The pandemic-caused lockdown has given rise to the second wave of technology reforms for organizations and has proved the need for automation. Most companies do major of their operations through digitized means and automation today. Artificial intelligence, digital aids, IT are important for performance. This shift of platform in the business world has resulted in a demand for individuals with strong technical skills. The work personalities that have digital skills in both existing, and upcoming technologies. Professionals who are proficient in the tools of demand are required to operate the digital face of organizations. DevOps is one such strategic concept that is used to improve the use of IT that has an impact on the services provided by an organization.

In traditional practices the SDLC process was too slow, then DevOps came in between and showed how a faster and continuous production and delivery of software provides a good profit to the organization and became today’s demand in the market. According to studies, organizations that have adopted DevOps, have over 60% improvement in their software development cycles. The DevOps market has already proven to grow faster than any other field in the software development sector. That’s why DevOps is a better carrier right now and in upcoming years.

Basically, DevOps helps to improve the overall efficiency of any program. It provides developers with more control over the production of any product. Most tech companies use it because it provides a far better production infrastructure than other methods. This is exactly why a future in DevOps would mean a career that will be challenging, but equally rewarding.

The DevOps market size keeps increasing day by day. As organizations try to transform their digital presence, they are seeing for professionals to take charge of digitization. As in result, more DevOps engineers are being hired.

Individuals trained in DevOps have many career options such as:

1. DevOps leader
2. Automation Architect
3. Code release manager
4. Software developer/tester
5. Security Engineer

DevOps is a growing technology. Therefore, in the upcoming times, it will only rise. As many stages of software development will depend on it, professionals will begin to move towards it. It is best to start your journey as a DevOps professional with a course that teaches you the basics of DevOps. The best way to rise in this industry is to keep learning and adapting to new technologies.

Yes! DevOps is the best career option. In 2021 and the upcoming year. DevOps will be the best choice for IT professionals as it is in demand and so many companies are adopting the DevOps culture. In India, so many companies have adopted DevOps culture and they can deduct the cost and enhance the software quality and software delivery has been improved. Continuous integration and continuous deployment/ delivery help the Company to reduce the cost, improve software quality, and help to deliver software faster in the market.

DevOps is all about collaboration and communication. The best benefit of working in DevOps is the organizational culture to be working in. It promotes better collaboration between the teams and helps you to develop and sharpen your networking skills to advance your future career paths.

DevOps is a recent skill. This means several professionals who are working in this domain will most likely have upskilled themselves. This will increase your value in the IT industry. Your career is defined by how much you are keen to learn and invest in your career. If you don’t upskill, you will soon be kicked out of demand, as IT technologies and methods are being developed every day. 

DevOps helps you apply a comprehensive approach to building applications. It helps you in Security skills as well in your profile. DevOps skills help bridge the gaps in your profile, which helps you make the value of your’s in the IT domain.

As a DevOps Engineer, there are opportunities for you to work in any company in the IT sector. Start-ups, consultancies, multinational companies, etc., are looking to integrate a DevOps model into their business and create new DevOps Engineer roles to strengthen their current tech teams.

As DevOps continues to transform modern business technology, more specialist senior-level positions are expected to be formed in the next five years, with wide-scale adoption of DevOps anticipated across the industry. It means, more than enough opportunity for internal progression is there as businesses invest in a DevOps function.      

As per the report in 2017, 78% of CIOs and CTOs considered applying DevOps into their organization (a 4% increase in 2016). An additional increase is being expected this year as multiple tech businesses realize the benefits of DevOps Engineers that can work over functions. This is the time for a sustainable carrier. The market has expanded up to 40 to 45 percent over the last five years, increasing the DevOps demand.

Learning DevOps will enable you to learn many more tools and technologies. Companies favor skilled and experienced people with multiple skill sets to improve company costs. If you are a DevOps person, you will be a valuable asset to your company. With DevOps, you will know how to utilize different tools and implement solutions faster and efficiently.

DevOps is a standard career choice now. According to research from Forbes magazine, a DevOps specialist with a basic degree in high school also can earn a middle wage of almost $106,734. Nevertheless, your salary will depend on your role, the average pay for the different roles isn’t completely different. For example, a DevOps Release Manager earns approximately $92K, a Site Reliability Engineer $125K, and a DevOps Engineer $115K. Researches have analyzed, in the upcoming years, DevOps will be the major hiring criteria of the IT companies.

A DevOps career permits you to deploy a broad set of skills that are constantly refined due to the new challenges you solve every day. It’s a constantly fascinating career, encompassing a broad variety of work. Being part of the DevOps process from build to release, you are continuously increasing your understanding of the framework, facing new technologies, and obtaining new knowledge to enable the faster deployment of better quality software.

Having involvement in every element of the DevOps ecosystem provides you as well a broad business understanding as you work to maintain and monitor an effective relationship between operations and development.

DevOps is in high demand, and many companies are incorporating the DevOps process to make their development processes even much faster. There is a high demand for DevOps professionals over various IT domains. In India, a DevOps Engineer earns approximately a salary of INR 7 lakhs per year, according to Glassdoor.com. This statistic changes with experience, skill sets, and the company, you work for.

Training place

I would like to tell you about one of the best places to get trained and certification in DevOps, DevSecOps, and SRE 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.

The post 15 Solid Reasons Learning DevOps Is Good For Your Career Advancement. appeared first on Artificial Intelligence.

But, still, there are a lot of high-paying certification programs that professionals can opt for to compete in the competitive world.

If you are planning for a next move in USA, then completing these IT certification programs will boost your skills and abilities, and it will surely help you to stand out in the job market of united states.

With the changing business landscape, the demand for skilled individuals has increased immensely and organizations are focusing more on candidates who have completed certificate courses in these specific fields.  The demand for below-mentioned skills is sky-high, and the supply of professionals is meager.

Master in DevOps Engineering (MDE):- DevOps positions are consistently ranked among the highest paying salaries in the IT industry. DevOps is here to stay. And with that, the need to obtain a DevOps certification is no longer trendy, it’s quickly becoming a non-negotiable. Having DevOps certification makes you ready to work in a team of cross-functional members, including QA, developers, operation engineers, and business analysts.

DevSecOps Certified Professional (DSOCP):- Security in IT’s is a significant issue in today’s digital era, and Cyber attacks are on the rise and the Cyber threats won’t go away overnight. With this harsh reality, it’s inconceivable that any organization today would neglect the security aspect of the DevOps methodology. With this in mind, all businesses are driven digitally and IT management have moved to prioritize security and compliance at all levels. As more organizations see the benefit of end-to-end security implementation, DevOps will either fade away or get absorbed into DevSecOps.

SRE Certified Professional (SRECP):- SRE certification proves that the professional have understanding of set principles and practices designed to help organizations reliably and economically scale services. A certified professional can make an organization’s infrastructures far more stable, predictable, and scalable – all essential elements of software engineering, development, and operations.

Docker & Kubernetes Certification:- The container management tool Docker and Kubernetes are used in DevOps process to manage software parts as isolated, self-sufficient containers that can be deployed and run in any environment. In today’s market, professionals with Docker and Kubernetes skills are highly sought.

Master in Microservices:- Designing your product or application architecture can be tough as much a business decision as a technological one. Microservices is a particular way of developing software, where applications are structured as a collection of autonomous services OR we can say its a way where a large complex application are broken down into individual small-apps that are responsible for one specific product function. This skill allows large companies to gain agility and new tech capabilities to meet the growing customer demands.

Master in Big Data:- Big data professionals helps organizations to work with their data efficiently and use that data to identify new opportunities. Different techniques and algorithms can be applied to predict from data. Multiple business strategies can be applied for future opportunities and success of the organization and that leads to smarter business strategies, more efficient operations, and higher profits. With huge opportunities and investment in the Big Data technologies, certified professionals carrying the skills of big data are in huge demand.

Master in Artificial Intelligence:- This is one of the best certifications you can own if you want to lead the AI-driven technological revolution. It is not just about replacing the human component of the industry. It’s also about making it easier to make decisions based on observable patterns, use logic and reasoning to form conclusions, and build pathways to boost efficiency and production. It is not an easy discipline, but this is the reason why salaries in the AI industry are much higher than average.

Master in Machine Learning:- Machine Learning is one of the fast-emerging technology with high demand in the industry. Whether it be medicine, cybersecurity, automobiles, etc. all these fields are exploring the capabilities of machine learning. It’s obvious that learning more about Machine Learning and becoming a Certified Machine Learning Professional is a great idea and may even be a very wise career move! Naturally, you will be a hot asset for potential employers if you possess domain knowledge and skills in this field.

Master in Data Science OR Analytics:- Data Science is the latest tech trend that has taken the industry by storm. Companies and organizations, irrespective of their trade, are adopting Data Science tools, technologies, and solutions to promote innovation, increase productivity, boost sales, and maximize customer satisfaction. Once you receive the certification, you can apply for promising roles like Data Science, Data Analyst, and many more DATA-driven roles.

Dynatrace

Quantum Computing

AIOps

DataOps

OpenShift/Tanzu/Rancher/Linkerd/Envoy/Traefik/istio/consul

These certifications program will require a few months of hard work and an investment of time and money but once you’ll successfully complete the course you will be ready to achieve your goals no matter what they are.

Wrapping Up

Hope you would found our list of In-Demand IT Certifications For A Great Career in USA useful. Whether you’re a new working professional or an experienced professional, you won’t have trouble following these courses. At DevOpsSchool.com All of these certification courses are delivered by best-in-class trainers and mentors who will guide you every step of the way. Before selecting any certifications program, one only needs to be clear about their goal, know which path to take, and preserve in order to make the best of that opportunity.

Talk to our certification advisor if you need more information and guidance. (contact@devopsschool.com or +91 700 483 5930 (Call/WhatsApp))

The post The 13 In-Demand IT Certifications For A Great Career in USA appeared first on Artificial Intelligence.

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.

• BOOST YOUR CAREER: 10 BEST DATA SCIENCE BOOTCAMP FOR 2021
• TOP 6 ONLINE DATA SCIENCE COURSES TO WATCH OUT FOR IN 2021
• FOR AMATEURS: BEST DATA SCIENCE COURSES FOR BEGINNERS IN 2021

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.

The post TOP 10 DATA SCIENCE CODING SCHOOLS FOR BOOSTING YOUR CAREER appeared first on Artificial Intelligence.

Artificial intelligence is considered to be one of the most disruptive and successful technologies that exists today. Its potential to continuously evolve and enable innovations is now being leveraged by a spectrum of industries across the globe.

Keeping in mind the significance of AI and its accelerated adoption, there has been a growing demand for AI talents in the companies these days. Observing the increasing demand of AI skills, both employees and the students are on a lookout to pursue Artificial Intelligence as a course and thus add stars to their portfolio.

Owing to this, many institutes are offering some of the best artificial intelligence courses and to make your choice a little simpler, here are five institutes wherein indian students can look for courses on Artificial Intelligence. If you are aspiring to carve a career path out of AI, you may definitely want to consider these courses:

Henry Harvin: The Artificial Intelligence course offered by ed-tech company – Henry Harvin takes you through various modules of AI, thereby accelerating one’s career. The course explains in detail the meaning, purpose, scope, applications and impacts of Artificial Intelligence. It allows you to comprehend AI & ML theories and understand the ML workflow along with the effective execution of strategies.

This 32-hour training session acquaints the pursuant with the best skills and knowledge transferred by professionals and helps you bag your dream job with an AI background.

Intellipaat: The PG Certification course in Artificial Intelligence and Machine Learning offered by Intellipaat is designed to upskill and make you an expert in the popular IT domain. Top faculty from MNIT and industry experts will assist you in mastering the skills required to excel in Artificial Intelligence, deep learning, neural networking, and other concepts in this domain. This course was created in collaboration with IBM and Microsoft in order to provide relevant industry experience.

In order to provide relevant industry experience, this course was developed in collaboration with IBM and Microsoft and spans over a period of 12 months.

Great Learning: This comprehensive course on ‘Artificial Intelligence’ covers the basics of neural network-based models. The course offers a conceptual understanding of learning mechanisms such as Need and history of neural networks, gradient, forward propagation, loss functions and its implementation using python libraries. This 12-month course intends to empower the learners with everything they need to succeed in their careers including the skills much needed for the future oriented professionals.

Edureka: Edureka in collaboration with one of the best NITs in India – NIT Warangal has introduced a diploma in Machine Learning and Artificial Intelligence. These Machine Learning and AI Certification Courses will nurture and transform you into a highly skilled professional thus helping you land a high-paying AI-ML job. This course gives you an opportunity to get hands-on exposure to industry-level assignments and projects with top-notch theories and insights.

Spread over a period of 450+ hours of intensive learning, the course is thoughtfully designed by NITW faculty.

Upgrad: Executive PG Programme in Machine Learning and AI, which is categorically designed for working professionals, is a 450+ hours of learning session that imparts in-demand skills along with the opportunity to work on industry projects. Best-in-class content by leading faculty and industry leaders in the form of videos, cases and projects, assignments and live sessions forms the USP of the course. TNS

The post Enhance your career prospects with Artificial Intelligence courses appeared first on Artificial Intelligence.

Analytics Insight explains the difference between big data engineers vs Artificial intelligence engineers.

‘A domain for the nerds,’ this is what technology was called in the late 1900s. However, a lot of things changed in the 21st century. In the digital world, we welcome hundreds of new Artificial intelligence-powered tools and solutions every day. Owing to the drastic surge in the implementation of artificial intelligence, the technology market has opened its door to more jobs. On the other hand, big data is also bringing many organizational changes to companies. Big data was previously seen as useless content occupying most of the memory in data centers. Fortunately, when technology evolved and became advanced, businesses realized the importance of big data and used it to get data-driven decisions. Following the upsurge in big data and artificial intelligence, two profiles namely big data engineer and artificial intelligence engineer took center stage. According to LinkedIn’s 2020 Emerging Jobs report, artificial intelligence engineers and data-related jobs continue to make a strong showing as the top emerging job roles for 2020 with 74% annual growth in the past four years. Big Data Engineering vs Artificial Intelligence Engineering is two data job roles that are often used interchangeably due to their overlapping skillset but are actually different. In this article, Analytics Insight explains the difference between big data engineers vs AI engineers and helps you choose the right career.

• 10 MUST-HAVE SKILLS FOR DATA ENGINEERING JOBS
• RECRUITMENT: TOP 10 BEST WORKPLACES TO GROW YOUR BIG DATA CAREER
• 5 HABITS OF A SUCCESSFUL AI ENGINEER. DO YOU HAVE THESE?

Definition 

Big data engineer: Big data engineering is a branch of data science that deals with the practical applications of data analysis and collection. A big data engineer is in charge of the design and development of data pipelines. They intensely work to collect data from various sources and give it for further processing to analysts and data scientists. Even though the profile is not directly connected to business teams and business decision-making, it centers on developing systems for better flow and access to information.

Artificial intelligence engineer: An artificial intelligence engineer is someone who works with algorithms, neural networks, and other tools to advance the field of artificial intelligence. They deal with artificial intelligence problems and solve them. Artificial intelligence engineers develop techniques and use them in commerce, science, and other fields. They must be able to extract data efficiently from a variety of sources, design algorithms, build and test machine learning models, then deploy those models to create AI-powered applications capable of performing complex tasks.

Roles and responsibilities

Big data engineer: A big data engineer has to design, develop, construct, install, test, and maintain the complete data management and processing system. Their key role is to seek the raw data and make it usable for other professionals. Without a big data engineer, the company can’t collect data from various sources. Not just collection, they also engage in managing the collection of data and handles its storage, and process it for further use. Some of the other routine responsibilities of a big data engineer are as follows,

• Build highly scalable, robust, and fault-tolerant systems to manage high volumes of data.
• To introduce new big data management tools and technologies to stay ahead in the race.
• Explore various choices of data acquisitions and try out new ways to use existing data.
• Create a complete solution by integrating a variety of programming languages and tools together.
• Employ disaster recovery techniques in case of mishaps.

Besides the basic responsibilities, big data engineers are expected to be well-versed in a set of technological aspects. They should have in-depth knowledge of big data technology and communicate the ideas within and out of the team. In order to carry out the task, they should be experts in the following context.

• Basic knowledge about Java, data structuring, and big data.
• Familiarity with NoSQL solutions, Cassandra, HIVE, CouchDB, and HBase.
• Experience in analytics, OLAP technologies, and more.

Artificial intelligence engineer: Besides creating techniques, artificial intelligence engineers are assigned other organizational responsibilities as well. In order to integrate their technique across the enterprise, artificial intelligence engineers must be able to overcome the unique challenges that result from combining the logic of traditional business applications with the learned logic of machine learning models. Some of the other responsibilities are as follows,

• Build artificial intelligence and machine learning models, then convert the machine learning models into application program interfaces (APIs) so that other applications can use them.
• Help stakeholders understand the output yielding.
• Set up and manage AI product infrastructure and the automation of the infrastructure used by an organization’s data science team.
• Conduct statistical analysis and interpret the results to help organizations drive data decisions.

So, what should you choose as your career option?

According to the World Economic Forum, artificial intelligence was anticipated to create over 58 million jobs by the end of 2020. As we are already in the middle of 2021, artificial intelligence engineering and big data engineering are seeing a sweeping demand rise in the job market. But while choosing a career between these two, you should validate your interest and preferences. If you are someone who is solely interested in data and big data management, it is safe to say that you are destined to work as a big data engineer. If you like coordinating with other teams and want to work out of the clustered data, then artificial intelligence engineering will better suit you.

The post BIG DATA ENGINEER VS AI ENGINEER: WHICH CAREER IS BETTER? appeared first on Artificial Intelligence.

To build a successful career in the field of artificial intelligence, the following skills are required.

Artificial intelligence has become the most popular sector in the tech industry at present. It is also a very popular choice of career for aspiring students. The following are the essential requirements that the aspirants must hone to make a successful career in artificial intelligence.

Educational Requirements

To build a career in artificial intelligence, students must acquire a bachelor’s degree in computer science, information technology, mathematics, statistics, finance, or economics. They should also be proficient in analytical skills, problem-solving skills, creative thinking, effective communication, and industry knowledge. A Master’s degree in advanced computer science education with a specialization in artificial intelligence or a Master’s degree in artificial intelligence will allow the students to weave their AI career. A working professional from the IT industry can build their skills in practical as well as technical knowledge regarding AI.

Technical Skills

Students aspiring for an AI career should learn programming languages like R, Python, Java, and C++. They should have a knack for coding. This will help them understand concepts like data structures and classes. Learning more than one programming language will always prove to be an added advantage since organizations these days are focusing on individuals with multiple skill-sets. Statistics, Linear Algebra, and Calculus would help them understand how algorithms and machine learning would function. A solid understanding of probability and models like Naive Bayes, Hidden Markov models, and Gaussian Mixture models are required. Familiarity with vectors, matrices, matrix multiplication, and understanding of integrals and derivatives are also advisable to be acquired for an aspiring AI engineer. They should also know more about neural network architectures, which have a vital role to play in performing acts of speech recognition, image classification, and translation.

Non-Technical Skills

Building an AI career does not only require technical skills but an AI engineer must also have the ability to explain matters in a simple way to people of the industry. While discussing the analytics with company people, using technical languages will not be helpful as everyone is not familiar with them. So, the AI engineer will be required to explain critical matters to the stakeholders and industrialists. This is why non-technical skills are very important.  In-depth knowledge regarding the industry scenario will also prove beneficial for AI professionals working in this field since this will help them understand the real-world scenario and the main issues of the industry.

Critical Thinking

After acquiring technical and non-technical skills, AI aspirants must develop a bridge between those skills. After creating structured smart data, AI engineers should come up with insightful conclusions from those findings. With the emergence of AI, every sector is now looking to implement artificial intelligence making the demand for professionals a lot higher.

An amalgamation of all these skills would make the most successful career in the field of AI. Posts like big data engineers, program developers, and data analysts have high salaries and come with a lot of responsibility. The most important matter is that without a knack for coding and programming language, building a career in this field is almost impossible.

The post SKILLS REQUIRED FOR A CAREER IN ARTIFICIAL INTELLIGENCE IN 2021? appeared first on Artificial Intelligence.

You may have heard that the field of data science is comparatively new. You may have also heard that there is a high demand for professionals and the trend is likely to continue for the next decade or so. All these are definitely tempting and but are not enough to make an informed decision. As with any other degree, you cannot randomly become a data scientist without knowing the full story. You must judge whether this is the right career path for you and know what all comes after you have graduated.

To ease your process of selection and decision making, this post compiles a set of essential questions that you should get answers to before starting your career in Data Science. These answers will clear the picture for you and help you to take the first step right while joining the top data science courses after 12^th.

What do you do as a data scientist?

A common misconception that goes around is that the work of a data scientist is only about coding. The statement, in fact, is only partially true as the role of a data scientist is yet to be properly defined. Companies hire data scientists under different roles but the core job remains the same: deal with and analyse a massive set of data that will provide value to the company’s business goals.

By analysing data, you will have to solve matters such as how to increase the current productivity, improve product quality and customer satisfaction, and reduce the manufacturing time and so on. To summarise, you will have to increase the business’ revenue using data.

How much coding should you learn?

While data science may not only be about coding, it is definitely an integral part. Coding comes in during the mathematical processes of dealing with large quantities of data that you simply cannot solve manually.

For instance, if a course is teaching you Python, try to learn this language to an expert level. Even if you face any other language during your job, the core principles of using them remain the same. So, you may not know five different languages but you can still land a top job.

Who hires data scientists and what is the pay like?

Data scientists are required everywhere. You can get into fields such as technology, marketing, financial services, corporate setting, government services, healthcare, gaming and so on. In fact, tech companies hire most of the data scientists (41 per cent) followed by marketing companies (almost 13 per cent). To give you a clearer idea, Google, Facebook, Amazon and Flipkart hire data scientists on a regular basis.

As the demand is high and the number of professionals available is low, the pay in this field is generally high. You can easily expect a six-figure annual income once you graduate and that can easily go up to seven within a matter of two years. Depending on your skills, you can get any amount you want.

What does the future look like?

Data science has already been named the top job in America for the year 2016. Statistics also suggest that during the decade 2014-2024, the field has an expected growth rate of 11-14 per cent. Also, almost 80 per cent of the data scientists out there suggest that there is indeed a shortage of professionals in this current field. All these establishes the fact that data science is, in fact, the next big career option.

But the field is also prone to automation as most of the task is ultimately done by machines. Algorithms can run bulk quantities of data through tools and produce faster results than any human possibly can. However, this does not mean that machines will replace data scientists totally. The courses will teach you to understand the automatic algorithms, better their technology and use your creativity to invent new techniques. If you learn to find optimal solutions for a problem at hand, you will never be searching for jobs.

To conclude

If you are fresh out of the 12^th standard now, there cannot be any proper time to start your career in data science. Both new and old companies are starting to realise the importance of this field and investing without limits on the right professionals. Even universities have branches who recruit scientists to help firms with their data or develop tools to process things better and faster.

Seize the opportunity, select the right institution and kick start your career. You will be dealing with real-world data sets and the scope for growth is limitless. Data science is indeed the next big thing.

The post Data Science: Essential Questions You Need To Answer Before Starting Your Career appeared first on Artificial Intelligence.

Yes, having a technical degree helps, but you can also pursue a rewarding career in data science with non-technical background

The global market revenues from data science activities are set to grow in leaps and bounds in the future. And hence, it is no wonder that the demand for data scientists in various industrial roles will rise in proportion to market growth. But the main question is how to get started for a career in data science?

While there are specialized technical courses that can be pursued if one has a technical background, things may not be the same for someone with a non-technical (non-engineering) background. At the same time, given the gap between existing skills and required skills, it will be sometime before a non-techie finds a perfect fit in the data science market. Nevertheless, interested individuals can still succeed professionally with or without a technical background.

But why work as a Data Scientist?

As data becomes an essential asset to the digital transformation pipeline, companies are seeking talent with data skills that can help derive actionable insight from the data for business growth.

Meanwhile, there is an acute shortage of people with data science skills. According to the Co-founder and CEO of Fractal Analytics, Srikanth Velamakanni, there are two types of talent deficits: Data Scientists – who can perform analytics and analytics consultants – who can understand and use data. He reiterates that the talent supply for these job titles, especially Data Scientist is extremely scarce, and the demand is enormous.

Besides, data science includes several sub-job roles each with lucrative salaries and highly rewarding careers.

A Slice from Daily Work of Data Scientists

A data scientist’s work involves predicting potential trends, exploring disparate and disconnected data sources, and finding better ways to analyze information. By using a combination of programming, statistical skills, and machine learning algorithms, data scientists can excavate through large amounts of structured and unstructured data to identify patterns. A data scientist can explore and examine data from multiple disconnected sources, and carry data mining by using APIs or building ETL pipelines.

Data scientists also perform the cleaning process of the datasets for separating the data which is relevant to a particular problem statement for better accuracy of the results. They are also assigned to determine the most optimal models and algorithms for the problem according to the requirements of the data.

Basically, a career in data science can seem intimidating at first but it is not inaccessible. Thanks to resources like video courses, eBooks, Stack Overflow, GitHub, hackathons, meetups, etc., most of which are free and open, it is not difficult to get started at least. It all depends upon passion, dedicated hard work, and interest.

So What to do if you are from Non-Technical Background?

Start From Scratch!

Though one may not have had the exposure to working with data, one can begin with understanding how data is being leveraged by organizations and its industrial applications. Then one can curate a curriculum to prep oneself with required technical skills.

For instance to learn about programming languages, and other key concepts, one can register for courses. Online platforms like Udacity, KDnuggets, Dataquest, and more, already offer online courses in data science. One must also be acquainted with basic mathematical concepts like linear algebra, calculus, probability and statistics. This is important because while data science tools and tech will continue to change rapidly, the underlying math will not.

One can also enroll in certification programs for data science. Earning a certification can improve one’s skills and boost the chances of being a better data scientist candidate. Potential certifications include certified applications professional, Cloudera certified professional: data scientist, EMC: data science associate and SAS certified predictive modeler using SAS Enterprise Miner 7.

Real-Life Projects Help!

Gaining practice training and experience is the linchpin of securing a data science job in the top reputed companies. For this, one must focus on building a portfolio of projects that focus on solving real-world bottlenecks and inefficiencies.

Obviously, there will be many candidates eying for the same data scientist job position. So, going for more focused project learning is a sure way to stand out in a crowd than the academic route. These projects also highlight one’s ability to transfer theoretical skills into the creation of data models that have an impact on society and industry.

The internet already has vast troves of free datasets that can be used for various kinds of projects like criminal records, census reports, cause of death count, etc. These projects can either be online courses based, individual undertaking or mentor-led. One can also host the project work on GitHub to receive feedback from experts or write content around it on Medium or a personal blog.  Either can help in increasing visibility and boost one’s chances of being noticed by a recruiter.

Apart from projects one must participate in various hackathons and other coding competitions conducted by online sites like Kaggle. Additionally, one must also invest some time in attending data science events like  Strata Conference, KDD and join data science communities like Datatau.

Find a Mentor

Charting a course in data science can seem daunting and overwhelming, especially when starting a new career. However, finding a good mentor can mean a huge difference between trying to find a job, preparing for an interview and working one’s first day as a data scientist. A mentor not only guides on what set of actions one must take in one’s career but also helps in securing the candidate’s future via networking. They also offer insider industry tips after one has fetched a well-paying job in data science. They can act as a bridge where one can channelize ideas to top executives and industry leads and also receive feedback in return.

Therefore, finding good mentors can ensure long-term career advice when needed. And when the time comes, one can return the favor by being a good mentor to other new-comers!

To surmise, if you are planning to start a career in data science, do not be afraid to explore your abilities. Figure out which persona you fit in and which are your gaps and take necessary action to propel oneself ahead in a competitive talent pool. Develop requisite skills, deploy learning in real-life use cases, receive healthy feedback, never hesitate to ask for help and lastly, never stop learning!

The post CAREER 101: HOW TO BECOME A DATA SCIENTIST WITH NON-TECHNICAL BACKGROUND appeared first on Artificial Intelligence.

The world is moving at a brisk pace. Several technologies have become obsolete, while the technologies that looked impossible a few years back are at our fingertips now. Hence, it has become pertinent for young professionals to learn the skills that drive future technologies. Data Science is one such promising field. In recent years, millions of professionals have secured their future by choosing Data Science as a career. You can enter this career by completing a degree or a course in Data Science. Finding these courses has become a cakewalk these days. You can easily find a reliable Data Science course in Hyderabad or other top tech cities to boost your career growth. But before we move ahead, let us look at this field in more detail. 

Who is a Data Scientist?

The world economy is leaning more towards analytics, with companies collecting data for many years. According to LinkedIn, there is an enormous demand for professionals who can mine and interpret data. These are the Data Scientists.

Data scientists are a mix of mathematicians, trend-spotters, and computer scientists. The data scientist’s role is to decode large volumes of data and analyze it further to find trends in the data and gain an insight into what the data suggests. Data scientists work between the business and the IT worlds. They drive markets by analyzing complex datasets to reveal insights that companies can leverage into actions.

Skills Required For Data Scientist

If you want to have a career in data science, you will need hard skills like analysis, machine learning, statistics, Hadoop, etc. Excelling in this role requires excelling at critical thinking, persuasive communication, and having good listening and problem-solving skills. 

Opportunities are plenty in this field. So, once you have the skill-sets and qualifications, the jobs are waiting for you—now and in the future.

Job Roles for Data Science Experts

Most companies are embracing data analysis to skyrocket their growth. Data Scientists are in demand not just in the tech world but also in all other major sectors, including FMCG, logistics, and more. The big-five tech giants: Google, Amazon, Apple, Microsoft, and Facebook, employ half of the world’s total data scientists. 

Here, we discuss the lucrative Data Science careers for the aspirants:

Data Scientists:

A Data Scientist explores and scrutinizes various data patterns to estimate the impact on an organization. A Data Scientist’s role is to explain the importance of data in a simple method so that others can understand it. They require statistical knowledge of different programming languages for solving complex problems. 

Data Analyst:

A Data Analyst’s role is to analyze the data to figure out a market trend. They provide a clear picture of the company’s standing in the market. The company sets the desired goal, and then the Data Analyst provides datasets to achieve the required target.

The Data Analyst’s role changes according to the company’s needs. For example, the marketing department may need their services to understand consumer behavior and responses against various marketing strategies. 

Data Engineer:

Data Engineers work with the organization’s core and are the company’s backbone. They are the builders, designers, and managers of large databases. Their task is to build data pipelines, correct data flow, and ensure the data reaches the relevant departments.

A Data Engineer has to collaborate with other data experts to communicate the results with his colleagues. In a nutshell, a data engineer shares his insights with the company through data visualization and helps the organization grow. 

Business Intelligence Analyst:

A business intelligence analyst analyzes the collected data to maximize the company’s efficiency. They have a more technical role than analytical and require knowledge of popular machines. They are the bridge between business and IT. 

Marketing Analyst:

A marketing analyst assists companies in their marketing division. They analyze and suggest which products to produce and which products to discontinue. They monitor customer satisfaction reports to help the company in improving existing products and services.

The post Data Science as a Career Option appeared first on Artificial Intelligence.

The coronavirus pandemic has transformed the lives of thousands of employed professionals all across the globe, including the ones in the data science industry. This crisis brought upon a new normal of working from home and pushed analytics to the forefront. Analytics professionals had to alter the way they work to keep up with the times. 

Data analytics and data science professionals who have been working in the industry for a while would understand how the COVID transformation will affect the field. However, a junior-level professional who is just starting out his/her career would experience a different scenario than what they expect. The pandemic might be tormenting for new graduates or amateur data scientists because more companies have no interest in recruiting fresh analytics professionals. That is why it is more important than ever to build your resume with a certification course. Here is how the career in data science will be different post COVID:

1. Increased competition

With virtual hiring and remote working in place, the norms of recruitment have changed. Companies are no longer required to hire talents from their area. This will create more competition, especially for amateur data scientists and freshers who have just begun their careers. Graduates will now be competing with not only the professionals from their own city or country, but also with those living thousands of miles away. However, there is an upside to this. This has also increased opportunities for data science professionals who can now apply for a job outside their country and gain a better salary. 

In order to stay relevant amidst this crisis, it is important for young data science professionals to gain appropriate skill sets and upskill themselves continuously. There are several online edtech companies that offer basic as well as advanced data science and AI courses. By enrolling yourself in one of these programs, you can help yourself sustain during these trying times. The landscape is evolving and businesses are relying heavily on advanced technologies. In order to stay ahead of the curve, it is important for data science professionals to continue learning.

2. Isolated learning process

Upskilling has always been an important aspect of the career of a data scientist. However, now that the pandemic has disrupted how businesses work, many want to hire professionals who have advanced skill sets. So, upskilling has become more important than ever to make advancements in the field. According to a LinkedIn report, 64% of professionals like data scientists have increased their focus on learning during the lockdown. 

However, since the companies have mandated working from homes for their employees, the learning and upskilling process has become isolated. Before COVID, companies offered training programs and in-person workshops for young data scientist professionals so that they could learn the skills needed for the business and get accustomed to the new workplace. The lockdown has omitted the process entirely and professionals have to rely on online programs to learn these skills. Moreover, these data scientists will be working from home for a long time that restricts their communication with their teammates, thus impeding their learning process.

On the other hand, online programs have become the only source for the new data scientists to enhance their skills and gain the knowledge they need to work in the field. It is important that you select the right online course that provides you with practical experience, interaction with the teacher, and other scientists. With the right program, you will be able to take on any challenges you might be facing in the workplace.

3. Increased efforts for collaboration

Data science is one of those fields that require immense collaboration between the team members to solve a problem. Through this effective collaboration, the data scientists are able to help the company enhance its business operations, create better products, and make informed decisions. A data scientist cannot work in isolation and collaboration is what will determine the success of the project.

This collaboration is especially important for new data scientists who have just joined the company. But, thanks to the pandemic, data scientists and analytics professionals are working by collaborating online. But these online collaborations are filled with challenges that require in-person training to understand the problems and business better. These challenges reduce the efficiency and productivity of the data scientists and create a large communication gap between the team members and the supervisors. 

In the field of data science, asking the right questions is important to solve business problems. With online collaboration, amateur professionals will face issues asking the right question at the same time leading to ineffective collaboration and hammering their work. That is why data scientists have to make a lot of effort to communicate in times of online collaboration.

4. Increased contract-based hiring

Another transformation occurring in the workplace post COVID will be contract-based hiring. This is applicable to almost every profession in the world including analytics professionals and data scientists. Once the pandemic cedes, companies will employ cost-cutting measures and hire freelancers, contract-based employees, and gig workers. This will allow them to keep the employer’s tenure for a limited time and avail data science capabilities for a specific project. 

Even though recruiting freelancers or contract-based hiring is beneficial for the businesses post COVID, it will bring added challenges of increased competition. Even when you want to be hired full-time, you might only be recruited as a contract-based worker because contract-based workers have cost edition benefits and flexibility in the organization. Companies realize that employees don’t have to be in-office or on the payroll for certain functions. 

Post-COVID, with the perspective of cost-cutting companies, will be looking for people who are generalists, instead of specialists. And that is why young data scientists need to have more than domain-specific knowledge. They have to resell themselves to have an understanding of the overall field of data science. And the best way to do that is through a data science course in Bangalore that you can take from the comfort and safety of your home. 

The post Career in Data Science post COVID appeared first on Artificial Intelligence.