datascience

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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.[1][2] 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"

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DATA SCIENCE:

DATA SCIENCE https://www.besanttechnologies.com/training-courses/data-science-training-in-bangalore?utm_source=backlinks&utm_medium=ppt&utm_campaign=coursepage&utm_term=datascience&utm_content=kiruthika

WHAT IS DATA SCIENCE:

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.

WHY DATA SCIENCE:

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

COMPONENTS OF DATA SCIENCE:

COMPONENTS OF DATA SCIENCE Statistics. Visualization Machine Learning Deep Learning

TOOLS FOR DATA SCIENCE:

TOOLS FOR DATA SCIENCE

APPLICATIONS OF DATA SCIENCE:

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

CHALLENGES OF DATA SCIENCE:

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

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