Dr. Mustafa Degerli - 2020 - Introduction to Data Science

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Dr. Mustafa Değerli completed an Introduction to Data Science course on LinkedIn Learning on April 12, 2020, which lasted 1 hour and 48 minutes. The Vice President of Learning Content at LinkedIn congratulated Dr. Değerli on continuing to learn and expanding his perspective and skills to make himself more in demand. The certificate was issued from LinkedIn Learning located in Sunnyvale, California.

Engineering

Certificate of Completion
Congratulations, Dr. Mustafa Değerli
Introduction to Data Science
Course completed on Apr 12, 2020 • 1 hour 48 min
By continuing to learn, you have expanded your perspective, sharpened your
skills, and made yourself even more in demand.
VP, Learning Content at LinkedIn
LinkedIn Learning
1000 W Maude Ave
Sunnyvale, CA 94085
Certificate Id: AWCPAr60JtgLOLEMRYo-_rFT_QAM

Once you get beyond basic programming, you'll need a database. Databases provide a consistent, organized structure for storing and retrieving large amounts of data. They allow programmers to build more complex applications that can take orders, process payments, make recommendations, authenticate users, and more. This course provides the foundation you need to get started in database programming. Explore the terminology: normal forms, ACID and CRUD, referential integrity, transactions, records, and tables. Learn what role keys and unique values play in a relational model. Discover how to design the best system to contain your data, starting with the tables and relationships at the core of your database. Find out how to write queries to extract the data you need, and how to juggle the different demands of storage, access, performance, and security. Instructor Scott Simpson provides practical examples and clear explanations that will help you design databases that can withstand the needs of your applications, your data, and your users. Learning Objectives: Relational databases Keys and unique values Planning a database Creating tables Defining relationships Normalization and denormalization Writing queries Sorting results Joining tables Modifying data

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Abstract Data-drivenhealthcareistrulyvaluableandpromising.Aslongasrele- vant data are gathered, probed, used, and managed in a good fashion, significant improvements in the dependability of healthcare practices are achievable. Neverthe- less, unless privacy facets of relevant sensitive data are addressed, there are notable concerns regarding data-driven healthcare policies and applications. In general, technical and engineering facets of such interventions are concentered on to a greater extent, but privacy facets are not adequately addressed. This chapter highlights and discusses privacy issues in data-driven health care. A comprehensive review and distillation of pertinent literature and works yielded relevant results and interpretations. Purposefully, generic privacy issues are elaborated in the beginning. Addition- ally, areas for improvement regarding privacy issues in data-driven health care are underlined and discussed. People, policy, and technology aspects are also explained and deliberated. Moreover, how privacy is related to people and policy to ensure the success in data-driven healthcare practices is discussed in this chapter. Besides, people’s perceptions about privacy are distilled and reported. The focal impact of this chapter is to deliver a contemporary interpretation and discussion regarding privacy issues in data-driven health care. Product developers and managers, policy-makers, and pertinent researchers might benefit from this chapter in order to improve related knowledge and implementations.

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