It’s time to move beyond Artificial Intelligence frameworks. Recently, a joined effort from the Digital giants Microsoft and Facebook has paved the pathway for developers to move beyond traditional AI frameworks. The Open Neural Network Exchange (ONNX) format announced the other day that Facebook and Microsoft are on a lookout to boost AI interoperability and innovation. This piece of information was published in their own blog posts, and from there it got viral.
In Facebook’s blog post, the Social Media behemoth clearly defined its new effort is “toward an open ecosystem where AI developers can easily move between state-of-the-art tools and choose the combination that is best for them.”
The Blockchain, the brainchild of a single curator or a group of specialists known by the nom de guerre, Satoshi Nakamoto is indisputably a brilliant invention. With the passage of time, it evolved into something greater and more insightful.
Scientists across the globe are looking forward towards formulating new methods to realize ‘quantum internet’, an unhackable internet, which connects particles linked together by the principle of quantum entanglement. In simple terms, quantum internet will entail multiple particles striking information at each other in the form of quantum signals – but specialists are yet to figure out what it actually does beyond that. The term ‘quantum internet’ is quite sketchy at this moment. There’s no real definition of it as of now.
Today, Pelle Braendgaard writes distributed applications, or “DApps,” for Ethereum—a cryptography-based technology that is waiting to make an impact. It’s similar to the green field of 1990’s web, providing similar opportunities as then.
The birth of DApps
If people at all know about Ethereum, it is as Bitcoin’s first cousin that stands for everything experimental and of course Braendgaard, who is widely acclaimed as the old-guard programmer. The price of Ether, the coin underlying Ethereum, has spiked up by over a factor of 20 in the last 6 months. Unfortunately, on the zest to become rich quickly, many of us have overlooked Ethereum’s prominent significance. More than just being a new type of digital currency, Ethereum has developed into a new breed of distributed computer, which no one can control but can see inside out. Through this computer, a new creed of applications is launched -“DApps”.
Designing Big Data architecture is no mean feat; rather it is a very challenging task, considering the variety, volume and velocity of data in today’s world. Coupled with the speed of technological innovations and drawing out competitive strategies, the job profile of a Big Data architect demands him to take the bull by the horns.
Big Data is the latest buzz. It has to be effectively analyzed to formulate brilliant marketing and sales strategies. It’s of immense importance, as it includes humongous amount of information accumulated about customers from numerous sources like email marketing schemes and web analytics.
However, due to the vast magnitude of information available, it may get quite difficult for marketers to analyze and evaluate all the data in an efficient way. Fortunately, plenty of tools are available in the market that can manage mammoth marketing data and here are few of them:
Each year, pronouncements are made. And each year, a particular job field rides high above the tides of fortune. For 2017, Data Scientist jobs seem to be #1 Best Job in India. Several magazines and research associates have put Data Scientist jobs at #1 position. No wonder, data science jobs are the hottest jobs in today’s market, hopefully in future too. So, how do you become a good data scientist? Affordable Data Science Training Course in Gurgaon is now available in India that too quite easily. DexLab Analytics is one such institute that offers state-of-the-art data science training facilities for young aspiring candidates.
Get hold of SAS skills
If you are aware of the top data science skills, you must have known that statistical analysis and data mining calls for SAS specialization. SAS plays an important role in all these disciplines. It has been the pioneer and the most reliable software suit, and for a long time enjoying the monopoly position.
However, since the advent of R and Python, the powerful open source competitors, it is true that the growth curve of SAS has been little but hampered. Nevertheless SAS skills still boast of astounding demand all over the world.
SAS training courses help you understand the nuances of data science. Nowadays, these training’s are not too difficult to find, myriad institutes offer online and classroom training for its students on a regular basis. It is no more too difficult to get a grip on the fundamentals of this subject matter.
The number speaks of positivity
It would be like mine 11th commandment – there is a shortage of data science jobs. It is being predicted that there could be a shortage of 200,000 data scientists by 2020, and this is for real. Indian market is an emerging economy, though data science may not be so famous here as it is in the US, yet I am proud to say that the importance of this field is on the rise.
The survey says – the global demand for data scientists grew by more than 50% in between 2014 and 2015, while the searches have increased by 73%.
The skills you require to possess
By analyzing a whole lot of LinkedIn job postings, we have come to a conclusion that there are 5 high-in demand skills that you need to master in order to ace in data analytics – SQL, Hadoop, Python, Java, and R. Apart from these five, you also need to be quite proficient in Data Visualization and statistics, and try to bring out your creative side to the front.
How much difficult is it to choose a data analytics course?
Make sure, you know what you want, very clearly. Prepare yourself well, before getting into any course. Experience matters, but before that you need encompassing training on the subject matter that can only be offered by a pioneering institute of data science. However, before investing money and your time, check properly if the curriculum satisfies your needs. The material needs to be crisp, to the point and in line with the current industry standards.
To learn more about Machine Learning Using Python and Spark – click here. To learn more about Data Analyst with Advanced excel course – click here. To learn more about Data Analyst with SAS Course – click here. To learn more about Data Analyst with R Course – click here. To learn more about Big Data Course – click here.
‘Big Data’, and then there is ‘Data Science’. These terms are found everywhere, but there is a constant issue lingering with their effectiveness. How effective is data science? Is Big Data an overhyped concept stealing the thunder?
Summing this up, Tim Harford stated in a leading financial magazine –“Big Data has arrived, but big insights have not.” Well, to be precise, Data Science nor Big Data are to be blamed for this, whereas the truth is there exists a lot of data around, but in different places. The aggregation of data is difficult and time-consuming.
Statistically, Data science may be the next-big-thing, but it is yet to become mainstream. Though prognosticators predict 50% of organizations are going to use Data Science in 2017, more practical visionaries put the numbers closer to 15%. Big Data is hard, but it is Data Science that is even harder. Gartner reports, “Only 15% organizations are able to channelize Data Science to production.” – The reason being the gap existing between Data Science expectations and reality.
Big Data is relied upon so extensively that companies have started to expect more than it can actually deliver. Additionally, analytics-generated insights are easier to be replicated – of late, we studied a financial services company where we found a model based on Big Data technology only to learn later that the developers had already developed similar models for several other banks. It means, duplication is to be expected largely.
However, Big Data is the key to Data Science success. For years, the market remained exhilarated about Big Data. Yet, years after big data infused into Hadoop, Spark, etc., Data Science is nowhere near a 50% adoption rate. To get the best out of this revered technology, organizations need vast pools of data and not the latest algorithms. But the biggest reason for Big Data failure is that most of the companies cannot muster in the information they have, properly. They don’t know how to manage it, evaluate it in the exact ways that amplify their understanding, and bring in changes according to newer insights developed. Companies never automatically develop these competencies; they first need to know how to use the data in the correct manner in their mainframe systems, much the way he statisticians’ master arithmetic before they start on with algebra. So, unless and until a company learns to derive out the best from its data and analysis, Data Science has no role to play.
Even if companies manage to get past the above mentioned hurdles, they fail miserably in finding skillful data scientists, who are the right guys for the job in question. Veritable data scientists are rare to find these days. Several universities are found offering Data Science programs for the learners, but instead of focusing on the theoretical approach, Data Science is a more practical discipline. Classroom training is not what you should be looking for. Seek for a premier Data analyst training institute and grab the fundamentals of Data Science. DexLab Analytics is here with its amazing analyst courses in Delhi. Get enrolled today to outshine your peers and leave an imprint in the bigger Big Data community for long.
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Data is now produced at an incredible rate – right from online shopping to browsing through social media platforms to navigating through GPS-enabled smartphones, data is being accessed everywhere. Big Data professionals now fathom the enormous business opportunities by perusing petabytes of data, which was impossible to grasp previously. Organizations are taking the best advantage of this situation and rushing to make the best of these revelations about.