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

1. DATA & INFORMATION
SCIENCE
ZEESHAN JAVED
17581556-019
PPT DATE: 13TH JAN,2021

2. “You can have data without information, but you
cannot have information without data”

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. 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. 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. 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. 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. BACHELOUR OF SCIENCE IN INFORMATION
TECHNOLOGY

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. WORKING (SIMPLE SCENERIO)

11. DATA
Data is a collection of facts, such as numbers, words,
measurements, observations or just descriptions of things.

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. 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. 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. 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. DOMAINS:

17. DIFFERENT TOOLS:

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. 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. 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. THANK YOU