in 20 minutes a day to interested students. It provides multiple fun learning mathematics classes from class 6 to 8 with a step-by-step personalized learning path. The students can receive personalized AI-based maths workout plans for a better understanding with minimal screen time. The Artificial Intelligence algorithms are designed to enhance the learning experience because the algorithms help to detect the appropriate skill level.

Jungroo Learning

Jungroo Learning has the vision to cater to the needs of each student who has the desire to learn more. It is a B2B SAAS-based start-up to help educators understand the learner’s journey with the help of an Artificial Intelligence-based personalized adaptive learning platform. The reinforcement learning in AI helps to power the personalization engine as well as with a faster diagnosis of master’s level, monitoring of learning state, and many more. It has also been awarded the Millennium Alliance Grant under Education. The authorized share capital of Jungroo Learning is Rs. 1 million.

Real Learning

Real Learning, is focused on overcoming stressful evaluation cycles with the implementation of Artificial Intelligence for teachers across India. Artificial Intelligence has enabled digitized checking of handwritten answer sheets and generates promising insights for teachers as well as students. The evaluation insight provides concept level analysis, cognitive ability assessment as well as personalized performance reports. RealLearning holds the record of evaluating more than one million answers while saving over 3000 man-hours. It tries to disrupt assessments through digitization, automation as well as analytics. The sophisticated image recognition and AI techniques are used to automate evaluation for reducing book-keeping as well as operational hassles.

Embibe

Embibe, is yet another Artificial Intelligence in education startup focusing on free learning content and Artificial Intelligence-based customized recommendations. This motto helps multiple students to achieve the best score by improving attempt strategies and time management. It provides classes from class 8 to 10 with national-level examinations like JEE Main, NEET, AIIMS, JEE Advanced, Bank, Railway, BITSAT, and so on. The research is focused on mentor intelligence, content intelligence as well as student intelligence with the implementation of Artificial Intelligence in education.

StemROBO Technologies

StemROBO Technologies is focused on providing integrated end-to-end offerings to educators across the country by leveraging Artificial Intelligence in education. It acts as an enabler for students to learn and innovate in Robotics, IoT, and many more disruptive technologies. It has over 5 lakh students and teachers at the K-12 level through unique custom-designed patented DIY kits, innovative pedagogy as well as world-class content and methodologies. The curriculum includes students receiving real-life exposure to AI concepts.

Avishkaar

Avishkaar, is focused on creating an army of young innovators in Robotics and next-generation cutting-edge technologies. It provides a comprehensive platform to teach the students about modern skills such as coding, Robotics, Artificial Intelligence, IoT, and many more. Around 1 lakh students use the kits and courses for a better understanding and more than 1500 schools across India are registered with Avishkaar. It has interactive courses for students from 5 years old to 15 years old. It has also set up Tinkering Labs in over 700 schools with its own equipment and solutions. There are monthly engagement activities, six subjects, training supports, events, and competitions.

Robokart

Robokart, has initiated to take supplementary training in multiple educational institutes to teach students about Robotics, Augmented Reality, Artificial Intelligence, and so on for practical experience. The program is adopted by more than 1000 educational institutes in India with over 1 lakh students. It provides four courses for students from grades 1 to 8 with three options for coding. It helps the kids to understand design games, logical thinking, app creation, website making, coding languages as well as problem-solving techniques.

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Analytics Insight Presents the Top 10 Big Data Statistics for You to Know in 2021.

The future is bright for companies that use Big Data and analytics in this cut-throat competitive market. People are generating more than 2.5 Qn bytes of real-time data due to globalization and digital transformation in the tech-driven era. IoT is also providing data through multiple smart devices, social media accounts, and search engines. The scope of Big Data is increasing at an increasing rate that leads to more job opportunities in the field of Data Science and other disruptive technology fields. Ample Big Data software tools are available to beginners as well as professionals for effective data management to generate interactive reports for meaningful in-depth business insights. Thus, reputed companies and start-ups have started adopting Big Data by investing millions of dollars. Let’s look at the top 10 Big Data statistics to predict the nearby future of this data-driven world.

Top 10 Big Data Statistics You Must Know in 2021

According to Statista, the market survey report showed that the total amount of data being consumed globally was forecasted to increase rapidly to 64.2 zettabytes in 2020 and 79 zettabytes in 2021 while it is projected to grow to over 180 zettabytes up to 2025. It also reported that the installed base of storage capacity will increase at a compound annual growth rate of 19.2% from 2020 to 2025.

The Big Data and business analytics revenue report from Statista showed the forecast of the Big Data market that it will grow to US$274.3 billion by 2022 with a five-year CAGR of 13.2%. The global cloud data center IP traffic will reach almost 19.5 zettabytes in 2021.

BARC, reported that organizations are reaping the benefits of Big Data— 69% chance of better strategic decisions, 54% chance of enhanced operational process control, 52% for a better understanding of consumers as well as 47% for effective cost reduction. The organizations that are reaping the benefits of Big Data reported an average 8% increase in revenues while there is a 10% reduction in costs.

Statista, forecasted that the Big Data market segment will grow up to US$103 billion by 2027 with a share of 45% from the software segment. The market is expected to receive annual revenue of US$274 billion in the next year, 2022.

Forbes, predicted that more than 150 zettabytes or 150 trillion gigabytes of real-time data will need analysis by 2025. Multiple companies dealing with structured data need different things than the companies using unstructured data. Forbes found that over 95% of companies require some help to manage the multiple sets of unstructured data while 40% of companies claimed that they need to deal with Big Data more frequently.

StrategyMRC, predicted that the Hadoop and Big Data Market will experience substantial growth from US$17.1 billion in 2017 to US$99.31 billion in 2022 with a 28.5% CAGR. The Big Data market is expected to jump US$30 billion in value in 2021 and 2022.

It is predicted by Statista, that the global Big Data revenue will experience a major shift in using Big Data in services, hardware, and software. In 2021, there is 24% in services, 16% in hardware, and 24% in software while there will be 33% in services, 24% in hardware as well as a whopping 46% in software use in 2027.

According to Wikibon, the Big Data and analytics, and application database solutions are expected to grow from US$6.4 billion in 2017 to US$12 billion by 2027 with a 6% CAGR, within a span of ten years. The demand for open-source platforms in the Big Data ecosystem such as Hadoop, Kafka, Spark, and TensorFlow can decline due to its direct address to Artificial Intelligence, machine learning, deep learning, or Data Science. But the hybrid deployment of data analytics platforms such as Hadoop, NoSQL, in-memory, streaming, and many more databases will experience a growth in market share for data lake and data fabric solutions.

Sigma, had a market survey to show how many business leaders are keen to adopt Big Data and analytics in their business. The result showed that 39% were not sure about the data-driven culture in organizations, 46% admitted that the lack of domain expertise creates a challenge for delivering relevant data models.

According to ReedSmith, the outbreak of the coronavirus pandemic has increased the rate of Big Data breaches and cyberattacks like scams, phishing, and ransomware to above 400%. The pandemic has forced people to use smart devices more than ever for online transactions and other purposes at home. Thus, there is a data explosion in the digital world that has created ample opportunities for malicious hackers.

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