Data Science comparison with AI, ML, BI, and data warehousing, data mining.

1. Data Science
Comparison with Business Intelligence,
Artificial Intelligence, Machine Learning and
Data Warehousing/Data Mining

2. Data Science vs Machine Learning
Data Science
• Data Science is a field about processes and
systems to extract data from structured and semi-
structured data.
• Data Science as a broader term not only focuses
on algorithms statistics but also takes care of the
data processing.
• Data in Data Science maybe or maybe not
evolved from a machine or mechanical process.
• Many operations of data science that is, data
gathering, data cleaning, data manipulation, etc.
• It is used for discovering insights from the data.
• Example: Netflix uses Data Science technology.
Machine Learning
• Machine Learning is a field of study that gives
computers the capability to learn without being
explicitly programmed.
• It is only focused on algorithm statistics.
• It uses various techniques like regression and
supervised clustering.
• It is three types: Unsupervised learning,
Reinforcement learning, Supervised learning.
• It is used for making predictions and classifying
the result for new data points.
• Example: Facebook uses Machine Learning
technology.

3. Data Science vs Artificial Intelligence
Data Science
• Data Science is a field about processes and
systems to extract data from structured and semi-
structured data.
• Data Science as a broader term not only focuses
on algorithms statistics but also takes care of the
data processing.
• Data Science will have a variety of different
types of data, including structured, semi-
structured, and unstructured type of data.
• Many operations of data science that is, data
gathering, data cleaning, data manipulation, etc.
• It is used for discovering insights from the data.
• Its applications are advertising, marketing,
Healthcare, etc.
Artificial Intelligence
• Artificial Intelligence is the implementation of a
predictive model to forecast future events and
trends.
• Automation of the process and the granting of
autonomy to the data model are the main goals of
artificial intelligence.
• AI uses standardized data in the form of vectors
and embeddings.
• It has a lot of high levels of complex processing.
• It is used for making predictions and classifying
the result for new data points.
• Its application is robotics, automation, etc.

4. Data Science vs Business Intelligence
Data Science
• Data Science is a field about processes and
systems to extract data from structured and semi-
structured data.
• It focuses on the future.
• It deals with both structured as well as
unstructured data.
• Many operations of data science that is, data
gathering, data cleaning, data manipulation, etc.
• It is used for discovering insights from the data.
• Greater business value is achieved with data
science in comparison to business intelligence as
it anticipates future events.
Business Intelligence
• It is basically a set of technologies, applications
and processes that are used by the enterprises for
business data analysis.
• It focuses on the past and present.
• It mainly deals only with structured data.
• It is much simpler when compared to data
science.
• Business Intelligence helps in performing root
cause analysis on a failure or to understand the
status.
• Business Intelligence has lesser business value as
the extraction process of business value carries
out statically.

5. Data Science vs DW-DM
Data Science
• Data Science is a field about processes and
systems to extract data from structured and semi-
structured data.
• It is focuses on historical and future data needs.
• Many operations of data science that is, data
gathering, data cleaning, data manipulation, etc.
• Works with structured and unstructured data.
• Applicable in virtually every industry with data-
driven needs.
Data Warehousing/Data Mining
• Data Warehousing is the technology of
storing/retrieving large amounts of data.
• It is focuses on historical data for mining.
• Primarily deals with data storage, retrieval and
mining.
• Focuses on structured data for analysis.
• Applied in finance, retail and customer
relationship management.

6. Artificial
Intelligence
Machine
Learning
Deep
Learning
Data
Science
Summary

7. Thanks for Watching!