History Of Data Science

Has Data-Science ever impacted your life? If yes, then you must keep in your mind that it would be constantly groping you now and then in a more stipulated manner.
Although the world is aware of the benefits of Data-Science you need to know the ultimate origin of Data-Science and its existence in the Universe.

The term "Data-Science" has been followed back to 1974, when Peter Naur proposed it as an elective name for software engineering.

In 1996,

The International Federation of Classification Societies turned into the prime conference to specifically feature data science as a domain.

During the 1990s,

The most familiar terms for the process of finding patterns in massive datasets were considered as "Knowledge Discovery" and "Data mining".

However, after the 1985,

Lecture held in the Chinese Academy of Sciences in Beijing.

In 1997,

C.F. Jeff Wu proposed that Statistics should be called Data Science. The reason behind being that a unique name would contribute to Statistics shed inaccurate stereotypes, such as being synonymous with accounting, or limited to data description and enhancement.

In simple words, Data Scientists try to get insights from massive amounts of data that can help companies make smarter business decisions. 

Data Science uses a bunch of data-oriented technologies, namely SQL, Python, R, and Hadoop, etc. However, it extensively uses statistical analysis, data visualization, distributed architecture, and more to extract meaning out of sets of data. 

The insights extracted through Data Science applications are used to guide business processes and reach organizational goals.

Technologies in Data Science

Come on let’s understand some basic terminologies that together constitute Data-Science - 

Data mining aims to discover previously unseen knowledge in the data.
Artificial intelligence (AI) makes machines smarter, aiming to create a system that behaves like a human.
Machine learning is a subset of AI. However, Machine learning aims to develop algorithms that can learn from historical data and improve the system with user experiences.
Deep learning is considered a subset of ML, in which data is passed via multiple numbers of non-linear transformations to calculate a precise output.
Special platforms designed for cloud computing, allows keeping the environmental costs low, and enabling the Data Scientists to pay only for the resources they use in particular.

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Brief History Of Data Science

Data Science has transformed various aspects of the technological world. Let's take a clear overview of the initial origin of Data science and its emergence.

In 1962,

American mathematician John W. Tukey wrote  “The Future of Data Analysis” in which he said that the first milestone in the history of Data Science will be globally recognized. The influence of John Tukey in Statistics is enormous, but the most famous coinage attributed to him is related to Computer Science.

NOTE - John W. Tukey was the first to introduce the term "bit" as a contraction of "binary digit."

In 1974,

during the Concise Survey of Computer Methods published by Peter Naur surveyed Data processing methods across a wide variety of applications. 
Peter Naur explains Data-Science as: “The science of dealing with data, once they have been established, while the relation of the data to what they represent is delegated to other fields and sciences.”

In the year 1977,

The International Association for Statistical Computing (IASC) was successfully established.

Soon in 1989,

Gregory Piatetsky-Shapiro organized and chaired the first Knowledge Discovery in Databases (KDD) workshop.

In 1994,

BusinessWeek had published a cover story on “Database Marketing.”

In 1996,

During a conference of the International Federation of Classification Societies (IFCS), for the very first time, the term “Data Science” was included in the title of the conference (“Data science, classification, and related methods”). 

NOTE - In the same year, Usama Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyth published “From Data Mining to Knowledge Discovery in Databases.”

In 1997,

Jeff Wu proposed Statistics to be renamed as “Data Science” and Expert Statisticians to be renamed as “Data Scientists” during his inaugural lecture as the H. C. Carver Chair in Statistics at the University of Michigan.

In 1998,

Hayashi Chikio complimented Data science as a new, interdisciplinary concept, with 3 basic aspects: Data design, collection, and analysis.
Breaking it down into much simpler terms one can understand a Data Scientist as a person who creates programming code, and combines it with statistical knowledge to create insights from data.

Hence, being an interdisciplinary field Data science is much more focused on extracting knowledge from data sets, which are typically large (i.e. big data), and applying the knowledge and actionable insights from data to solve problems in a wide range of application domains.

This interesting field encompasses preparing data for analysis, formulating data science problems, analyzing data, developing data-driven solutions, and presenting findings to inform high-level decisions in a broad range of application domains. As such, it incorporates skills from computer science, statistics, mathematics, data visualization, data integration, complex systems, communication and business.

Statistician Nathan Yau, drawing on Ben Fry, has linked Data science to man-computer interaction: users should be able to intuitively control and explore data. 

In 2015,

The American Statistical Association identified Database management system, Statistics and ML, distributed and parallel systems as the 3 emerging foundational professional communities.

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