Data science is a multi-disciplinary field that uses scientific
methods, processes, algorithms, and systems to extract knowledge
and insights from structured (labeled) and unstructured (unlabeled)
data. It is the future of Artificial Intelligence (AI) and a
necessity of the future to make things easier and more productive.
In simple terms, data science is the discovery of data or
uncovering hidden patterns (such as complex behaviors, trends, and
inferences) from data. Moreover, Big Data analytics/data analytics
are the analysis mechanisms used in data science by data
scientists. Several tools, such as Hadoop, R, etc., are used to
analyze this large amount of data to predict valuable information
and for decision-making. Note that structured data can be easily
analyzed by efficient (available) business intelligence tools,
while most of the data (80% of data by 2020) is in an unstructured
form that requires advanced analytics tools. But while analyzing
this data, we face several concerns, such as complexity,
scalability, privacy leaks, and trust issues. Data science helps us
to extract meaningful information or insights from unstructured or
complex or large amounts of data (available or stored virtually in
the cloud). Data Science and Data Analytics: Opportunities and
Challenges covers all possible areas, applications with arising
serious concerns, and challenges in this emerging field in detail
with a comparative analysis/taxonomy. FEATURES Gives the concept of
data science, tools, and algorithms that exist for many useful
applications Provides many challenges and opportunities in data
science and data analytics that help researchers to identify
research gaps or problems Identifies many areas and uses of data
science in the smart era Applies data science to agriculture,
healthcare, graph mining, education, security, etc. Academicians,
data scientists, and stockbrokers from industry/business will find
this book useful for designing optimal strategies to enhance their
firm's productivity.
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