"Data Science is an interdisciplinary field about processes and systems to extract knowledge or insights from data in various forms, either structured or unstructured, which is a continuation of some of the data analysis fields such as statistics, data mining, and predictive analytics" [1]

The  data science as a hot new field promises to revolutionize industries from business to government, health care to academia

The field of data science is emerging at the intersection of the fields of social science and statistics, information and computer science, and design. The UC Berkeley School of Information is ideally positioned to bring these disciplines together and to provide students with the research and professional skills to succeed in leading edge organizations.[2]

[1] https://en.wikipedia.org/wiki/Data_science

[2] https://datascience.berkeley.edu/about/what-is-data-science/

https://datascience.berkeley.edu/about/what-is-data-science/

"Data Science is an interdisciplinary field about processes and systems to extract knowledge or insights from data in various forms, either structured or unstructured, which is a continuation of some of the data analysis fields such as statistics, data mining, and predictive analytics" [1]

The  data science as a hot new field promises to revolutionize industries from business to government, health care to academia

The field of data science is emerging at the intersection of the fields of social science and statistics, information and computer science, and design. The UC Berkeley School of Information is ideally positioned to bring these disciplines together and to provide students with the research and professional skills to succeed in leading edge organizations.[2]

[1] https://en.wikipedia.org/wiki/Data_science

[2] https://datascience.berkeley.edu/about/what-is-data-science/

https://datascience.berkeley.edu/about/what-is-data-science/

Dear Colleagues,

Good Day,

"Data Science is an interdisciplinary field about processes and systems to extract knowledge or insights from data in various forms, either structured or unstructured, which is a continuation of some of the data analysis fields such as statistics, data mining, and predictive analytics, similar to Knowledge Discovery in Databases (KDD).

...... Data Scientist

Data scientists use their data and analytical ability to find and interpret rich data sources; manage large amounts of data despite hardware, software, and bandwidth constraints; merge data sources; ensure consistency of datasets; create visualizations to aid in understanding data; build mathematical models using the data; and present and communicate the data insights/findings. They are often expected to produce answers in days rather than months, work by exploratory analysis and rapid iteration, and to get/present results with dashboards (displays of current values) rather than papers/reports, as statisticians normally do.

"Data Scientist" has become a popular occupation with Harvard Business Review dubbing it "The Sexiest Job of the 21st Century"  and McKinsey & Company projecting a global excess demand of 1.5 million new data scientists.".....

Please, see the link for more information....

https://en.wikipedia.org/wiki/Data_science

Data science without sustainable, measurable added value, is not data science.

Data science is the science of producing revenue out of data. If it does not produce added value, it is not data science.

~ Laetitia Van Cauwenberge

Dear @Behrouz Ahmadi-Nedushan,

Thank you very much for interesting anwser and att. file.

Regards, Shafagat

Dear @Sofia Cividini,

Thank you very much for your point of view.

Regards, Shafagat

Dear @Hazim Hashim Tahir  and @Fateh Mebarek-Oudina,

Thank you very much for interesting opinion.

Regards, Shafagat

Dear @Sofia has brought THE DATA SCIENCE VENN DIAGRAM! Link is attached. I do bring Data Science Venn Diagram v2.0! Link follows.

Compare those two diagrams. "The center is marked "Unicorn". This a reference to the recent discussions in the press and blogosphere indicating that Data Scientists are as hard to find as unicorns..." Data scientists are hard to find.

http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram

http://www.anlytcs.com/2014/01/data-science-venn-diagram-v20.html

An estimate that is slightly biased but robust, easy to compute, and easy to interpret, is better than one that is unbiased, difficult to compute, or not robust. That's one of the differences between data science and statistics.~ L. V. Cauwenberge

Talented data scientists leverage data that everybody see; visionary data scientists leverage data that nobody see.

Data scientist is an hybrid business/tech role. If you don''t have the business component, you are an handicapped data scientist.

~ L. V. Cauwenberge

Dear @Ljubomir Jacić,

Thank you very much for valuable  answer.

Regards, Shafagat

Dear @Eraldo Banovac,

 Thank you very much for useful link.

Regards, Shafagat

Dear Colleagues,

Good Day,

"A Very Short History Of Data Science, Gil Press , CONTRIBUTOR.

The story of how data scientists became sexy is mostly the story of the coupling of the mature discipline of statistics with a very young one–computer science. The term “Data Science” has emerged only recently to specifically designate a new profession that is expected to make sense of the vast stores of big data. But making sense of data has a long history and has been discussed by scientists, statisticians, librarians, computer scientists and others for years. The following timeline traces the evolution of the term “Data Science” and its use, attempts to define it, and related terms.

(the article start from the year 1962 - the year 2009), see what had happened in 2002& 2003.

April 2002 Launch of Data Science Journal, publishing papers on “the management of data and databases in Science and Technology. The scope of the Journal includes descriptions of data systems, their publication on the internet, applications and legal issues.” The journal is published by the Committee on Data for Science and Technology (CODATA) of the International Council for Science (ICSU).

January 2003 Launch of Journal of Data Science: “By ‘Data Science’ we mean almost everything that has something to do with data: Collecting, analyzing, modeling…… yet the most important part is its applications–all sorts of applications. This journal is devoted to applications of statistical methods at large…. The Journal of Data Science will provide a platform for all data workers to present their views and exchange ideas.”

please, see the original article for detail....

http://www.forbes.com/sites/gilpress/2013/05/28/a-very-short-history-of-data-science/#4fd6226f69fd

I was engaged in the joint programs of data compilation and making empirical formulas for the compiled data in the field of atomic and molecular collision physics, and published some results in the journal, "Atomic Data and Nuclear Data Tables." These jobs are concrete examples of data science, though there are much more things in this category of science.

Dear @Shafagat, this is fine article: Ten Trends in Data Science 2015!

"The following trends are particularly noteworthy...:

• Rise of Data Virtualization
• Hybrid Data Stores Become More Common
• Semantics Becomes Standard
• ...
• Databases Become Working Memory
• Data Analytics Moves Beyond SQL
• Data Visualization and Flexible Reporting Becomes Real..."

https://www.linkedin.com/pulse/ten-trends-data-science-2015-kurt-cagle?trkInfo=VSRPsearchId%3A577811291456140664798%2CVSRPtargetId%3A5954684582975520768%2CVSRPcmpt%3Aprimary&trk=vsrp_influencer_content_res_name

Dear @Harshvardhan Singh,

Thank you very much for opinion.

Regards, Shafagat

Dear @Pierlorenzo Brignoli,

Thank you very much for answer.

Regards, Shafagat

Dear @Tatsuo Tabata,

Thank you very much for opinion.

Regards, Shafagat.

Dear Shafagat.

I believe the answers of Behrouz and Sofia are clear enough so that I can add something else.

Excuse my late answer but these weeks can not devote the time I wanted to RG.

Best regards.

One way to consider data science is as an evolutionary step in interdisciplinary fields like business analysis that incorporate computer science, modeling, statistics, analytics, and mathematics.

At its core, data science involves using automated methods to analyze massive amounts of data and to extract knowledge from them. With such automated methods turning up everywhere from genomics to high-energy physics, data science is helping to create new branches of science, and influencing areas of social science and the humanities. The trend is expected to accelerate in the coming years as data from mobile sensors, sophisticated instruments, the web, and more, grows. In academic research, we will see an increasingly large number of traditional disciplines spawning new sub-disciplines with the adjective "computational" or “quantitative” in front of them. In industry, we will see data science transforming everything from healthcare to media.

http://datascience.nyu.edu/what-is-data-science/#

https://en.wikipedia.org/wiki/Data_science

Dear Ms Mahmudova,

Probability and Stats is basis for "Big data analysis" / Data science .

Well , I stopped when programming (with C) that was logic-centric  got switched to (C++) that was data-centric !!!

"Probability  is common sense reduced to calculation" ... P S Laplace

I guess it is important when 0 is good (for me all the time !!) but when it is not ...

Numbers are important - Market depends on it (flip-side - reality is IT IS TOTALLY VOLATILE  ...)

Funny, it is easy with #s than characters (machine level ) I guess simulation and # crunching is best done using 64-bit processors (up to ridiculous values) .

I hope I did not skew the perspective  ...

I always like to provide simple answers, therefore, I would like to propose the following answer to the question:

With the many emerging processing techniques, the field of data science is born to further develop and assess the ways that data are processed. 

Best regards

Dear Dr. Shafagat,

Thanks for asking my contribution. In our research work in biology, it is necessary to specify the research problem then we suggest a scheme for solving the problem then we use the suitable materials and methods to achieve the suggested scheme and finally harvest the experimental data (results) that should be statistically analysed to conclude the right decision in solving the research problem. So, in my opinion, the data science is the science deals with the scientific analysis of the experimental data to extract the right decision in solving a specific problem.

Best wishes 

Data Science is an interdisciplinary field about processes and systems to extract knowledge or insights from data in various forms, either structured or unstructured

it is a systematic enterprise that builds and organizes knowledge in the form of testable explanations and predictions

the systematic study of the organization, properties, and analysis of data and its role in inference, including our confidence in the inference.

Biomedical and health informatics (BMHI) continues to expand rapidly as a necessary and integral part of our healthcare systems. Data collection and analysis are not only a way for healthcare organizations and public entities to measure outcomes and quality, but are required to demonstrate the meaningful use (MU) of health information technology (HIT) dictated by the U.S. Department of Health and Human Services, Centers for Medicare and Medicaid (McLane & Turley, 2011). BMHI is critical for generating knowledge that is instrumental for improving the quality of healthcare delivery, while increasing efficiency and productivity. Additionally, BMHI can provide customizable tools for sharing information to facilitate the breakdown of silos within and outside of healthcare organizations (Cantor, 2012). The application of biomedical and health informatics has the potential create new pathways from biological discovery to the bedside, increasing patient participation and safety while enhancing the delivery of care (Embi, 2012). Embi goes on to emphasize that new models, methods, tools and innovative approaches need to be developed to address the “ever increasing need for clinical research that will enable the testing and implementation of cost-effective therapies at the exclusion of those that are not” (p. 415).

The contextual complexities of biomedicine create a semantic gap between data and its associated information. Individual human beings are complex systems that respond differently to the same stimuli (drugs, treatments, interventions, etc.), making it difficult to develop a knowledge base that is applicable for in large populations (Martin-Sanchez, & Gray, 2011). In other words, the aim of BMHI is to identify information that can be managed to create knowledge to solve a unique problem for a particular patient or population. There are several areas of healthcare where BMHI can be utilized to facilitate the improvement of patient care including; drug development, population health management, clinical decision support, and the integration of genomics and proteomics, which will ultimately lead to the application of personalized medicine. Martin-Sanchez & Gray (2011) sum it up nicely by stating; “aspects of databases (storage and retrieval), data processing (algorithms), artificial intelligence (generation of meaning), representation and integration (ontologies), visualization techniques and other aspects of usability (human factors) represent the core components of the body of knowledge and abilities of BMHI” (p. en66). 

The current state of BMHI can be described as the “best, dynamic vehicle” for bringing discoveries to clinical providers “in a meaningful way” (Cantor, 2012, p. 2). The exponential growth of medical data will likely facilitate rapid implementation of clinical research findings, the realization of personalized medicine, and ultimately a healthier society. 

References

Cantor, M. N. (2012). Translational informatics: An industry perspective. Journal of the American Medical Informatics Association. doi:10.1136/amiajnl-2011-000588.

Embi, P., (2012). Future direction in clinical research informatics. In R.K. Richesson, & J.E. Andrews (Eds.) Clinical Research Informatics (pp. 409-416). London UK: Springer-Verlag.

Martin-Sanchez, F., & Gray, K. (2011). Education, research and professionalism in health and biomedical informatics: Myths, realities and proposals for the future. European Journal of Biomedical Informatics, 7(2): 64-71.

McLane, S. C., & Turley, J. P. (2011). Informaticians: How they may benefit your healthcare organization. Journal of Nursing Administration, 41(1), 29-35.

Dear Prof Shafagat

There is significant and growing demand for data-savvy professionals in businesses, public agencies, and nonprofits. The supply of professionals who can work effectively with data at scale is limited, and is reflected by rapidly rising salaries for data engineers, data scientists, statisticians, and data analysts.

 There are publications which may interest you in this field.

https://www.researchgate.net/search.Search.html?type=publication&query=Data+science+Krishnan+Umachandran

Data are simply the figures if they do not carry any information , the same data once turned into some kind of information , it becomes data of science Infact,  whole universe is based on such kind of data as apart of science .  

Dear Colleagues,

Good Day,

"3 Ways to Use Data Science to Improve Company Sales

You can never have too much information about your target customer/audience.

Data science is one of the biggest trends in business. For many teams, however, the concept remains a black box. Positioned at the intersection of engineering and statistics, we often thinking of data science as closely tied to IT, marketing, analytics and product. We imagine complex algorithms, messy data sets and endless lines of statistical code.

What we often overlook is the direct link between data science and sales. At the end of the day, predictive models, datasets and trend forecasts are about people and processes — not numbers. Data science programs can help sales leaders run their operations more efficiently, focus efforts on the ‘right’ sales prospects and uncover missed opportunities. Here are three trends that every sales leader should know.

Please, the link for detail....

https://businesscollective.com/3-ways-to-use-data-science-to-improve-company-sales/

It is the use of large data sets to suggest models of the phenomenon. Mainly through the use of machine learning and computer-aided statistical analysis and pattern recognition.The premise is that if you know how to, you can tease out a prediction. Rather than just explain in the conventional statistical analysis. Another key distinction is the use of advance data visualizations.

See data science ....

See ...

A good article from Harvard business review:

"What data scientists do is make discoveries while swimming in data. It’s their preferred method of navigating the world around them. At ease in the digital realm, they are able to bring structure to large quantities of formless data and make analysis possible. 

They identify rich data sources, join them with other, potentially incomplete data sources, and clean the resulting set. In a competitive landscape where challenges keep changing and data never stop flowing, data scientists help decision makers shift from ad hoc analysis to an ongoing conversation with data"

"Much of the current enthusiasm for big data focuses on technologies that make taming it possible, including Hadoop (the most widely used framework for distributed file system processing) and related open-source tools, cloud computing, and data visualization. While those are important breakthroughs, at least as important are the people with the skill set (and the mind-set) to put them to good use. On this front, demand has raced ahead of supply. Indeed, the shortage of data scientists is becoming a serious constraint in some sectors"

https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century/

I think that too many applied scientists have mixed up the terms "correlation" and "causality", I'm afraid ... many seem to work with data is if they are the same!

Have you heard the story about life expectancy and the economic development among a population, as analyzed through the study of cigarette smoking?

Ok: Let's compare, for example, the states of Uganda and the USA. In Uganda, life expectancy is quite a lot shorter than in the USA. Furthermore, in Uganda, a smaller proportion of the population are cigarette smokers. Hence, smoking is good, right? (A nice example of a statistical correlation that is NOT causal.)

The fun thing here is - of course - that if any data set is very large then you WILL find spurious correlations within it, as any statistician will tell you.

What is Data Science?

There is much debate among scholars and practitioners about what data science is, and what it isn’t. Does it deal only with big data? What constitutes big data? Is data science really that new? How is it different from statistics and analytics?

One way to consider data science is as an evolutionary step in interdisciplinary fields like business analysis that incorporate computer science, modeling, statistics, analytics, and mathematics.

At its core, data science involves using automated methods to analyze massive amounts of data and to extract knowledge from them. With such automated methods turning up everywhere from genomics to high-energy physics, data science is helping to create new branches of science, and influencing areas of social science and the humanities. The trend is expected to accelerate in the coming years as data from mobile sensors, sophisticated instruments, the web, and more, grows. In academic research, we will see an increasingly large number of traditional disciplines spawning new sub-disciplines with the adjective "computational" or “quantitative” in front of them. In industry, we will see data science transforming everything from healthcare to media.

http://datascience.nyu.edu/what-is-data-science/

Actually it refers to data mining

Dear Colleagues,

Good Day,

"! want to leverage the creativity of researchers across mathematics, statistics, data mining, computer science, biology, medicine, and the public at large."

                                                                            ------Tan Le

Generally, data science refers to the tools and methods used to analyze large amounts of data and the set of practices targeted at the storage, management and analysis of data sets large enough that require distributed computing and storage resources. It is a multidisciplinary area and its methodology and tools, primarily comes from statistics, computer science, where issues of algorithmic efficiency and storage scalability form the main focus. Currently the majority of data sources are internet- and transaction-related, but may also find applications from high-energy physics, meteorology, military simulations, as well as future applications in life sciences.

Data flows in data sewers. Data scientists process it to make it drinkable, that is, consumable, operationalized. ~ L. V. Cauwenberge

Dear Colleagues,

Good Day,

"Three big benefits of big data analytics

Today, big data analytics is no longer just an experimental tool. Many companies have begun to achieve

real results with the approach, and are expanding their efforts to encompass more data and models. For a SAS-sponsored project called “Big Data in Big Companies” and my new book Big Data at Work, I interviewed more than 50 companies that were using big data analytics. Here’s how they’re getting value:

1. Cost reduction.

2. Faster, better decision making.

3. New products and services.

Ready for prime time

These examples make clear that big data analytics projects are delivering value. There are, of course, still some issues to be worked out with regard to how big data capabilities will evolve, but the time for questioning big data’s business value has passed. These companies and many more have already shown that they can analyze big data successfully to achieve cost reductions, faster and better decisions, and even new offerings for customers. It’s clear that the big data era will be one of dramatic business opportunity – don’t wait too long to exploit its potential."....

Please, see the link...

https://www.sas.com/en_ca/news/sascom/2014q3/Big-data-davenport.html

From my point of view, when you are teaching this topic (say at master's level) to people who are going to go out and use it to create wealth in the real word, Data Science is about using techniques (including machine learning, statistical methods etc.) to analyse / interpret / visualise high-dimensional, high volume, complex and often poorly-structured data. Data Analytics is more about interpreting such analyses (but maybe more dependent on packages like SPSS) and communicating  them to a non-technical business audience for decision support..

What is data science?

By Mike Loukides

Maps are data made into a product.(source: New York Public Library).

We’ve all heard it: according to Hal Varian, statistics is the next sexy job. Five years ago, in What is Web 2.0, Tim O’Reilly said that “data is the next Intel Inside.” But what does that statement mean? Why do we suddenly care about statistics and about data?

In this post, I examine the many sides of data science — the technologies, the companies and the unique skill sets.

The web is full of “data-driven apps.” Almost any e-commerce application is a data-driven application. There’s a database behind a web front end, and middleware that talks to a number of other databases and data services (credit card processing companies, banks, and so on). But merely using data isn’t really what we mean by “data science.” A data application acquires its value from the data itself, and creates more data as a result. It’s not just an application with data; it’s a data product. Data science enables the creation of data products.

One of the earlier data products on the Web was the CDDB database. The developers of CDDB realized that any CD had a unique signature, based on the exact length (in samples) of each track on the CD. Gracenote built a database of track lengths, and coupled it to a database of album metadata (track titles, artists, album titles). If you’ve ever used iTunes to rip a CD, you’ve taken advantage of this database. Before it does anything else, iTunes reads the length of every track, sends it to CDDB, and gets back the track titles. If you have a CD that’s not in the database (including a CD you’ve made yourself), you can create an entry for an unknown album. While this sounds simple enough, it’s revolutionary: CDDB views music as data, not as audio, and creates new value in doing so. Their business is fundamentally different from selling music, sharing music, or analyzing musical tastes (though these can also be “data products”). CDDB arises entirely from viewing a musical problem as a data problem

https://www.oreilly.com/ideas/what-is-data-science

In my opinion, data science it management of large information for any system. Normally, banking, cyber cells, online systems always need huge data management for their systems.

Data science entails the use of data, its analysis, processing and interpretation for decision support and to build data-intensive products and services.

Big Data needs Data Science but Data Science doesn't need Big Data!

"Data science has been around for decades, and it’s not just big data. I hear a lot of people clumping these two together like they go hand-in-hand, which I agree with to an extent. However, big data needs data science but data science doesn’t necessarily need big data. Most of the data a typical company handles on a daily basis or house internally is not big data. Even Facebook and Google break up or segment their data into workable pieces. Data science is big, small, structured, unstructured, messy, clean, etc… It’s more than just analytics. As a data scientist, you’ll become a liaison between the IT department and the C suite. You have to talk both languages and you have to understand the hierarchy of data, you can’t be just an architect or data expert..."

https://www.linkedin.com/pulse/big-data-needs-science-doesnt-need-carla-gentry?trkInfo=VSRPsearchId%3A577811291460302835153%2CVSRPtargetId%3A7555796742914812740%2CVSRPcmpt%3Aprimary&trk=vsrp_influencer_content_res_name

DATA SCIENCE - WHEREWITHAL Application in Agriculture and Manufacturing (Knowledge Protection)

Data DATA SCIENCE - WHEREWITHAL Application in Agriculture and Ma...

The use of the term “Data Science” is becoming increasingly common along with “Big Data.” What does Data Science mean? Is there something unique about it? What skills should a “data scientist” possess to be productive in the emerging digital age characterized by a deluge of data? What are the implications for scientific inquiry?

The term “Science” implies knowledge gained by systematic study. According to one definition, it is a systematic enterprise that builds and organizes knowledge in the form of testable explanations and predictions about the universe.

Data Science might therefore imply a focus around data and by extension, Statistics, which is a systematic study about the organization, properties, and analysis of data

and their role in inference, including our confidence in such inference. Why then do we need a new term, when Statistics has been around for centuries? The fact that we now have huge amounts of data should not in and of itself justify the need for a new term.

The short answer is that it is different in several ways. First, the raw material, the “data” part of Data Science, is increasingly heterogeneous and unstructured – text, images, and video, often emanating from networks with complex relationships among its entities. Secondly, the proliferation of markup languages, tags, etc. are designed to let computers interpret data automatically, making them active agents in the process of sense making.

Source: Dhar, V. (2013). Data science and prediction. Communications of the ACM, 56(12), 64-73.

http://archive.nyu.edu/bitstream/2451/31553/2/Dhar-DataScience.pdf

Dear Colleagues,

Good Day,

"Computer science only indicates the retrospective omnipotence of our technologies. In other words, an infinite capacity to process data (but only data -- i.e. the already given) and in no sense a new vision. With that science, we are entering an era of exhaustivity, which is also an era of exhaustion."

                                                           ------Jean Baudrillard

It is a value capture from data with large economies of scale.

University of Berkeley offers a Master's in data science. It is interesting to see how Berkeley professors define data science

In recent years data science has emerged as the field that exists at the intersection of math and statistics knowledge, expertise in a science discipline, and so-called “hacking skills,” or computer programming ability. While these skills are changing the way that science is practiced, they’re also changing other aspects of society, such as business and technology startups. In a world where rapidly advancing technology is forcibly changing data science practices, universities are struggling to keep up, often losing good researchers to industries that place a high value on their computational skills.

Despite its increasing importance and relevance, it’s almost impossible to pin down what data science actually is. Data scientists hate doing it. Bloom describes data science as a context-dependent way of thinking about and working around data—a set of skills derived from statistics, computer science, and physical and social sciences. Cathryn Carson, the associate dean of Social Science who is heavily involved in BIDS and the new Social Science Data Laboratory (D-Lab), is more interested in how we can use the idea of data science to do more interesting science. This involves bringing people from different areas of expertise together to work on multifaceted problems. “It’s a kind of social engineering,” Carson says. “I don’t even know the ontology to describe it with. It’s not a discipline; it’s not a branch of science; it’s a platform for building a coalition. It’s one of these interdisciplinary, non-disciplinary spaces where people get stuff done in interesting ways, but don’t even know what to call it themselves.”

Please read the rest of this interesting article at:

http://berkeleysciencereview.com/article/first-rule-data-science/

http://berkeleysciencereview.com/article/first-rule-data-science/

Dear Colleagues,

Good Day,

In the spirit of science, there really is no such thing as a 'failed experiment.' Any test that yields valid data is a valid test.

                                                                                      -----   Adam Savage

Data Science is basically, Data and Knowledge (joint) approach to problem solving. The knowledge may emanate from physical, engineering, cognitive and many other domains. It is rather "Statistical science" in disguise. 

According to Harvard Business Review (HBR), the shortage of data scientists is becoming “a serious constraint” in some sectors, hence why there are now specialised courses being offered to help address the issue. The best part? Students who are interested in working in this area, don’t even need to have an appropriate first degree in the subject.

That’s right - those without any previous computer or data science experience at undergraduate level can now study towards a Master’s degree in this area of emerging importance. Crucially, such courses are not limited just to data science, but encapsulate software engineering too, a combination of skills sought after in industry...

http://www.independent.co.uk/student/student-life/Studies/data-science-engineering-computing-mathematics-technology-a7091631.html