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Definition of Key Terms

Johannes Kepler (1571-1630) is often considered one of the first Data Scientists due to his groundbreaking work in analyzing astronomical data to uncover the laws governing planetary motion. In the early 1600s, Kepler meticulously studied large amounts of observational data gathered by the astronomer Tycho Brahe. By applying mathematical models and repeated analysis, Kepler discovered that planets move in elliptical orbits around the sun, challenging the long-standing belief in circular orbits. His findings were published in the Rudolphine Tables in 1627, which were the most precise planetary position tables of the time. These tables, based on Kepler's laws of planetary motion, were crucial for navigation and scientific research for over a century, illustrating his innovative use of data to revolutionize our understanding of the solar system. This work laid the foundation for modern data analysis practices.

In the world of data analysis and processing, terms like Data Science, Machine Learning, Artificial Intelligence (AI), Business Intelligence (BI), Predictive Analytics, and Data Engineering are often used. Each of these terms refers to distinct but interconnected fields and methods. To gain a better understanding of these areas, it is important to clearly define these terms and explore how they are related.

Definitions of Key Terms

To familiarize yourself with fundamental concepts, research the following terms:

  • Data Science: Investigate what Data Science encompasses and how it differs from other disciplines. What are the central tasks and methods involved in Data Science?

  • Machine Learning: Research how Machine Learning is defined and what role it plays within Data Science. Explore the different types of Machine Learning and their applications.

  • Artificial Intelligence (AI): What is Artificial Intelligence, and how does it differ from Machine Learning? What are the application areas of AI that extend beyond Machine Learning?

  • Business Intelligence (BI): BI is a commonly used term in businesses. What does BI mean, and how does it differ from Data Science? What tools and methods are typical for BI?

  • Predictive Analytics: Investigate what Predictive Analytics is and how it is used in the context of Data Science and BI. How is Predictive Analytics related to Machine Learning?

  • Data Engineering: Find out what tasks and responsibilities are included in Data Engineering and how it differs from Data Science. Why is Data Engineering an essential part of successful data projects?