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Quantum Internet Is Now Turning Into a Reality

Quantum Internet Is Now Turning Into a Reality

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.

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Televisory Launches Data Analytics & Operational Benchmarking Platform

Televisory Launches Data Analytics & Operational Benchmarking Platform

Televisory, a start-up based out of India and Singapore, has launched its data analytics and operational benchmarking platform. The platform can measure real-time operational and financial performance of companies. While the firm has chosen to launch its platform from the US, its services are available globally.

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Business Intelligence: Now Every Person Can Use Data to Make Better Decisions

The fascinating world of Business Intelligence is expanding. The role of data scientists is evolving. The mysticism associated with data analytics is breaking off, making a way for non-technical background people to understand and dig deeper into the nuances and metrics of data science.
Business Intelligence: Now Every Person Can Use Data to Make Better Decisions

“Data democratization is about creating an environment where every person who can use data to make better decisions, has access to the data they need when they need it,” says Amir Orad, CEO of BI software company Sisense. Data is not to be limited only in the hands of data scientists, employees throughout the organization should have easy access to data, as and when required.

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Curiosity is Vital: How Machine Inquisitiveness Improves the Ability to Perform Smartly

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What happens when a computer algorithm merges with a form of artificial curiosity – to solve precarious problems?

Meticulous researchers at the University of California, Berkeley framed an “intrinsic curiosity model” to make their learning algorithm function even when there is a lack of strong feedback signal. The pioneering model developed by this team visions the AI software controlling a virtual agent in video games in pursuit of maximising its understanding of its environment and related aspects affecting that environment. Previously, there have been numerous attempts to render AI agents’ curiosity, but this time the trick is simpler and rewarding.

The shortcomings of robust machine learning techniques can be solved with this mighty trick, and it could help us in making machines better at solving obscure real world problems.

Pulkit Agrawal, a PhD student at UC Berkeley, who pulled off the research with colleagues said, “Rewards in the real world are very sparse. Babies do all these random experiments, and you can think of that as a kind of curiosity. They are learning some sort of skills.”

Also read: Data Science – then and now!

Like several potent machine learning techniques rolled out in the past decade, Reinforcement Learning has brought in a phenomenal change in the way machine accomplish their things. It has been an intrinsic part of AlphaGo, a poster child of DeepMind; it helped playing and winning the complex board game GO with incredible skill and wit. As a result, the technique is now implemented to imbue machines with striking skills that might be impossible to code manually.

However, Reinforcement Learning comes with its own limitations. Agrawal pointed that sometimes it demands a huge amount of training in order to grasp a task, and the procedure can become troublesome, especially when the feedback is not immediately available. To simplify, the process doesn’t work for computer games where the advantages of specified behaviours is not just obvious. Hence, we call for curiosity!

Also read: After Chess, Draughts and Backgammon, How Google’s AlphaGo Win at Go

For quite some time now, a lot of research activity is going around on artificial curiosity. Pierre-Yves Oudeyer, a research director at the French Institute for Research in Computer Science and Automation, said, “What is very exciting right now is that these ideas, which were very much viewed as ‘exotic’ by both mainstream AI and neuroscience researchers, are now becoming a major topic in both AI and neuroscience,”. The best thing to watch now is how the UC Berkeley team is going to run it on robots that implement Reinforcement Learning to learn abstract stuffs. In context to above, Agrawal noted robots waste a nifty amount of time in fulfilling erratic gestures, but when properly equipped with innate curiosity, the same robot would quickly explore its environment and establish relationships with nearby objects.


In support of the UC Berkeley team, Brenden Lake, a research scientist at New York University who lives by framing computational models of human cognitive capabilities said the work seemed promising. Developing machines to think like humans is an impressive and important step in the machine-building world. He added, “It’s very impressive that by using only curiosity-driven learning, the agents in a game can now learn to navigate through levels.”

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Data Science – then and now!

Data Science – then and now!

  • Data Science = Statistics + Computer Science
  • emerges as a designation for stores of big data

The following timeline traces the evolution of the term “Data Science”, along with its use, attempts to define it, and related terms:


“The future of Data Analyses “- by John W.Turkey, 1962


  • More emphasis was placed on using data to suggest hypotheses to test
  • Exploratory Data Analysis and Confirmatory Data Analysis works in parallel


“Book on Survey – Contemporary data processing methods “– by Peter Naur, 1974


    • Data is a representation of the facts or ideas in a formalized manner
    • It is capable of being communicated or manipulated by some process
    • The rise of “Datalogy”, the science of data and data processes and its place in education
    • Data Science here defined as – the science of dealing with data, once established and the relation of data being delegated to the other fields and sciences.


“The International Association for Statistical Computing (IASC)”- Section of ISI, 1977


  • The mission is to link traditional statistical methodology, modern computer technology and the knowledge of domain experts in order to convert data into information and knowledge


Gregory Piatetsky-Shapiro, 1989


  • Arrival of Knowledge Discovery in Databases (KDD) workshop
  • It became the annual ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) in 1995


“Database Marketing” – cover story by BusinessWeek, 1994


  • Companies collect mountains of information about you
  • Then crunch it to predict how likely you are to buy a product
  • Implement the knowledge to craft a marketing message precisely calibrated to get you to do so
  • Many companies were too overwhelmed by the sheer quantity of data to do anything useful with the information
  • However, many companies believe they have no choice but to brave the database-marketing frontier


“Members of the International Federation of Classification Societies (IFCS)”, 1996


  • Data science is included in the title of the conference (“Data science, classification, and related methods”)


“From Data Mining to Knowledge Discovery in Databases” by – Usama Fayyad, Gregory Piatetsky-Shapiro and Padhraic Smyth,1996


  • Historically, the notion of finding useful patterns in data has been given a variety of names,
  • Some of the names are data mining, knowledge extraction, information discovery, information harvesting, data archaeology, and data pattern processing
  • KDD [Knowledge Discovery in Databases] refers to the overall process of discovering useful knowledge from data, and
  • Data mining refers to a particular step in this process
  • Data mining is the application of specific algorithms for extracting patterns from data
  • Data preparation, data selection, data cleaning, incorporation of appropriate prior knowledge, and proper interpretation of the results of mining, are essential to ensure that useful knowledge is derived from the data


H. C. Carver Chair in Statistics at the University of Michigan -Professor C. F. Jeff Wu, 1997


  • Asked statistics to be renamed as data science, and statisticians to be renamed data scientists


The journal Data Mining and Knowledge Discovery, 1997


  • “Data mining” designates as – “extracting information from large databases.”


“Mining Data for Nuggets of Knowledge” – Jacob Zahavi quoted – 1997


  • Conventional statistical methods work well with small data sets
  • Today’s databases, however, involves millions of rows and scores of columns of data
  • Scalability is a huge issue in data mining
  • Another technical challenge is developing models that can do a better job analysing data, detecting non-linear relationships and interaction between elements
  • Special data mining tools may have to be developed to address web-site decisions


Also read: The Beginners’ Guide to Data Science Jargon


“Data Science: An Action Plan for Expanding the Technical Areas of the Field of Statistics.” – by William S. Cleveland, 2001


  • Plan to enlarge the major areas of technical work of the field of statistics
  • The benefit to the data analyst has been limited, because the knowledge among computer scientists about how to think of and approach the analysis of data is limited, just as the knowledge of computing environments by statisticians is limited
  • A merger of knowledge bases would produce a powerful force for innovation
  • The statisticians should look to computing for knowledge today just as data science looked to mathematics in the past
  • The departments of data science should contain faculty members who devote their careers to advances in computing with data and who form partnership with computer scientists


“Statistical Modeling: The Two Cultures” (PDF) – by Leo Breiman, 2001


  • Two cultures in the use of statistical modeling to reach conclusions from data
  • One assumes that the data are generated by a given stochastic data model, while the other uses algorithmic models and treats the data mechanism as unknown
  • Algorithmic modeling, both in theory and practice, has developed rapidly in fields outside statistics
  • It can be used both on large complex data sets and as a more accurate and informative alternative to data modeling on smaller data sets.
  • If our goal as a field is to use data to solve problems, then we need to move away from exclusive dependence on data models and adopt a more diverse set of tools


Launch of Journal of Data Science, 2003


  • Data Science means almost everything that has something to do with data: Collecting, analyzing, modeling
  • The most important part is its applications–all sorts of applications


“Competing on Analytics,” a Babson College Working Knowledge Research Center report “- by Thomas H. Davenport, Don Cohen, and Al Jacobson, 2005


  • The emergence of a new form of competition based on the extensive use of analytics, data, and fact-based decision making
  • Beside competing on traditional factors, companies starts to employ statistical and quantitative analysis and predictive modeling as primary elements of competition


The National Science Board publishes “Long-lived Digital Data Collections – 2005


  • Data scientists are – “the information and computer scientists, database and software engineers and programmers, disciplinary experts, curators and expert annotators, librarians, archivists, and others, who are crucial to the successful management of a digital data collection.”
  • In simple terms, they are the people who work where the research is carried out–or, in the case of data centre personnel, in close collaboration with the creators of the data–and may be involved in creative enquiry and analysis, enabling others to work with digital data, and developments in data base technology


Also read: Secrets To Clinch Victory in Global Data Science Competitions


Harnessing the Power of Digital Data for Science and Society, 2009


  • The nation needs to identify and promote the emergence of new disciplines and specialist’s expert in addressing the complex and dynamic challenges of digital preservation, sustained access, reuse and repurposing of data
  • Many disciplines are seeing the emergence of a new type of data science and management expert, accomplished in the computer, information, and data sciences arenas and in another domain science
  • These individuals are key to the current and future success of the scientific enterprise
  • However, these individuals often receive little recognition for their contributions and have limited career paths.


“Google’s Chief Economist, tells the McKinsey Quarterly”- Hal Varian, 2009


  • Quote – “I keep saying the sexy job in the next ten years will be statisticians. People think I’m joking, but who would’ve guessed that computer engineers would’ve been the sexy job of the 1990s?”
  • The ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it—are going to be the most important skills in the coming decades
  • Managers need to be able to access and understand the data themselves.


“The Revolution in Astronomy Education: Data Science for the Masses “- Kirk D. Borne, 2009


  • Understanding the data is crucial for the success of sciences, communities, projects, agencies, businesses, and economies
  • It is true for both specialists (scientists) and non-specialists (everyone else: the public, educators and students, workforce)
  • specialists must learn and apply new data science research techniques
  • Non-specialists require information literacy skills


“Rise of the Data Scientist”- Nathan Yau, 2009


  • As quoted, “the next sexy job in the next 10 years would be statisticians.”
  • By statisticians, he actually meant a general title for someone who is able to extract information from large datasets and then present something of use to non-data experts
  • Ben Fry argues for an entirely new field, which will combine the skills and talents from disjointed areas of expertise… [Computer science; mathematics, statistics, and data mining; graphic design and human-computer interaction].


Also read: How is data science helping NFL players win Super bowl?!


Troy Sadkowsky, 2009


  • Created the data scientists group on LinkedIn, complementing his website, (which later became


”Data, Data Everywhere“- The Economist Special Report – Kenneth Cukier, 2009


  • A new kind of professionals has emerged – the data scientists, who combines the skills of software programmer, statistician and storyteller/artist to extract the nuggets of gold hidden under mountains of data


“What is Data Science?”- Mike Loukides, 2010


  • Data scientists combine entrepreneurship with patience, along with the willingness to build data products incrementally, the ability to explore, and the ability to iterate over a solution
  • They are inherently interdisciplinary
  • They can tackle all aspects of a problem, from initial data collection and data conditioning to drawing conclusions
  • They can think outside the box to come up with new ways to view the problem, or to work with very broadly defined problems: ‘here’s a lot of data, what can you make from it?’


Also read: What Sets Apart Data Science from Big Data and Data Analytics


“A Taxonomy of Data Science” – Hilary Mason and Chris Wiggins – 2010


  • Data scientist, in roughly chronological order: Obtain, Scrub, Explore, Model, and Interpret
  • Data science is clearly a blend of the hackers’ arts
  • Statistics and Machine learning and the expertise in mathematics and the domain of the data for the analysis to be interpretable
  • Requires creative decisions and open-mindedness in a scientific context


“The Data Science Venn Diagram”- Drew Conway, 2010


  • Simply enumerating texts and tutorials does not untangle the knots
  • Data Science Venn Diagram – hacking skills, math and stats knowledge, and substantive expertiseData_Science


“Why the term ‘data science’ is flawed but useful “- Pete Warden, 2011


  • The people tend to work beyond the narrow specialties that dominate the corporate and institutional world, handling everything from finding the data, processing it at scale, visualizing it and writing it up as a story
  • They also seem to start by looking at what the data can tell them, and then pick interesting threads to follow rather than the traditional scientist’s approach of choosing the problem first and then finding data to shed light on it


“Data Science’:  What’s in a name?”- David Smith, 2011


  • Many companies are now hiring ‘data scientists’, and the entire branch of study is run under the name of ‘data science’
  • Yet some have resisted the change from the more traditional terms like ‘statistician’ or ‘quant’ or ‘data analyst’
  • However, unabashedly ‘Data Science’ better describes what we actually do, which is a combination of computer hacking, data analysis, and problem solving


“The Art of Data Science” – Matthew J. Graham, 2011


  • To flourish in the new data-intensive environment of 21st century, we need to evolve new skills
  • We need to understand what rules [data] obey, how it is symbolized and communicated, and what its relationship to physical space and time is.


“Data Science, Moore’s Law, and Moneyball” – Harlan Harris, 2011


  • Data Scientist runs the gamut from data collection and munging, through an application of statistics, machine learning and related techniques for interpretation, communication, and visualization of the results
  • Data Science is defined by its practitioners, as a career path rather than a category of activities
  • People who consider themselves Data Scientists typically have eclectic career paths, that might in some ways seem not to make much sense.Data-Science-Teams


“Building Data Science Teams”- D.J. Patil, 2011


  • Jeff Hammerbacher shared the experiences of building the data and analytics groups at Facebook and LinkedIn
  • He realized that as their organizations grew, they need to figure out what to call the people on their teams
  • ‘Business analyst’ seemed too limiting
  • ‘Data analyst’ was a contender, but they felt that title might limit what people could do. After all, many of the people on their teams had deep engineering expertise
  • ‘Research scientist’ was a reasonable job title used by companies like Sun, HP, Xerox, Yahoo, and IBM
  • However, they felt that most research scientists worked on projects that were futuristic and abstract, and the work was done in labs that were isolated from the product development teams
  • Instead, the focus of the teams was to work on data applications that would have an immediate and massive impact on the business
  • The term that seemed to fit best was data scientist: those who use both data and science to create something new


“Data Scientist: The Sexiest Job of the 21st Century” in the Harvard Business Review – Tom Davenport and D.J. Patil, 2012


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After Chess, Draughts and Backgammon, How Google’s AlphaGo Win at Go

After Chess, Draughts and Backgammon, How Google’s AlphaGo Win at Go

Two decades ago, if someone asked me to write a computer program that played tic-tac-toe, I would have failed horribly. Now being an accomplished computer programmer, I know the desirable tricks to solve tic-tac-toe with the help of “Minimax Algorithm”, and what it takes is just about an hour to jot down the program. No doubt, my coding skills have evolved over the period of time, but also computer science technology has reached unattainable heights.

Computers paved the ways for a startled innovation. When in 1997, IBM introduced a chess-playing computer, known as Deep Blue, which eventually beat world-renowned Grandmaster Garry Kasparov in a six-game match, people remained in awe for years. Following the trend, in 2016, Google’s London-based AI Company, DeepMind launched AlphaGo – and it mastered over the ancient board game Go. Computers have outplayed the best human players in the games of chess, draughts and backgammon, now it’s time for Go.

Also read: Infographic: How Big Data Analytics Can Help To Boost Company Sales?

The technology goes on thriving, beating humans at games. In late May, AlphaGo is all set to take on its human rival Ke Jie, the best player in the world during the Future of Go Summit in Wuzhen, China. Games, which solely relied on human intelligence, wit, intuition, discern is now excelled by the AI, which is powered by improved engineering and computer superiority.

Also read: Top Databases of 2017 to Watch Out For

Don’t you think this is great! Where AI is driving our cars, looking for ways to cure deadly cancer and helping us in everyday work, winning at Go takes AI a step ahead. It not only makes the games more fun and exciting, but endlessly enjoyable.

The strategy explained

In the eastern part of the world, notably in China, Japan and South Korea, Go is extremely popular and many celebrities indulge in it. The game developers showed interest for long in the complexity of this game. However, the rules are simple – the main objective is to secure the maximum territories by placing and capturing black and white stones on a 19×19 grid.

Also read: Shadowing a Data Architect for a Day!

Chess is less complicated than Go; in the latter, the chances of recognising wins and losses is relatively tougher, as stones possess equal values, and ensures understated impacts throughout the board. To play Go, AlphaGo program implemented deep learning in neural networks – a brain-stimulated program. The connections formed here runs in-between layers of simulated neurons, further strengthened by examples and experiences. Firstly, it analysed 30 million positions from expert games, while gaining abstract information about the state of play from the board data, just like other programmes that classify images from pixels. After all this, finally it played against itself over 50 computers to improve its performance, with each iteration and this came to be known as reinforcement learning.

Go-02 (1)

The round of applause

“AlphaGo plays in a human way”, says Fan – DeepMind’s program AlphaGo beat Fan Hui, the European Go champion. He further added, “If no one told me, maybe I would think the player was a little strange, but a very strong player, a real person.” “The program seems to have developed a conservative (rather than aggressive) style”, adds Toby Manning, a veteran Go player and a referee.

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Top Databases of 2017 to Watch Out For

Data processing is the most talked about topic of this year. From the figure below, you can comprehend that NoSQL and SQL databases are the ones most preferred by the respondents. 

Top Databases of 2017to Watch Out For

By putting together the percentage of respondents who found them fetching and who called them ‘extremely engaging’, we can conclude who the runner-up is. Here, NoSQL databases secure the second rank with 74.8%.

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Shadowing a Data Architect for a Day!

Shadowing a Data Architect for a Day!

A data architect is a noteworthy role in the present analytics industry. One can naturally evolve from a data analyst or a database designer to a data architect after gathering sufficient experience in the field. The prominence of this role showcases the emergence of the online websites and other internet avenues which require the integration of data from several unrelated data sources.

These data sources can be anything from:

  • External sources, like market feeds (for e.g. Bloomberg) or other News Agencies (like, Reuters)
  • Or they could be internal sources like exiting systems that collect data, for instance HR operations that gather employee data

Here is a depiction of a day in the life of a successful data architect:

Data analyst certification from a reputable analytics-training institute can help to speed up your process of evolution from being a data analyst to becoming a successful data architect!


Shadowing a Data Architect for a Day! from Infographics


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Harnessing Big Data for Water Management

World Water Day: Save Water with Big Data

Appalling forces are re-establishing the relationship between humans and water.

In the past, communities developed slowly, while the weather remained constant. Water sources never depleted at tumultuous rates as it has today. Water is no longer a dependable resource. That’s why many countries and cities are embracing smart technologies to manage water efficiently and preserve it for the coming generations.

As we observe the United Nations World Water Day on Wednesday, 22nd March, it is apt to assess the development being made in conserving this diminishing resource.


 Today, the Internet of Things (IoT) – a blooming worldwide network of devices and appliances linked to the internet – has materialized as a propitious solution to save water and protect clean drinking water, especially in cities.  

To begin our discussion, Netherlands is on its way to develop a pioneering program to address the relevant problems of increasing sea levels, surging number of droughts and the effect of extreme weather changes on its trains, bus networks and roadways, and the efficiency with which the entire nation tackles situations like this. The ambitious project, Digital Delta draws in local and regional water jurisdictions, top-notch scientists and proliferating businesses to implement Big Data technology for upgrading the systems of its €7 billion water management, while restricting the costs of preserving water by 15%.

Prophecies about Urban Centres

Plummeting freshwater resources: a serious challenge faced by the global population is now at its apex. An overwhelming 89 percent of the world population thrives on enhanced water supply systems, which results in a loss of more than 32 billion cubic meters of fresh water, through physical leakage. Thereby, more than 50 percent of world population will be vulnerable in water-stressed regions by 2025. And by 2040, the figures will further push the energy demand by 56%, making US the second highest energy consumer across the globe.

Saving Water Globally

In the meantime, most of the world cities should re-invent and re-structure their assets to pull together advanced functions encompassing different complex systems and to associate with new powerful allies. Urbanization comes with its own costs. Day by day, these networks are growing more complicated and even more expensive. By delving deeper into the interconnections of systems, the societies will be in a better position to grasp how to run them more efficiently.

Water has never grabbed eyeballs, as it has today. Many countries are not at all prepared to manage such burgeoning complexities of water management. Besides, water management authorities are constantly under pressure to harness their power for flood protection and drinking water standards.

Reality Check: Water demand is set to rise by 30% by 2030. Ever increasing population and swelling urbanization are the reasons behind such calamitous figures.

Smart City Technology – The Key to Urban Sustainability


New Jersey Institute of Technology (NJIT) revealed that by 2025 smart city technologies would multiply to an industry estimating $27.5 billion. Moreover, nearly 88 smart cities will develop by the end of 2025. Smart cities whirl around the concept of using improved, interconnecting technologies to make environment safe, lives easier and urban living cost-effective and more efficient.

Societies are enduring new weather extremes. It is the high time to use big data and analytical science to cure the growing complexities in managing our water systems. Smart technology is the only viable option that can take future generations towards a sustainable future.

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