History of data science, Introduction, Who data scientist are, a comparative study

1. DATA SCIENCE
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Presented by: Rabin BK
BSc.CSIT 5th Semester

2.  History
 Introduction
Data Scientist
Comparative study
References
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3. History
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 In November 1997, C.F. Jeff Wu gave the inaugural lecture entitled "Statistics =
Data Science?" for his appointment
 In April 2002, the International Council for Science (ICSU): Committee on
Data for Science and Technology (CODATA) started the Data Science Journal
 Around 2007, Turing award winner Jim Gray envisioned "data-driven science"
as a "fourth paradigm" of science that uses the computational analysis of large
data as primary scientific method
 In 2012, Harvard Business Review article DJ Patil with Jeff Hammerbacher
defined their jobs at LinkedIn and Facebook respectively stating about Data
Science
 In 2013, the IEEE Task Force on Data Science and Advanced Analytics was
launched

4. Introduction
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Source: towardsdatascience.com[3]Source: crate.io[12]

5. Introduction contd..
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Source: https://crate.io[12]

6. Data Scientist
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 New breed of analytical data expert who have the technical skills to solve
complex problems – and the curiosity to explore what problems need to be
solved
 Focuses on answering the question of why something is happening and how
to improve it
 They build machine learning applications to be deployed into production

7. Data Scientist contd..
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Who data scientist really is:
Source: https://medium.com[9]

8. What’s in a data scientist’s toolbox?
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 Data visualization: the presentation of data in a pictorial or graphical format
so it can be easily analyzed.
Machine learning: a branch of artificial intelligence based on mathematical
algorithms and automation.
Deep learning: an area of machine learning research that uses data to model
complex abstractions.
Pattern recognition: technology that recognizes patterns in data (often used
interchangeably with machine learning).
Data preparation: the process of converting raw data into another format so
it can be more easily consumed.
Text analytics: the process of examining unstructured data to glean key
business insights.

9. Data Science vs. Big Data vs. Data Analytics
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Source:
https://www.simp
lilearn.com[7]

10. References
1. https://www.sas.com/en_us/insights/analytics/what-is-a-data-scientist.html
2. https://www.dataschool.io/resources/
3. https://towardsdatascience.com/data-science-for-beginners-850c3376a34a
4. https://www.datascience.com/resources/white-papers/data-science-definitions
5. https://www.dataversity.net/brief-history-data-science/#
6. https://101.datascience.community/
7. https://www.simplilearn.com/data-science-vs-data-analytics-vs-machine-learning-article
8. https://www.simplilearn.com/data-science-vs-big-data-vs-data-analytics-article
9. Google Images
10. https://medium.com/@hafidz/hindsight-8-months-down-the-analytics-road-37c69fc6d99
11. https://gifer.com/en/QNtd
12. https://crate.io/a/machine-learning-and-cratedb-part-two-getting-started-with-jupyter/
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11. Queries
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