Data Preprocessing

Due to their usually huge size (often several gigabytes or more) and their probable origin from various, heterogeneous sources, the real-world databases of today are highly susceptible to noisy, incomplete, and unreliable information.

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Introduction to Data Science

  1. Why Data Science?
  2. What is Data Science?
  3. What is Data Science process?
  4. What Kinds of Data Can Be Analyzed?
  5. What Kinds of Patterns Can Be Analyzed?
  6. Which Technologies are Used?
  7. Which Kinds of Applications Are Targeted?
  8. Major issues in Data Science
  9. Data Science and Society

Why Data Science?

We live in a world where a vast amount of data are collected daily. It is a significant necessity to analyze such data to discover knowledge from it.

We live in the information age

It is a popular saying, but in fact, we live in the information age. Every day, terabytes or petabytes of data flow into our computer networks, the World Wide Web (WWW), and various data storage devices from the company, society, science and engineering, medicine, and almost every other aspect of everyday life. Powerful and versatile tools are badly required to automatically discover and convert precious information from enormous quantities of data into structured knowledge.

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