Data science is nothing but the study of data. It is an interdisciplinary field and it uses scientific methods of developing, storing and analyzing the data to extract knowledge and insights from unstructured data. Data science can also be defined as Data Mining and Big data.

1. DataScience
IQ Trainings
DataScience

2. OUTLINE
Topics
Covered:-
Introduction to DataScience?
Core Components of Data Science?
Types of DataScientists?
What is Big Data?
Challenges in BigData?
What is Hadoop?
What is MachineLearning?
Tools for DataScience?

3. What is Data
Science?
Data science is nothing but study of data.it is a
inter-disciplinary field and it uses scientific
methods of developing ,storing and analyzing
the data to extract knowledge and insights
from unstructured data.Data science can also
be defined as Data Mining ,and Big data.

4. IQ TRAININGS
How is Data
usedin Science?
.To extract meaningful data, Data
science uses Artificial intelligence and
Machine Learning to predict future
patterns and exactbehavior.
What is Data?
How itworks?
Data is an individual units of information
that is used for processing.Data science is
a combination of computer science,
mathematics, and statistics. AData
scientist must have to know the
components and analyze the statistics and
extract the information from the
unstructured data.

5. Data Science
Components ?

6. Core Components
of Data Science?
1.Data Architecture
2.Machine Learning
3.Analytics
Types ofData
Scientists?
1.Data Businesspeople
2.Data Creatives
3.Data Researchers
4.Data Developers

7. STRUCTURED DATA :-
In Structured data, the data that can be processed,
stored, retrieved will be in a fixedformat.
UNSTRUCTURED DATA :-
In unstructured data, the data cannot be adjusted
properlyinto the rows and columns of a relational data
base.
SEMI- STRUCTURED DATA:-
Semi-structured data lies between structured data and
unstructured data. itis a form of data that does not obey the
formal structure of data that is associated with relational data
bases.
What is BigData?
Big data is a technology that is
designed to analyze process and extractthe
information from large data sets.
Systems or Enterprises generate huge
amounts of data fromTerabytes to and even
Peta bytes ofInformation.
TYPES OF BIG DATA?

8. Challenges in Big Data?
The most important challenges that are included in Big data are:-
1. Data Capturing
2.Data Storage
3.Data Analysis
4.Data Transfer
5.Visualization
6.Data privacy
7.Data Source
8.Information privacy…etc

9. What is Hadoop ?
Apache Hadoop is a framework and
an open-source data management that is
used for processing large data sets. In
2004 Google published a paper on a process
called Map-reduce which is a framework that
provides a parallel processing model and
associated implementation that process a
large amount of data. Later this framework
was adopted byApache open source project
and they renamed itas Hadoop.

10. KeyCharacterstics
of Hadoop
1.Scalable
2.Flexible
3.Reliable
4.Economical
5.Robust Ecosystem

11. Machine Learning
in Data Science?
Machine learning is an application of data
science that focuses on the development of
computer programs that can access the data
and ability to learn automatically.
Mahout algorithms are implemented on the
top ofApache Hadoop using Map Reduce.

12. Applications of
Machine Learning
1.Medical Diagnosis
2.Image Processing
3.Prediction
4.Virtual Personal Assistants(Siri,Alexa,
Google)
5.Video Surveillance
6.Social MediaServices
7.Online customersupport
8.Search Engine results in refining etc

13. Tools forData
Science?
1.SAS
2.Apache spark
3.Big ML
4.D3.Js
5.MATLAB
6.Excel
7.Ggplot2
8.Tableau
9.Jupyter
10.Matplotlib
11.Tensor flow
12.Weka
13.NLTK

14. Thank You

15. IQ Trainings
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