Data science is an interdisciplinary field that uses scientific methods to extract knowledge and insights from data. It unifies statistics, data analysis, machine learning and related methods. Data science is the future of artificial intelligence and can add value to businesses by turning ideas seen in movies into reality. It involves working with large data sets and machine learning. Data science is primarily used for decisions, predictions, and machine learning by uncovering findings from data. Data science and technology delivers methods for solving data-intensive problems ranging from research to software deployment. Feature engineering is selecting or generating useful columns for modeling. Data cleaning takes up most of a data scientist's time along with exploratory analysis, visualization, machine learning, and communication. Data science education

1. Data Science

2. INTRODUCTION:-
Data Science Technology is a key of https://technologymoon.com/ Data Science must to know how to
analyze the problem.
Data science is an interdisciplinary field that uses scientific methods. ,processes,algorithms and systems to
extract knowledge and insights. from data in various forms,both structured and unstructured,similar. to
data mining. Data science is a “concept to unify statistics,data. analysis, machine learing and their related
methods.

3. Meaning of Data Science:-
Data science is the future of Artificial
Intelligence.All the ideas which you seein
Hollywood scientific movies can actually turn
into reality by Data science.Therefore,it is
very important to understand what is Data
science and how can it add value to your
business.

4. Principle of Data Science:-
Data science involves working with
large amounts of data sets. You may
want to be familier with machine
larning. The business world produces a
vast amount of data frequently.

5. Uses of Data Science:-
Data science is primarily used to make
decisions and predictions making use of
predictive causal analytics, prescriptive
analytics and machine larning. This
aspect of data science is all about
uncovering findings from data.

6. Data Science & Technology(DST):-
The Data Science and Technology
(DST) Department delivers innovative
methods for solving data-intensive
science problems. DST activities range
from bas and applied research to
deployment of software tools.

7. Data Science features:-
Feature Engineering is the art and
science of selecting and/or generating
the columns in a data table for a
machine learning model. When we
prepare a table for modeling, not all
columns are useful in their raw form.

8. Learning data science:-
This technique is the one which most
people are actually to when they talk
about feature engineeringWe may
generate a new attribute such as
number of claims a member has filed
for in a given time period, by
combining date attribute and a
nominal attribute

9. Data Science Projects:-
Data Cleaning. Data scientists can
expect to spend up to 80% of
theirtime cleaning data,Exploratory
Data Analysis, Interactive Data
Visualization,.Machine Learning.
Communication.

10. Conclusion:-
Data science education is well into its formative stages of development; it is evolving into a
self-supporting discipline and producing professionals with distinct and complementary
skills relative to professionals in the computer, information, and statistical sciences.
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