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5 Things to Consider While Using Data for Artificial Intelligence

Data is the most influential strategic asset for companies in a data-powered economy. Data is used to measure the ability of a business to perform notable tasks and operations, and draw significant insights through complex machine learning algorithms.

5 Things to Consider While Using Data for Artificial Intelligence

Gaining access to data is not a problem; but the real issue lies in having the right kind of data that helps companies remain on edge. A large number of them don’t even realize they are supplied with chunks and chunks of bad data, punched with wrong formatting, plenty of duplicates, having missing fields or irrelevant information.

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4 Ways in Which Data Scientists Can Add Value to an Enterprise

4 Ways in Which Data Scientists Can Add Value to an Enterprise

Data is everywhere. There is no shortage of data – even the neophyte entrepreneurs who have just begun their business operations are sitting on mounds and mounds of data – but this often makes us introspect how can we use data to grow bigger, more productive?

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Why Data Science Matters More Than Data Scientists?

More is always better, isn’t it? But does it always holds true, especially when it comes to customer data? Maybe not, because business is all about extracting meaningful insights from data, and if that cannot be acted upon then it is of no good.

Why Data Science Matters More Than Data Scientists?

Recently, Accenture concluded that one of the greatest challenges that marketers face nowadays is to discover the right ways to turn data into productive insights and then into action. For that, you would need analytics professionals who do know how to collect, store and integrate information, while mastering the technology aspect.

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Data Science and Machine Learning: In What State They Are To Be Found?

Keen to have a sweeping view of data science and machine learning as a whole? 

Want to crack who is playing tricks with data and what’s happening in and around the budding field of machine learning across industries?

Looking for ways to know how aspiring, young data scientists are breaking into the IT field to invent something new each day?

Hold your breath, tight. The below report showcases few of our intrinsic findings – which we derived from Kaggle’s industry-wide survey. Also, interactive visualizations are on the offer.

  1. On an average, data scientists fall under the age bar of 30 years old, but as a matter of fact, this age limit is subject to change. For example, the average age of data scientists from India tends to be 9 years younger than the average scientists from Australia.
  2. Python is the most commonly used language programs in India, but data scientists at large are relying on R now.
  3. Most of the data scientists are likely to possess a Master’s degree, however those who bags a salary of more than $150K mostly have a doctoral degree under their hood.

Who’s Using Data?

A lot of ways are there to nab who’s working with data, but in here we will fix our gaze on the demographic statistics and the background of people who are working in data science.

What is your age?

To kick start our discussion, according to the Kaggle survey, the average age of respondents was 30 years old subject to some variation. The respondents from India were on an average 9 years younger than those from Australia.

What is your employment situation?

What kind of job title you bag?

Anyone who uses code for data analysis is termed as a data scientist. But how true is this? In the vast realm of data science, there are a series of job titles that can be pegged. For instance, in Iran and Malaysia, the job title of data scientist is not so popular, they like to call data scientists by the name Scientist or Researcher. So, keep a note of it.

How much is your full-time annual salary?

While “compensation and benefits” ranked a little lower than “opportunities for professional developments”, the best part remains it can still be considered a reasonable compensation.

Check out how much a standard machine learning engineer brings home to in the US

What should be the highest formal education?

So, what’s going on in your mind? Should you get your hands on the next formal degree? Normally, most of the data scientists have obtained a full-time master’s degree, even if they haven’t they are at least data analytics’ certified. But professionals who come under a higher salary slab are more likely to possess a doctoral degree.

What are the most commonly used data science methods at work?

Largely, logistic regression is used in all the work areas except the domain of Military and Security, because in here Neural Networks are being implemented extensively.

Which tool is used at work?

Python was once the most used data analytics tool, but now it is replaced by R.

The original article can be viewed in Kaggle.

Kaggle: A Brief Note

Kaggle is an iconic platform for data scientists, allowing ample scope to connect, understand, discover and explore data. For years, Kaggle has been a diverse platform to drag in hundreds of data scientists and machine learning enthusiasts, and is still in the game.

For excellent data science certification in Gurgaon, look no further than DexLab Analytics. Opt for their intensive data science and machine learning certification and unlock a string of impressive career milestones.


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Embedded Analytics: How it’s Revolutionizing Businesses Today?

Embedded Analytics: How it’s Revolutionizing Businesses Today?


Analytics is the key to modern business growth. But, developing it as a highly interactive analytical interface is challenging enough to exhaust the time and resources both. As a result, many businesses are shifting their focus to Embedded Analytics (EA) for their operations, workflows and decision-making capabilities. This new breed of analytics, known as Embedded Analytics helps businesses leverage the power of data to process them in the most useful manner.

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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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Data Journalism: What is it and how it works

The internet has killed some newspapers’ lunch, but it also presented them something truly remarkable – Data Journalism.

Data Journalism: What is it and how it works

Introducing Data Journalism

Data journalism is an amalgamation of a nosy reporter’s news sniffing capabilities and a statistician’s fondness for data analysis. By scrounging through vast amounts of data sets that are available through extensive connectivity, data journalists are using this data to etch out interesting stories.

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The Tides Of Change Is Here: Accenture’s Bhaskar Ghosh Talks About AI, IoT and Big Data

With the Fourth Industrial Revolution looming ahead, many would think that we are already in a digital economy era. Well, somewhat it holds true even. There are countless new apps and software programmes that help people hail a cab, make reservations in a hotel or mop floors by using robotic technology. Smart machines have become really smart to do a plethora of highly adept jobs, which would have been a little bit difficult on the part of humans to perform.

The Tides Of Change Is Here: Accenture’s Bhaskar Ghosh Talks About AI, IoT and Big Data

“While technology has long been developed to serve specific business needs, we are now in an era where people are central to the design and development of technologies,” stated Bhaskar Ghosh, group chief executive, Accenture Technology Services. In a recent interview with a leading financial magazine, he talked over Accenture’s Technology Vision 2017 and gave snippets about the latest trends and innovations that have become a pre-requisite to achieve success in the more-than-ever digitised economy.

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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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