Data Science
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The document provides an overview of data science including its history and introduction. It discusses how data science emerged in the late 1990s and early 2000s, with Jim Gray coining the term "data-driven science" in 2007. It defines a data scientist as a new breed of analytical expert who uses technical skills to solve complex problems and explore which issues need addressing. Data scientists build machine learning applications and their toolbox includes skills like data visualization, machine learning, deep learning, and data preparation. The document also compares data science to related fields of big data and data analytics.
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Data Science
- 1. DATA SCIENCE 1 Presented by: Rabin BK BSc.CSIT 5th Semester
- 2. History Introduction Data Scientist Comparative study References 2
- 3. History 3 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
- 6. Data Scientist 6 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.. 7 Who data scientist really is: Source: https://medium.com[9]
- 8. What’s in a data scientist’s toolbox? 8 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 9 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/ 10
- 11. Queries 11