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Information & data science (1) converted

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Information & data science (1) converted

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WHAT IS DATA AND INFORMATION SCIENCE?
• IMPORTANCE
• WORKING
• DATA & INFORMATION
• ROLE OF DATA AND INFORMATION IN IT
• IMPORTANCE OF INFORMATION SCIENCE
• HOW DATA SCIENCE WILL BE CONDUCTED

WHAT IS DATA AND INFORMATION SCIENCE?
• IMPORTANCE
• WORKING
• DATA & INFORMATION
• ROLE OF DATA AND INFORMATION IN IT
• IMPORTANCE OF INFORMATION SCIENCE
• HOW DATA SCIENCE WILL BE CONDUCTED

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Information & data science (1) converted

  1. 1. DATA & INFORMATION SCIENCE ZEESHAN JAVED 17581556-019 PPT DATE: 13TH JAN,2021
  2. 2. “You can have data without information, but you cannot have information without data”
  3. 3. KEY POINTS: • WHAT IS DATA AND INFORMATION SCIENCE? • IMPORTANCE • WORKING • DATA & INFORMATION • ROLE OF DATA AND INFORMATION IN IT • IMPORTANCE OF INFORMATION SCIENCE • HOW DATA SCIENCE WILL BE CONDUCTED
  4. 4. DATA vs INFORMATION SCIENCE: Data science is the discovery of knowledge or actionable information in data. Information science is the design of practices for storing and retrieving information.
  5. 5. INFORMATION The definition of information is news or knowledge received or given. An example of information is what's given to someone who asks for background about something. Information is the summarization of data. Technically, data are raw facts and figures that are processed into information, such as summaries and totals.
  6. 6. INFORMATION SCIENCE Information science (also known as information studies) is an academic field which is primarily concerned with analysis, collection, classification, manipulation, storage, retrieval, movement, dissemination, and protection of information.
  7. 7. ROLE OF INFORMATION SCIENCE IN IT Information science is the science and practice dealing with the effective collection, storage, retrieval, and use of information. It is concerned with recordable information and knowledge, and the technologies and related services that facilitate their management and use.
  8. 8. BACHELOUR OF SCIENCE IN INFORMATION TECHNOLOGY
  9. 9. WHY INFORMATION SCIENCE IS IMPORTANT Information science brings together and uses the theories, principles, techniques and technologies of a variety of disciplines toward the solution of information problems. It is concerned with recordable information and knowledge, and the technologies and related services that facilitate their management and use.
  10. 10. WORKING (SIMPLE SCENERIO)
  11. 11. DATA Data is a collection of facts, such as numbers, words, measurements, observations or just descriptions of things.
  12. 12. DATA SCIENCE Data science is used in business functions such as strategy formation, decision making and operational processes. It touches on practices such as artificial intelligence, analytics, predictive analytics and algorithm design. The discovery of knowledge and actionable information in data.
  13. 13. WHY DATA SCIENCE IS IMPORTANT Data is one of the important features of every organization because it helps business leaders to make decisions based on facts, statistical numbers and trends. Data science is an extension of various data analysis fields such as data mining, statistics, predictive analysis and many more.
  14. 14. ROLE OF DATA SCIENCE IN IT Data scientists help companies interpret and manage data and solve complex problems using expertise in a variety of data niches. They generally have a foundation in computer science, modeling, statistics, analytics, and math - coupled with a strong business sense.
  15. 15. Why Data Science? Traditionally, the data that we had was mostly structured and small in size, which could be analyzed by using simple BI tools. Unlike data in the traditional systems which was mostly structured, today most of the data is unstructured or semi-structured. Let’s have a look at the data trends in the image given below which shows that by 2020, more than 80 % of the data will be unstructured.
  16. 16. DOMAINS:
  17. 17. DIFFERENT TOOLS:
  18. 18. How data science is conducted: Planning • Define a project and its potential outputs. Building a data model • Data scientists often use a variety of open source libraries or in-database tools to build machine learning models. Evaluating a model • Data scientists must achieve a high percent of accuracy for their models before they can feel confident deploying it.
  19. 19. Explaining models • : Being able to explain the internal mechanics of the results of machine learning models in human terms has not always been possible—but it is becoming increasingly important. Deploying a model • Taking a trained, machine learning model and getting it into the right systems is often a difficult and laborious process. Monitoring models • Unfortunately, deploying a model isn’t the end of it. Models must always be monitored after deployment to ensure that they are working properly.
  20. 20. REFERENCES https://simplicable.com/new/data-science-vs-information- science#:~:text=Data%20science%20is%20the%20discovery,for%20storing%20and%20retrieving%20information. https://www.asist.org/about/what-is-information-science/ https://www.asist.org/about/what-is-information- science/#:~:text=%E2%80%9CInformation%20science%20is%20the%20science,facilitate%20their%20management%20and%20 use.&text=Information%20science.,-In%20M.%20J.%20Bates https://en.wikipedia.org/wiki/Information_science https://www.asist.org/about/what-is-information- science/#:~:text=%E2%80%9CInformation%20science%20is%20the%20science,facilitate%20their%20management%20and%20 use. https://medium.com/@crampeteb/what-is-the-scope-for-data-science-in-2020-applications-and-salary-7f57ec91e4a2
  21. 21. THANK YOU

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