Is data science really a rising career?

A huge amount of data is generated every day by companies. This means every company is now sitting with a pile of data and does not really know what to do with that data. So, to organize this volume of data and draw meaningful insights from it, they require people who are experts in Data Science.

Organizations (irrespective of their size) are on the lookout of those employees who can fathom as well as synthesize data and then, in turn, communicate those very outcomes in a method that serves advantageous to the organization and assists the management in making decisions.

A data scientist can work in any global location. In addition, along with the technology industry, a data scientist can work in different industries as well as domains, ranging from pharma/healthcare to sales/marketing. They can also work in domains like consulting, financial services, CPG industries, and retail.

Apart from the above-mentioned uses, data science has proven to solve many complex real-time problems. A few examples of the real-world problems which have found peace in the modern-day data-driven solutions are:

· Advancement in the field of data science has made it easier to detect fraud and abuse in insurance firms now. The credit card fraud detection system works on similar grounds. It protects the security of customers, thus, minimizing losses due to fraud.

· Automated piloting: The concept behind self-driven vehicles runs on data science. Still, in its nascent stage, this will change the functioning of the automobile industry entirely.

· Prediction of short term (local) and long term (global) weather.

The demand for professionals in data is increasing due to the rise in the popularity of data-driven decision making. Where people used Excel to work on data earlier, tools like Hadoop have nowadays secured a place for managing Big Data. With frequent advancement in technology, several other tools find a meaningful use for organizations to make impactful decisions. The usage of a few is listed below:

· Tools like Python and R have witnessed exceptional improvements in their codes. These allow users to solve complex problems with only a few lines of codes.

· Google Analytics is another effective tool for the marketing department.

· Tools like Tableau, Microsoft Power BI and Sisense have found relevance in the business intelligence departments for the purpose of data visualization.

Nowadays, there are plenty of options for data science training that one can opt for. There are many institutes offering data science courses and I would recommend you to enrol in Technogeeks data science program, they offer the best combination of data science courses which are in demand and the course which they offer is provided by working IT professionals.

What should I learn, AI or blockchain?

Artificial Intelligence is the science and engineering of making computer machines able to perform tasks which normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.

The ultimate effort is to make a computer programs that can solve problems and achieve goals in the world, as well as humans.

There is a scope in developing the machines in game playing, speech recognition machine, language detection machine, computer vision, expert systems, robotics and many more.

70% of the companies in the world believe that they need to adopt Artificial Intelligence, in some way or another, to remain competitive by 2020. While most of them have already started using Artificial Intelligence to automate most of their work-force. The use of Artificial Intelligence has helped companies in being more productive in less time.

The biggest challenge corporates across the globe are facing is the lack of Artificial Intelligence professionals. The rate at which the field of Artificial Intelligence is growing the demand for Artificial Intelligence professionals are growing at the same rate or even faster.

Blockchain has given rise to a new field of development called blockchain development. Blockchain development involves developing and optimizing blockchain protocols and crafting the architecture of blockchain systems. A blockchain developer is responsible for creating integrated smart contracts and web applications using blockchain technology. The two types of blockchain developers are:

·        Core Blockchain Developers: They develop and design the architecture and protocols of a blockchain system and design consensus protocols and high-level development decisions related to blockchain.

·        Blockchain Software Developers: They use the protocols and architecture designed by core blockchain developers to build decentralized applications.

As Blockchain technology continues to evolve, so will its professional opportunities. The Blockchain is here with us to stay which means that Blockchain Expertise is to be in high demand for years and years to come. So, whether you are a techie or not, a career in Blockchain is a new and exciting opportunity worth exploring.

The combination of AI and blockchain holds many exciting potential applications. It’s worth remembering, though, that these technologies are still very young. Most of these hybrid applications will take years to develop or may not even develop at all. They’re certainly powerful ideas, but researchers and developers should take great care when working on such projects. The ethical implications of both blockchain and AI are massive, and the stakes are high for the economy, governance, and society as a whole.

If you are looking to learn these technologies then I would recommend you to enrol in Technogeeks training program under a working IT professional.

What kind of degree do I need to work with/on artificial intelligence?

AI is a sound career choice for a while now and as the adoption of AI in various verticals continues to grow, the demand for trained professionals to do the jobs created by this growth is also skyrocketing. Even though many AI pundits have prophesied that this technology will wipe out a massive amount of human jobs, there other pundits too who have said this will offer many unique and viable career opportunities. Therefore, if you are an AI enthusiast then be optimistic and prepare for a great career in AI.

Basic computer technology and math backgrounds form the backbone of most artificial intelligence programs. Entry-level positions require at least a bachelor’s degree while positions entailing supervision, leadership or administrative roles frequently require master’s or doctoral degrees. Typical coursework involves the study of:

·    Various levels of math, including probability, statistics, algebra, calculus, logic and algorithms.

·        Bayesian networking or graphical modelling, including neural nets.

·        Physics, engineering and robotics.

·        Computer science, programming languages and coding.

·        Cognitive science theory.

Candidates can find degree programs that offer specific majors in AI or pursue an AI specialization from within majors such as computer science, health informatics, graphic design, information technology or engineering.

Earning a bachelor’s degree in artificial intelligence can develop strong analytical thinking skills and problem-solving abilities that benefit graduates personally and professionally. Graduates may also develop proficiency in data mining and analysis that will open doors in a diverse range of industries. 

Having a bachelor’s degree in artificial intelligence may open the door to new and exciting career paths. Possible jobs include data scientist, machine learning specialist, or computer engineer. Someone who enjoys teaching could train others to develop and use artificial intelligence or may wish to teach at the secondary level or in higher education. Another alternative is working in sales for companies that market artificial intelligence applications to consumers. This is a growing field, so jobs may become available in a few years that do not exist today.

If you are not able or interested to do a degree in AI then you can also opt for an AI course from any reputed institution that will also help you to get into Artificial Intelligence field and boost your career in AI. So, I would recommend you to enrol in Technogeeks AI program, the course they offer is provided by working IT professionals and after the completion, of course, you will also get certification which is recognisable in the IT industry.

Is Python programming used in artificial intelligence?

 Over the years, choosing the best programming languages between a variety of languages is one of the current debates between programmers. However, your preferred language is undoubtedly the one which you are most comfortable to access and enables you to get the job done efficiently. And one such programming language is Python.

Python comes up with a considerable number of inbuilt libraries that influenced the fact that Python is quickly spreading in the area of AI. The extensive range of frameworks and libraries for Artificial Intelligence and Machine Learning, making it the most preferred language for artificial intelligence development companies. Some of the popular libraries are TensorFlow, Pylearn2, Scikit learns, and the list keeps going.

Secondly, Python is known for its robust, easy implementation and large community. Even programmers with basic knowledge can access this language and do wonders.

AI and ML are seeping into nearly every aspect of our lives, helping us in ways that augment our abilities and make us better at what we do. For instance, voice assistants such as Alexa, Google Assistant, Siri, and Cortana are transforming the way we work, study, and entertain ourselves.

AL and ML solutions are intelligent, i.e. they are different from traditional software applications. The focus here is more on in-depth research, data collection, and predictive analysis. Consequently, the language to be used here should be stable, flexible, and have a variety of tools.

One language that holds a special place among all is Python. Python programming language is among developers’ favourite languages for AI and ML development because of its simplicity, shorter development time, consistent syntax, platform independence and access to a huge set of libraries. (Reasons are discussed in detail in the later section)

When you code with Python programming language, you get a well-structured & tested environment where you can quickly turn your ideas into action. Also, it takes less coding and time to solve complex computational problems.

Moreover, as you get closer to understanding and working with Python app development, you will find it easy to handle massive amounts of data (i.e. data analysis and parsing required for AL and ML work well with Python, and its libraries).

AI and ML have a profound effect in the world we live in, with new solutions coming up day after day. Enterprises have realized that there is no better time than now to invest in these technologies. With its amazing set of libraries, simple & clean coding format, fast prototyping, outstanding flexibility, and huge community support, Python makes the development process of AI and ML-based projects a lot easier, fast, and budget-friendly.

If you are looking to learn python with hand-on training then I would recommend you to take a look in Technogeeks AI with the Python program by working IT professionals.

Are there any good alternatives for Selenium (testing framework)?

Selenium is a framework to conduct software testing. It is used mostly to test web applications. With selenium there is no need to write testing scripts, the software comes with easy navigation tools that can write test cases without the need for any script. Selenium can also provide a domain-specific language to write test cases in any of the popular programming languages such as C#, Java, Scala, Ruby, etc.

It can be very useful even for load testing as it allows users to re-use existing functional tests and run them with virtual concurrent users. Selenium is a very powerful open-source testing tool mainly used for automated functional testing via interacting with browser level objects.

Here are the great alternatives to Selenium available in the market: -

1. Robot Framework:

Robot Framework is an open-source automation system that executes the keyword-driven methodology for acceptance test-driven development (ATDD) and acceptance testing. This tool gives structures to various test automation demands. Its testing technique can be additionally increased out by leveraging special test libraries utilizing Java and Python. A famous external library - Selenium WebDriver - is utilized in the Robot Framework.

Test engineers can use Robot Framework as an automation system for web testing as well as for iOS and Android test automation. Robot Framework tool is also not at all difficult to learn for testers who know about keyword-driven testing.

2. Cucumber:

Cucumber removes any barrier among non-technical and technical project personnel. Fundamentally, that is the crucial element of its mystery sauce. Actually, Cucumber can go about as a Selenium alternative or perform in pairs with Selenium both. Its human-readable test cases encourage cross-team coordination and eliminate isolated software QA.

In the Cucumber framework, functional requirements, acceptance tests, and documentation converge into a solitary automatically refreshed source for testers and partners. Additionally, living documentations implement best practices for necessities management.

3. Test Project:

Test Project is the first free tool to make Selenium testing effortless. With a cloud-based interface built on top of Selenium, you can easily start testing in a matter of no time. Developers will find Test Project's SDK familiar and compatible with existing Selenium code, while non-technical testers will love Test Project's codeless recorder. Think twice before spending precious time building and maintaining a Selenium framework – Test Project is available now completely for free.

Selenium is probably the most popular test automation tool in the market at present. Though, there is no official support available since it is an open-source tool, some of the brightest minds are behind the success of this tool which makes the selenium community strong and growing. Selenium is not just a single tool, it is a complete package. It is a suite of tools consisting of quite a few components, each one of them playing an explicit role in the development of web applications. If you are looking to learn Automation Testing then I would recommend you to enrol in Technogeeks training program by working IT professionals.

Will Artificial Intelligence Take Care of Us or Destroy Us?

From SIRI to self-driving cars, artificial intelligence (AI) is progressing rapidly. While science fiction often portrays AI as robots with human-like characteristics, AI can encompass anything from Google’s search algorithms to IBM’s Watson to autonomous weapons.

Artificial intelligence today is properly known as narrow AI (or weak AI), in that it is designed to perform a narrow task (e.g. only facial recognition or only internet searches or only driving a car). However, the long-term goal of many researchers is to create general AI (AGI or strong AI). While narrow AI may outperform humans at whatever its specific task is, like playing chess or solving equations, AGI would outperform humans at nearly every cognitive task.

Today, the most popular A.I. techniques are machine learning and its younger cousin, deep learning. Unlike computer programs that rigidly follow rules written by humans, both machine learning and deep learning algorithms can look at a dataset, learn from it, and make new predictions. Deep learning in particular can make impressive predictions by discovering data patterns that people might miss.

People say these machines will be benevolent. They will be perfect. They will truly be these beautiful, benevolent, creatures. I don't think that is accurate and I don't think that they are going to be terminating, baby-eating machines either. These machines are going to reflect our species and our evolutionary process. Everything we are will end up in these artificially intelligent machines no matter what we do.

It's such a complex and such new technology in a lot of ways. I think we are afraid of ourselves. That is what it is. We are afraid of ourselves and our own unconscious minds. When we are building something that reflects us, it's the one thing we're all afraid to face. We're afraid to face ourselves. Building machines that mirror our consciousness is a very frightening proposition because we have seen how evil people can be.

What we are really talking about here is an artificially intelligent machine that is going to be able to surpass our intelligence level. At a certain point, you can call it a singularity. There will be a certain point where these machines have super-human intelligence. The best-case scenario is that they will be caretakers and they will be our teachers. They will teach us to be a better species. That is what I am hoping for as a techno-optimist.

If you are interested in learning Artificial Intelligence and want to make a career in this field then I would recommend you to enrol in Technogeeks AI program by working IT professionals.

How Does AI Learn Things?

Unlike humans, AI has computational power that allows it to process copious amounts of data. As AI’s tasks continue to become more advanced, there’s also an increase in computational power necessary-which can become expensive. In order to avoid this need, AI was made to be a specific purpose learner. If AI gains the ability to relate data that is similar, it should be able to more efficiently process the data and understand it with a lower amount of power.

AI makes you look better in selfies, prevents blindness, and can even turn water into tastier beer. Tech giants and governments say we’re living in a golden age of AI. Roll out the self-driving cars!

AI is already used in multiple disciplines and industries. These devices are capable of reading, writing, speaking, seeing, and understanding. They are able to recognize emotions, play games, and even debate. Due to these capabilities, AI is often leveraged as a virtual assistant that can help plan meetings; as a tool to simplify the job hunting and hiring processes; and much more.

However, in order for this technology to be able to advance, it needs to be able to learn.

AI systems rely on artificial neural networks (ANNs), which try to simulate the way the brain works in order to learn.

ANNs can be trained to recognise patterns in information - including speech, text data, or visual images - and are the basis for a large number of the developments in AI over recent years.

Conventional AI uses input to 'teach' an algorithm about a particular subject by feeding it massive amounts of information.  

Practical applications include Google's language translation services, Facebook's facial recognition software and Snapchat's image altering live filters.

The process of inputting this data can be extremely time consuming and is limited to one type of knowledge.

A new breed of ANNs called Adversarial Neural Networks pits the wits of two AI bots against each other, which allows them to learn from each other.

This approach is designed to speed up the process of learning, as well as refining the output created by AI systems.

If you want to learn artificial intelligence then you can enrol for one of the best resources i.e. Artificial Intelligence Course Technogeeks. This training is led by trainers who are AI experts. Besides, at the end of the training, they also awarded me an AI course completion certificate.