12 July 2016

5 Essential IT Skills for Data Engineers

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30 High Paying IT Jobs
30 High Paying IT Jobs
Skill-1
Experience working with big data tools such as MapReduce, Pig, Spark, Kafka and NoSQL data stores such as MongoDB, Cassandra, HBase, etc.

Skill-2
Expertise in multi-structured data modeling, reporting on NoSQL & structured database technologies such as HBase and Cassandra, SQL.

Skill-3
Experience with languages such as Python, Perl, Ruby, Java, Scala, R etc.

Skill-4
Strong data & visual presentation skills and ability to explain insights using tools like tableau, D3 charts or other tools.

Skill-5
Basic knowledge and experience of statistical analysis tools such as R.

06 July 2016

Human Intelligence Vs Artificial Intelligence

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Key differences of Human and Artificial Intelligence

Human Intelligence: A leading researcher in human intelligence, suggests ``as a heuristic hypothesis'' that all normal humans have the same intellectual mechanisms and that differences in intelligence are related to ``quantitative biochemical and physiological conditions''.

I see them as speed, short term memory, and the ability to form accurate and retrievable long term memories.

What is artificial intelligence
What is artificial intelligence
Artificial Intelligence: Computer programs have plenty of speed and memory but their abilities correspond to the intellectual mechanisms that program designers understand well enough to put in programs. Some abilities that children normally don't develop till they are teenagers may be in, and some abilities possessed by two year olds are still out.

The matter is further complicated by the fact that the cognitive sciences still have not succeeded in determining exactly what the human abilities are. Very likely the organization of the intellectual mechanisms for AI can usefully be different from that in people.

Whenever people do better than computers on some task or computers use a lot of computation to do as well as people, this demonstrates that the program designers lack understanding of the intellectual mechanisms required to do the task efficiently.

29 June 2016

Should You have CSE degree to become a Programmer

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Data Engineers Mentor
While the debate over the value of a computer science degree rages on, these programming leaders emphasize the importance of a well-rounded education, with plenty of time spent studying subjects beyond the console.

"The biggest challenges in life do not have technical fixes," Eich says, "so it's important to study history, literature, art, and other kinds of human knowledge than anything to do with computers."

Hickey agrees, placing programming into a broader perspective.

"Programming is a very new endeavor in the historical scheme of things," he says. "One shouldn't presume that we understand how best to pursue it."

Instead, Hickey suggests pursuing other educational interests to help understand the kinds of problems programming can solve.

"The best programmers are those that can understand, communicate about, and solve problems in the domains they are in," he says. "Software is just a tool for that."

Johnson agrees that would-be programmers should investigate subjects outside the CS lab and mathematics department to help round out their education because, ultimately, "programming is about people rather than math."

Even when it comes to pursuing CS as a degree, Johnson is "torn."

"Mostly one uses things learned on the job," Johnson says. "There's a real value in a good CS education, but I've seen a lot of great programmers who had different backgrounds."

Van Rossum agrees. "You have people who come with an English degree and they go to a Django Girls workshop, and from then on they are Web developers," he says. "You also have people who go through the traditional four years of college with a major in computer science."

Of course, theory does have its place, Johnson says, even in the real world.

"Yesterday I used some compiler theory that I learned as a CS student, and it helped me get a neat, robust solution to the problem I had," he says. "But that doesn't happen very often."

Eich sees more value in studying mathematics as mathematics, rather than as a pretext for studying computer science theory.

"Programming is not all about mathematics, sometimes hardly at all," he says. "But if you're good at math, study it as math while you are young. Don't worry about programming so much."

Van Rossum goes a bit deeper, recommending "the kind of math that develops logical thinking."

Schlueter has a different theory for going general before becoming a programmer: "A liberal arts education is a great way to spend four years after high school, if you can afford it," he says. "There won't be another time in your life when you can mostly just goof off and party for that long with societal approval."

If you do take him up on his suggestion, Schlueter also offers advice that's deadly practical: "State schools are way cheaper, and student loans are no joke, so be thrifty," he says. "Try to get any scholarship or grants that you can."

Writing, Schlueter argues, is a key facet of being a strong programmer.

"Whether you go to college or not, try to make time as early as possible to read lots of literature and philosophy, both primary and secondary sources, and write as much as you can," he says. "If you're not going to college, then as soon as you can, shell out for a writing tutor who'll give you assignments and then help you polish them. This job happens on the Internet, and the written word is how people communicate there. The more effectively you can write, the better off you'll be."

Related: Computer World Full Article

As for where that exciting work may be for young programmers planning future careers, Eich suggests "space, 3D printing, 3D rendering, bioinformatics, web."

3D printing -- also known as additive manufacturing -- turns digital 3D models into solid objects by building them up in layers. The technology was first invented in the 1980s, and since that time has been used for rapid prototyping (RP). However, in the last few years, 3D printing has additionally started to evolve into a next-generation manufacturing technology that has the potential to allow the local, on-demand production of final products or parts thereof.

17 June 2016

6 Different Skills You Need for Artificial Intelligence Career

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Acquire the knowledge and skills for developing various intelligent information systems through a basic grasp of computer science and information processing technology. Courses : Computer programming, data structure and algorithms, programming for systems design, object-oriented programming, computer systems, mathematical logic, automata and language theory, logical circuit, computer networks, etc.
AI Basic Skills
AI Basic Skills

Develop a novel technique of intelligent information processing in which computers collaborate with human beings, by learning various technologies in intelligent information processing. Courses : Basis of AI, AI programming, AI system design, logic and proofs, inference and learning, knowledge base, natural language processing, pattern understanding, computer vision, computer graphics, etc.

Master the fundamentals of mathematics and natural science. Courses : Linear algebra, analysis, discrete mathematics, probability and statistics, applied analysis, differential equations, classical physics, modern physics, etc.

Mathematical Informatics Section: To explicate the process of intelligent activities based on logical reasoning by human beings and to create novel methods of problem-solving by implementation with computers.
Topics : Automated reasoning by logic, data compression, retrieval and mining, development of various efficient algorithms, fluent motion controls of automobiles and robots, etc.

AI Architecture Section : To establish the techniques for designing hardware and software systems necessary for the development of the next-generation of intelligent information processing systems.
Topics : Parallel distributed environments, next-generation interfaces, new technology for software design, the design of cutting-edge software systems, etc.

Media Informatics Section : To develop essential technologies to make computers human-friendly, such as natural language interfaces and automated visual recognition.
Topics : Intelligent tutoring systems, human-computer interactions, computer graphics, pattern recognition, computer vision, information extraction and classification from natural language, etc

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