DATA SCIENCE.

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DATA SCIENCE
 DEFINITION:
Data Science is the study that concerns the retrieval and analysis of data sets, intend-
ing to identify information and correspondences hidden in the unprocessed data, defined
as raw. Data Science, in other words, is the science that combines programming skills
and mathematical and statistical knowledge to extract meaningful information from data.

Data Science consists of the application of machine learning algorithms to numerical,

textual data, images, video, and audio content. The algorithms, therefore, perform spe-
cific tasks that concern the extraction, cleaning, and processing of data, generating in
turn, data that are transformed into real value for each organization.

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 Are Data Science and Business Analytics the same?

Often the terms Data Science and Business Analytics are considered synonymous. After
all, both the Business Analytics and Data Science activities deal with the data, their ac-
quisition, and the development of models and information processing.

What then is the difference between Data Science and Business Analytics? As the name
suggests, Business Analytics is focused on the processing of data, business or sectorial,
to extract information useful to the company, focused on its market and on that of its
competitors. 

Data Science instead responds to questions about the influence of customer behavior on
the company's business results. Data Science combines the potential of data with the
creation of algorithms and the use of technology to answer a series of questions. Re-
cently the functions of machine learning and artificial intelligence have evolved and will
bring data science to levels that are still difficult to imagine. Business Analytics, on the
other hand, continues to be a form of business data analysis with statistical concepts to
obtain solutions and in-depth analysis by relating past data to those relating to the
present.

 Why use Data Science?

The Data Science aims to identify the most significant datasets to answer the questions
asked by the companies, elaborate them to extract new data related to behaviors, needs,
and trends that are the basis of the data-driven decisions of their managers.

The data thus identified can help an organization contain costs, increase efficiency, rec-
ognize new market opportunities and increase competitive advantage.

Can the data produce other useful data? Of course yes! Data Science was created to un-
derstand the data and their relationships, analyze them, but above all to extract value
and ensure that, properly interrogated and correlated, they generate information that is
useful not only to understand the phenomena but above all to orient them.

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Data Science is indispensable for companies dealing with digital transformation because it
allows them to direct their products or services towards the customer, their purchasing be-
havior and respond to their needs. Leading companies in the global market, such as Netflix,
Amazon, and Spotify use applications developed by Data Scientists. Thanks to artificial in-
telligence, allow creating recommendation engines that suggest what to buy, what to listen
to and which films to see based on the tastes of the individual user. These algorithms are
also able to evaluate what were the suggestions that did not affect the user's interest thanks
to the machine learning process, which allows refining the proposals more and more and
thus increase conversions and optimizing the ROI.

 The Data Science process

Data Science is mainly used to provide forecasts and trends. It also used to make deci-
sions using tools for predictive analysis, prescriptive analysis, and machine learning.

1) Predictive causal analysis

If the data analysis has the purpose of obtaining a prediction that a certain event will oc-
cur in the future, it is necessary to apply the predictive causal analysis. Suppose that a
bank that provides loans wants to predict the likelihood that customers will repay the loan
in the future. In this case, Data Science uses a model that can perform predictive analy-
sis on the customer's payment history to predict whether future payments will be properly
received.

2) Prescription analysis

On the other hand, if you want to create a model or pattern that applies AI to make deci-
sions autonomously and can constantly update with dynamic self-learning functions, it is
certainly necessary to create a prescriptive analysis model. This relatively recent area of
Data Science consists of providing advice or directly assuming consequent behavior.

In other words, this model is not only able to predict but suggests or applies a series of
prescribed actions. The best example of this is the self-driving car: the data collected by
the vehicles are used to optimize the software that drives the car without human inter-
vention. The model will be able to make decisions independently, establishing when to
turn, which path to take, when to slow down or break decisively.

3) Machine learning to make predictions

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If you have, for example, transactional data from a credit card company and you need to
build a model to determine the future trend, you need to use machine learning algorithms
through supervised learning. It is called supervised because the data based on which the
algorithm can be trained is already available. An example could be the continuous opti-
mization of the voice recognition of Alexa or Google voice assistants.

 The main phases of the Data Science process

The concrete application of Data Science involves a series of sequential phases, now
codified in a sort of process.

1. Knowledge and analysis of the problem

Before starting an analysis project, it is essential to understand the objectives, the con-
text of reference, the priorities and the budget available. In this phase the Data Scientist
must identify the needs of those who commission the analysis, the questions to which
the project must respond, the data sets already available and those to be found to make
the analysis work more effective. Finally, it is necessary to formulate the initial hypothe-
ses, in a research framework open to the answers generated by relating the data, whose
combinations can reserve surprises

2. Data preparation

In this phase, the data coming from various sources, generally inhomogeneous, are ex-
tracted and cleaning is performed to transform them into elements that can be analyzed.
In this phase, an analytical sandbox is needed in which it is possible to perform analyzes
for the entire duration of the project. Often we use models in R language to clean, trans-
form and display data. This will help identify outliers and establish a relationship between
the variables. Once the data has been cleaned and prepared, it is now possible to per-
form the data analysis activity by entering them in a data warehouse

3. Model planning

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We then proceed to determine the methods and techniques for identifying the relation-
ships between the variables. These relationships will be the basis of the algorithms that
will be implemented for that function. In this phase, we use R, which has a complete set
of modeling features and provides a good environment for the construction of interpreta-
tive models. SQL analysis services that perform processing using data mining functions
and basic predictive models are also useful. Although there are many tools on the mar-
ket, R is the most used programming language for these activities.

4) The realization of the model

After investigating the nature of the data available and designing the algorithms to be
used, it is time to apply the model. This is tested with data sets specifically identified and
made available for self-learning of the algorithm. We will evaluate if the existing tools will
be sufficient for the execution of the models or we will need a more structured elabora-
tion, then we move on to the optimization of the model and the elaboration is launched.

5. Communicating the results

Here is the moment in which the Data Science activity is called to make the relationships
identified between the data and the answers to the questions envisaged in the project
understandable. In this phase, we reach the objective of the analysis. It is, therefore,
necessary to elaborate one or more reports, destined to the managers of the various
business functions, making the data emerged from the data science process easily un-
derstandable, adopting elements of graphic display, such as infographics and graphics.
The text will be understandable even to those who do not have too much experience with
data and will simplify their interpretation. It is also useful for those who are involved in
product design, marketing management like top managers, who can make data-driven
decisions based on data.

 LIST OF SOME COUNTRIES WITH GREATEST OPPORTUNITIES FOR DATA

SCIENTIST:

UNITED STATES UNITED KINGDOM SOUTH AFRICA CHINA

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EUROPE CANADA INDIA JAPAN

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 CONCLUSION:

Data Science is revolutionizing in many sectors. It is just all

about to know your client, analyzing his behavior by identifying
relationships between data that can turn into predictive results re-
garding market trends and orientations. Today we are at an early
stage, which already allows us to obtain results, but through the
development of the IoT, sensors and other tools for data collec-
tion will be possible developments now only imaginable. Data sci-
ence is relatively a new concept for students and schools. As technology ad-
vances, there is demand for experts in education, business, accounting, gov-
ernment, engineering, health care, and the energy sector. This has enhanced
demand in this area which has a low supply of expertise. You are guaranteed
a sacksful career path as a data scientist with not only a great salary but also
help solve global challenges.

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