Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data. Data science is the same concept as data mining and big data: "use the most powerful hardware, the most powerful programming systems, and the most efficient algorithms to solve problems".

1. DATA SCIENCE
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2. WHAT IS DATA SCIENCE
 Data science is a multi-disciplinary field that uses
scientific methods, processes, algorithms and systems to
extract knowledge and insights from structured and
unstructured data.
 Data science is the same concept as data mining and big
data: “use the most powerful hardware, the most
powerful programming systems, and the most efficient
algorithms to solve problems”.
 The term "data science" became a buzzword. It is now
often used interchangeably with earlier concepts
like business analytics or business intelligence.

3. WHY DATA SCIENCE
 Data Science can help you to detect fraud using
advanced machine learning algorithms
 It helps you to prevent any significant monetary losses
 Allows to build intelligence ability in machines
 You can perform sentiment analysis to gauge customer
brand loyalty
 It enables you to take better and faster decisions
 Helps you to recommend the right product to the right
customer to enhance your business

4. COMPONENTS OF DATA SCIENCE
 Statistics.
 Visualization
 Machine Learning
 Deep Learning

5. TOOLS FOR DATA SCIENCE

6. APPLICATIONS OF DATA SCIENCE
 Internet Search
 Recommendation system
 Image and Speech recognition system
 Gaming World
 Online price Comparison

7. CHALLENGES OF DATA SCIENCE
 Unavailability of/difficult access to data
 Data Science results not effectively used by business
decision makers
 Explaining data science to others is difficult
 Privacy issues
 Lack of significant domain expert
 If an organization is very small, they can't have a Data
Science team