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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.

Continue reading “Why Data Science Matters More Than Data Scientists?”

Discover the Best Industries to Have a Career in Data Science


Data fires up everything, nowadays. And data science is gaining exceptional traction in the job world, as data analytics, machine learning, big data, and data mining are fetching relevance in the mainstream tech world. By 2025, it is being expected that data science industry will reach $16 billion in value – this is why landing a job in data science domain is the next big thing!

The skills you will imbibe as a data scientist would be incredible, powerful and extremely valuable. You can easily a bag a dream job in corporate moguls, like Coca-Cola, Uber, Ford Motors and IBM, as well as play a significant role in any pro-social or philanthropic endeavors to make this world a better place to live in.

Check out these extremely interesting fields you could start your career in data science:


No wonder, science and medicine are intricately related to each other. As the technology pushes boundaries, more and more companies are recommitting themselves towards a better public health by nabbing biotechnology. Being a data scientist, you would help in unraveling newer ways of studying large amounts of data – including machine learning, semantic and interactive technologies. Eventually, they would influence treatments, drugs-usage, testing procedures and much more.



Power industry functions on data – and tons of it. Whether it’s about extracting mineral wealth from the earth’s crust or transporting crude oil or planning better storage facilities, the demand for data scientists is on the rise. Just as expanding oil fields ask for humongous amounts of data study, installing and refining cleaner energy production facilities relies on data about the natural environment and ways of modern construction. Data scientists are often given a ring to enhance safety standards and help companies recommit themselves towards better safety and environmental regulations.


Recently, transportation is undergoing a robust change. For example, Tesla paved a new road of development and turned countless heads by unveiling a long-haul truck that could drive on its own. Though it’s not the first time, they are prone to lead the change.

Beyond self-driving vehicle technology, the transportation industry is looking for more efficient ways to preserve and transport energy. These advancements in technology works wonders when combined with better battery technology development – in simple terms, every individual field in transportation industry is believed to benefit from a motley team of data scientists.



The internet is not only about tubes, but all about data. The future of the internet is here, with ever-increasing networks of satellites and user devices establishing communication through blockchain. Though they are yet to be used on large-scale, they have started making news. In situations like this, it would be difficult not to highlight the importance of data science and data architecture as they are becoming major influencers in the internet world. Whenever there is a dire need to make the public aware of a new product, we rely on user data – hence the role of data scientists is the key to a better future.

Today, data science is an interesting field to explore, and it is going to play an integral role as the stride in technology and globalization keeps expanding its base. If you have a keen eye for numbers, charts, patterns and analytics, this niche is perfectly suitable for you.

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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.

Continue reading “Data Journalism: What is it and how it works”

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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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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Data Analytics for the Big Screen

Can the film industry leverage more on data analytics?

Film making as an industry is as dependent on good marketing as it is on good content.

Data Analytics for the Big Screen

And it is here that data analytics comes to the picture, for not only does it govern marketing strategies of a Studio but in future it might govern the creative half as well.

For a conventional Hollywood blockbuster, an average of $70 Million are spent within 10-12 weeks and data analytics might direct us as to how much cash needs to be spent and where. Nowadays companies such as IBM are experimenting with Deep Sentiment Analysis, which tries to gauge the market sentiment by listening to the constant stream of content being posted by the users in a given area. The data comes from all sorts of sources, both structured and unstructured, which then needs to be cleaned before gaining any actionable insights from it. Nowadays, companies are working towards developing Market Optimisation Models where they can use historical data to create models, which are then fed current data in order to guide marketing budget allocation decisions. Another way studios are using data analytics is to predict market reaction in USA and Europe by analysing moviegoer’s reaction to the initial run of the movie (usually in smaller markets of Asia). They then proceed to rebrand/improve its offering to make it more ‘commercial’ for a given region.

But does this seemingly endless data and ever improving predictive model point towards a future, where Big Data might tell writers what to write, directors how to direct and actors how to act? If the answer is in affirmative, then are we diluting cinema as an artistic medium? Studios, such as Netflix have now extracted about 70,000 unique characteristics from its movie collection, and now they are analysing how the presence/absence of a characteristic has an impact on the movie revenue/rating/viewing. It then uses these findings to develop and fine-tune the shows it will produce in future. This increasingly ‘scientific’ manner of developing movies is taking over at other studios as well, along with experts fearing that this practice might lead to the industry losing its experimental and creative edge.

With proved benefits, including increased revenue and minimal risk, it is imperative for studios to invest into Data Analytics. It has become imperative to design their marketing strategy using this mine of user data to make their offerings economic, popular, efficient and successful.

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What Makes Artificial Intelligence So Incredibly Powerful?

What Makes Artificial Intelligence So Incredibly Powerful?

Do you also feel that Artificial Intelligence (AI) is getting eerily powerful day-by-day? That is because the structures of Artificial Intelligence exploit the very fundamental laws of physics and of the universe as per latest research.

These new findings help to answer a long-awaited mystery about a category of AI that employs an interesting strategy called deep learning. These are programs based on deep neural networks hence, the name deep learning. The way this works is that they have multi-layered algorithms in which the lower-level calculations feed into the higher level ones within the hierarchy. These deep neural networks often perform surprisingly well when it comes to solving problems which are highly complex, like beating the world’s best player of a strategic board game called Go or categorising cat photos, however no one truly knows why… Continue reading “What Makes Artificial Intelligence So Incredibly Powerful?”

Are you taking care of your digital self?

Whether you like the idea or not, we all have a digital self, a facade that we put on to engage and participate in the technological world! As per psychoanalysts and physicians, a theory proposed by them says that there is a ‘true self’ that is the instinctive core of our personality, it must be realized and nurtured. And there is also a ‘false self’ that is built to protect this true self. From what you ask? From the dangers of insults and vulnerabilities!

Dexlab blog for 12th Oct

Our true selves are usually complex and fragile but it ultimately remains to be our essence. In trying to share that self with the world, we send out our decoy selves to take on the day-to-day vulnerabilities, challenges, and anxieties that come forth.

Continue reading “Are you taking care of your digital self?”

Sherlock Holmes Has Been Doing Data Visualization Before Big Data

Investigative minded people will definitely relate to this story from almost every child’s formative years. The day they get their hands on a magnifying glass, kids would feign being the most famous detective of all times – Sherlock Holmes with a cap they would focus the magnifying glass on an object and try and derive meaning by studying the details closely. This would be their first lesson in data visualization. Later as we learnt about Mr. Holmes through books of Sir Arthur Conan Doyle many of us may have imagined pursuing a career as a full-fledged detective. In his book A Study in Scarlet is the most vivid description of the inclination Mr. Holmes has for the sciences.

Sherlock Holmes Has Been Doing Data Visualization Before Big Data

Now that we come to think of it a detective has probably evolved in this technologically driven planet into a modern-day data analyst or an experimental scientist. The job of a data analyst or scientist revolves around gathering a bunch of disorganized data, and then we use this to build a case through deduction and logic and then you reach a conclusion after analysis. Continue reading “Sherlock Holmes Has Been Doing Data Visualization Before Big Data”

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