Data science is an emerging field leveraging scientific methods to derive insights from data, essential for business decision-making. The process encompasses data acquisition, cleaning, analysis, curation, and storage, focusing on extracting meaningful patterns and trends from large datasets. Ultimately, data science aids organizations in optimizing processes and fostering innovation across various industries.

2. Introduction To Data Science
• Data Science is a field that uses scientific
methods to extract insights from data to help
Business and Industries .
• Data Science is the one of the fastest growing
fields across all industries .
• Data science is used for tools like Data
Analytics ,Machine Learning .

4. Data Science Process
• The initial step of gathering raw data from
various source, like Surveys, Sensors and
Data bases .
• Cleaning and organizing raw data by
removing errors and handling missing
values .
• The process of extracting patterns,
insights and trends from large datasets
using Machine learning techniques.

5. Data values chain

6. Data Acquisition
• Process of gathering , filtering and cleaning
data before it put in data warehouse or other
storage solution on which data analysis can
be carried out.
• Data Acquisition is one of bag data challenges
in terms of infrastructure requirements .
• Infrastructure required to support the
acquisition of big data must deliver low.

7. Data Analysis
• The raw data acquired amenable to use in
decision making as well as domain specific usage
.
• Data analysis involves exploring , transforming
and modelling data.
• From a business point of view synthesizing and
extracting the hidden information with high
potential.

8. Data Curation
• Data curation processes can be categorized
into difference activities such as a content
creation, selection ,validation and
preservation.
• Data curation is responsible for improving
accessibility and quality of data.
• Ensuring that the data are trustworthy and
fit their purpose.

9. Data Storage
• Data storage is persistence and
management of data in scalable way that
satisfies needs of applications.
• Relational database management systems
(RDMS) are majorly used.
• NOSQL technologies have been designed
with scalability goal in mind and present
wide range of solutions based on
alternative data models.

10. Key Data Science Topics To Explore

11. Big Data Analytics
• Big data analytics helps in
organizations harness their data
and use to identify new
opportunities .
• It leads to smarter business moves
higher profits and more efficient
operations .

12. Sources Of Big Data
CATEGORIES
• From human activities
• From the physical world
• From computers
EXAMPLE
• Internet data ,network data ,mobile
network or telecom
• Unstructured (such as a test , audio and
video ) or semi structured(such as a
emails , tweets and weblogs).

13. Big Data Processing
The general categories of activities
involved with big data processing are:
• Ingesting data into the system
• Persisting the data in storage
• Computing and Analyzing data
• Visualizing the results

14. STRUCTURED SEMI-STRUCTRED UNSRTUCTURED

16. Conclusion
• Data science is a crucial field that helps organizations make decisions
and drive innovations across many industries.
• Data Science can help businesses gain insights, optimise process and
make better decisions.
• The Data Science process is structured but it’s important to be
flexible.

17. THANK
YOU