.

In 2020, HandWiki has become the largest online wiki encyclopedia for major science topics (physics, math etc.) and computing. It has more than 105,000 scholarly articles, incorporating the current Wikipedia articles, scholarly articles submitted to the Wikipedia foundation (but later rejected), and various wiki books related to programming languages and computers. Unlike Wikipedia, the articles are structured by topic and publication type. Plus it includes a quite decent (but not perfect) converter of Wikicode (MediaWiki) articles to the LaTeX format for journal publication, and an author control. The latter feature can be used for real-time collaboration on paper drafts, similar to the paid version of Overleaf.

You can search by keyword online, here, or check the data science section, here. Below is sample content related to the history of datascience.

The term "data science" has appeared in various contexts over the past thirty years but did not become an established term until recently. In an early usage, it was used as a substitute for computer science by Peter Naur in 1960. Naur later introduced the term "datalogy".^{[17]} In 1974, Naur published *Concise Survey of Computer Methods*, which freely used the term data science in its survey of the contemporary data processing methods that are used in a wide range of applications.

The modern definition of "data science" was first sketched during the second Japanese-French statistics symposium organized at the University of Montpellier II (France) in 1992^{[18]}. The attendees acknowledged the emergence of a new discipline with a specific focus on data from various origins, dimensions, types and structures. They shaped the contour of this new science based on established concepts and principles of statistics and data analysis with the extensive use of the increasing power of computer tools.

In 1996, members of the International Federation of Classification Societies (IFCS) met in Kobe for their biennial conference. Here, for the first time, the term data science is included in the title of the conference ("Data Science, classification, and related methods"),^{[19]} after the term was introduced in a roundtable discussion by Chikio Hayashi.^{[4]}

In November 1997, C.F. Jeff Wu gave the inaugural lecture entitled "Statistics = Data Science?"^{[20]} for his appointment to the H. C. Carver Professorship at the University of Michigan.^{[21]} In this lecture, he characterized statistical work as a trilogy of data collection, data modeling and analysis, and decision making. In his conclusion, he initiated the modern, non-computer science, usage of the term "data science" and advocated that statistics be renamed data science and statisticians data scientists.^{[20]} Later, he presented his lecture entitled "Statistics = Data Science?" as the first of his 1998 P.C. Mahalanobis Memorial Lectures.^{[22]} These lectures honor Prasanta Chandra Mahalanobis, an Indian scientist and statistician and founder of the Indian Statistical Institute.

In 2001, William S. Cleveland introduced data science as an independent discipline, extending the field of statistics to incorporate "advances in computing with data" in his article "Data Science: An Action Plan for Expanding the Technical Areas of the Field of Statistics," which was published in Volume 69, No. 1, of the April 2001 edition of the International Statistical Review / Revue Internationale de Statistique.^{[23]} In his report, Cleveland establishes six technical areas which he believed to encompass the field of data science: multidisciplinary investigations, models and methods for data, computing with data, pedagogy, tool evaluation, and theory.

- 11 data science skills for machine learning and AI
- Get started on AWS with this developer tutorial for beginners
- Microsoft, Zoom gain UCaaS market share as Cisco loses
- Develop 5G ecosystems for connectivity in the remote work era
- Choose between Microsoft Teams vs. Zoom for conference needs
- How to prepare networks for the return to office
- Qlik keeps focus on real-time, actionable analytics
- Data scientist job outlook in post-pandemic world
- 10 big data challenges and how to address them
- 6 essential big data best practices for businesses
- Hadoop vs. Spark: Comparing the two big data frameworks
- With accelerated digital transformation, less is more
- 4 IoT connectivity challenges and strategies to tackle them

Posted 10 May 2021

© 2021 TechTarget, Inc. Powered by

Badges | Report an Issue | Privacy Policy | Terms of Service

**Most Popular Content on DSC**

To not miss this type of content in the future, subscribe to our newsletter.

- Book: Applied Stochastic Processes
- Long-range Correlations in Time Series: Modeling, Testing, Case Study
- How to Automatically Determine the Number of Clusters in your Data
- New Machine Learning Cheat Sheet | Old one
- Confidence Intervals Without Pain - With Resampling
- Advanced Machine Learning with Basic Excel
- New Perspectives on Statistical Distributions and Deep Learning
- Fascinating New Results in the Theory of Randomness
- Fast Combinatorial Feature Selection

**Other popular resources**

- Comprehensive Repository of Data Science and ML Resources
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- 100 Data Science Interview Questions and Answers
- Cheat Sheets | Curated Articles | Search | Jobs | Courses
- Post a Blog | Forum Questions | Books | Salaries | News

**Archives:** 2008-2014 |
2015-2016 |
2017-2019 |
Book 1 |
Book 2 |
More

**Most popular articles**

- Free Book and Resources for DSC Members
- New Perspectives on Statistical Distributions and Deep Learning
- Time series, Growth Modeling and Data Science Wizardy
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- Comprehensive Repository of Data Science and ML Resources
- Advanced Machine Learning with Basic Excel
- Difference between ML, Data Science, AI, Deep Learning, and Statistics
- Selected Business Analytics, Data Science and ML articles
- How to Automatically Determine the Number of Clusters in your Data
- Fascinating New Results in the Theory of Randomness
- Hire a Data Scientist | Search DSC | Find a Job
- Post a Blog | Forum Questions

## You need to be a member of Data Science Central to add comments!

Join Data Science Central