246
F. Firouzi et al.
generally shaped by the human brain’s data processing patterns. Data is subject
to several nonlinear transformations through virtual neurons in order to generate
a specific output. The output from one step becomes the input for another, and
this process continues until a final output is achieved. The details of DL will be
discussed in Sect. 5.6.
• Data Science: The term “Data Science” was born in the 1960s when it was
used as an interchangeable name for computer science. Today, the phrase “data
science” carries a very different meaning. Jeff Hammerbacher and D.J. Patil
took the term in a new direction in 2008, when they became the first to refer
to themselves as “data scientists” when describing their positions in Facebook
and LinkedIn, respectively. Today, data science refers to a set of methods or
techniques used to extract insights or information from data. While it intersects
with AI, data science is not a subarea of AI or ML. It is a multidisciplinary field
utilizing skills from a variety of areas, including visualization, statistics, and
machine learning, to manipulate and analyze data, generate insights, or extract
needed information from large amounts of data. In contrast, machine learning
focuses on building programs and algorithms that learn independently and do not
require human intervention to improve. For example, ML techniques are more
appropriate than data science methods when it comes to realizing self-driving
cars.
• Data Mining: Data mining became a widely used term in the database communities in the 1990s and is a subprocess of Knowledge Discovery in Databases
(KDD), the process of gaining knowledge from information found in databases.
Data mining is focused on recognizing patterns within a set of data and often
requires analysis of massive amounts of historical data that was previously
ignored or thought useless. These patterns are then used to predict future patterns,
which is an important step in the KDD process. In contrast, data science is a
broader field that includes various subareas from data visualization, big data
analytics, and predictive modeling to data mining, statistics mathematics, and
data visualization. The main differences between data science and data mining
can be clarified with an example. If you wanted to review the previous 8 years’
data in order to know how many sweets were sold during the festival seasons of
three different cities, a data mining professional would review the historical data
in legacy systems and use algorithms to extract patterns. On the other hand, if
you need to know which of the sweets received the most positive reviews, the
required data may not be located only in databases. This information could be
spread across social media, customer surveys, or websites, requiring the skill of
a data scientist.
• Data Engineering: The responsibilities and skills of data scientists and data
engineers overlap significantly; however, the main point of difference is the
specific focus of each. Data engineers focus primarily on creating data architecture or infrastructure. They develop, build, test, and maintain architectures
F. Firouzi et al.
generally shaped by the human brain’s data processing patterns. Data is subject
to several nonlinear transformations through virtual neurons in order to generate
a specific output. The output from one step becomes the input for another, and
this process continues until a final output is achieved. The details of DL will be
discussed in Sect. 5.6.
• Data Science: The term “Data Science” was born in the 1960s when it was
used as an interchangeable name for computer science. Today, the phrase “data
science” carries a very different meaning. Jeff Hammerbacher and D.J. Patil
took the term in a new direction in 2008, when they became the first to refer
to themselves as “data scientists” when describing their positions in Facebook
and LinkedIn, respectively. Today, data science refers to a set of methods or
techniques used to extract insights or information from data. While it intersects
with AI, data science is not a subarea of AI or ML. It is a multidisciplinary field
utilizing skills from a variety of areas, including visualization, statistics, and
machine learning, to manipulate and analyze data, generate insights, or extract
needed information from large amounts of data. In contrast, machine learning
focuses on building programs and algorithms that learn independently and do not
require human intervention to improve. For example, ML techniques are more
appropriate than data science methods when it comes to realizing self-driving
cars.
• Data Mining: Data mining became a widely used term in the database communities in the 1990s and is a subprocess of Knowledge Discovery in Databases
(KDD), the process of gaining knowledge from information found in databases.
Data mining is focused on recognizing patterns within a set of data and often
requires analysis of massive amounts of historical data that was previously
ignored or thought useless. These patterns are then used to predict future patterns,
which is an important step in the KDD process. In contrast, data science is a
broader field that includes various subareas from data visualization, big data
analytics, and predictive modeling to data mining, statistics mathematics, and
data visualization. The main differences between data science and data mining
can be clarified with an example. If you wanted to review the previous 8 years’
data in order to know how many sweets were sold during the festival seasons of
three different cities, a data mining professional would review the historical data
in legacy systems and use algorithms to extract patterns. On the other hand, if
you need to know which of the sweets received the most positive reviews, the
required data may not be located only in databases. This information could be
spread across social media, customer surveys, or websites, requiring the skill of
a data scientist.
• Data Engineering: The responsibilities and skills of data scientists and data
engineers overlap significantly; however, the main point of difference is the
specific focus of each. Data engineers focus primarily on creating data architecture or infrastructure. They develop, build, test, and maintain architectures