data science
Budget: $250 – $750 USD
Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms, and
systems to extract knowledge and insights from structured, semi-structured and unstructured data.
Data science is much more than simply analyzing data. It offers a range of roles and requires a
range of skills.
Let’s consider this idea by thinking about some of the data involved in buying a box of cereal
from the store or supermarket:
• Whatever your cereal preferences—teff, wheat, or burly—you prepare for the purchase by
writing “cereal” in your notebook. This planned purchase is a piece of data though it is
written by pencil that you can read.
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• When you get to the store, you use your data as a reminder to grab the item and put it in
your cart. At the checkout line, the cashier scans the barcode on your container, and the
cash register logs the price. Back in the warehouse, a computer tells the stock manager that
it is time to request another order from the distributor because your purchase was one of
the last boxes in the store.
• You also have a coupon for your big box, and the cashier scans that, giving you a
predetermined discount. At the end of the week, a report of all the scanned manufacturer
coupons gets uploaded to the cereal company so they can issue a reimbursement to the
grocery store for all of the coupon discounts they have handed out to customers. Finally,
at the end of the month, a store manager looks at a colorful collection of pie charts showing
all the different kinds of cereal that were sold and, on the basis of strong sales of cereals,
decides to offer more varieties of these on the store’s limited shelf space next month.
• So, the small piece of information that began as a scribble on your notebook ended up in
many different places, most notably on the desk of a manager as an aid to decision making.
On the trip from your pencil to the manager’s desk, the data went through many
transformations. In addition to the computers where the data might have stopped by or
stayed on for the long term, lots of other pieces of hardware—such as the barcode
scanner—were involved in collecting, manipulating, transmitting, and storing the data. In
addition, many different pieces of software were used to organize, aggregate, visualize, and
present the data. Finally, many different human systems were involved in working with the
data. People decided which systems to buy and install, who should get access to what kinds
of data, and what would happen to the data after its immediate purpose was fulfilled.
As an academic discipline and profession, data science continues to evolve as one of the most
promising and in-demand career paths for skilled professionals. Today, successful data
professionals understand that they must advance past the traditional skills of analyzing large
amounts of data, data mining, and programming skills. In order to uncover useful intelligence for
their organizations, data scientists must master the full spectrum of the data science life cycle and
possess a level of flexibility and understanding to maximize returns at each phase of the process.
Data scientists need to be curious and result-oriented, with exceptional industry-specific
knowledge and communication skills that allow them to explain highly technical results to their
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non-technical counterparts. They possess a strong quantitative background in statistics and linear
algebra as well as programming knowledge with focuses on data warehousing, mining, and
modeling to build and analyze algorithms. In this chapter, we will talk about basic definitions of
data and information, data types and representation, data value change and basic concepts of big dataData can be defined as a representation of facts, concepts, or instructions in a formalized manner,
which should be suitable for communication, interpretation, or processing, by human or electronic
machines. It can be described as unprocessed facts so simple
systems to extract knowledge and insights from structured, semi-structured and unstructured data.
Data science is much more than simply analyzing data. It offers a range of roles and requires a
range of skills.
Let’s consider this idea by thinking about some of the data involved in buying a box of cereal
from the store or supermarket:
• Whatever your cereal preferences—teff, wheat, or burly—you prepare for the purchase by
writing “cereal” in your notebook. This planned purchase is a piece of data though it is
written by pencil that you can read.
22
• When you get to the store, you use your data as a reminder to grab the item and put it in
your cart. At the checkout line, the cashier scans the barcode on your container, and the
cash register logs the price. Back in the warehouse, a computer tells the stock manager that
it is time to request another order from the distributor because your purchase was one of
the last boxes in the store.
• You also have a coupon for your big box, and the cashier scans that, giving you a
predetermined discount. At the end of the week, a report of all the scanned manufacturer
coupons gets uploaded to the cereal company so they can issue a reimbursement to the
grocery store for all of the coupon discounts they have handed out to customers. Finally,
at the end of the month, a store manager looks at a colorful collection of pie charts showing
all the different kinds of cereal that were sold and, on the basis of strong sales of cereals,
decides to offer more varieties of these on the store’s limited shelf space next month.
• So, the small piece of information that began as a scribble on your notebook ended up in
many different places, most notably on the desk of a manager as an aid to decision making.
On the trip from your pencil to the manager’s desk, the data went through many
transformations. In addition to the computers where the data might have stopped by or
stayed on for the long term, lots of other pieces of hardware—such as the barcode
scanner—were involved in collecting, manipulating, transmitting, and storing the data. In
addition, many different pieces of software were used to organize, aggregate, visualize, and
present the data. Finally, many different human systems were involved in working with the
data. People decided which systems to buy and install, who should get access to what kinds
of data, and what would happen to the data after its immediate purpose was fulfilled.
As an academic discipline and profession, data science continues to evolve as one of the most
promising and in-demand career paths for skilled professionals. Today, successful data
professionals understand that they must advance past the traditional skills of analyzing large
amounts of data, data mining, and programming skills. In order to uncover useful intelligence for
their organizations, data scientists must master the full spectrum of the data science life cycle and
possess a level of flexibility and understanding to maximize returns at each phase of the process.
Data scientists need to be curious and result-oriented, with exceptional industry-specific
knowledge and communication skills that allow them to explain highly technical results to their
23
non-technical counterparts. They possess a strong quantitative background in statistics and linear
algebra as well as programming knowledge with focuses on data warehousing, mining, and
modeling to build and analyze algorithms. In this chapter, we will talk about basic definitions of
data and information, data types and representation, data value change and basic concepts of big dataData can be defined as a representation of facts, concepts, or instructions in a formalized manner,
which should be suitable for communication, interpretation, or processing, by human or electronic
machines. It can be described as unprocessed facts so simple