Data science is a mixture of mathematics and statistics, machine learning, domain knowledge, Information technology, and software development. |

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Importance of Data
Science| How Data Science Works: Steps of Data Science Process

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What is Data Science?

**Data science**is a field of Big Data designed to provide meaningful information based on large volumes of complex data. Data science combines various fields of work in mathematical, statistical and computational tasks to interpret data for decision-making purposes.

Data is drawn from
various fields and platforms, including social media, phones data, e-commerce
sites, healthcare surveys, Internet search, etc. Increasing the number of
available data opens the door to develop a new field of study called Big Data -
or extremely large data sets that can help produce better operational tools in
all areas. Easy access to continuously growing sets and data is possible
in collaboration with financial technology companies, who use technology to
innovate and enhance traditional financial products and services. The data
produced creates more data for emerging financial technology products such
as cloud computing and storage that are easily shared across all
entities. However, the interpretation of vast amounts of unstructured data
for effective decision making can prove to be very complex and time-consuming
for companies. To deal with such inconveniences, data science has emerged
in the world.

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What is a Data Scientist?

A data scientist is
someone who collects large volumes of data, analyzes them, and interprets to help a
company increase its sales and improve its operations. Certified professionals in data science develop
advanced analytical tools and statistical models to analyze data and use various findings and results to find trends, patterns, and relationships in data sets. This information can be used to calculate the repeat purchase rate and predict
consumer behavior while identifying operational risks and meeting the challenges faced by small businesses. A data scientist is often a storyteller who presents data insights to people in an organization or institution in a simple and effective way.

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Subsets of Data Science

Data science is a mixture of artificial intelligence, machine learning, deep learning, mathematics and statistics, domain knowledge,
Information technology, and software development.

Everything from an
analysis of core data to mathematics and statistics to the model building is
the basics of data science, which requires dealing with probability, numerical, vectors and more.

Machine learning can be divided into artificial intelligence and deep learning. Actually, machine learning is a subset of artificial intelligence and is a model building
subset of data science. Additionally, the necessary software development and information technology skills are considered essential to apply machine learning in those areas.

Finally, basic knowledge of business domains may take a long time to determine the accuracy of results as different businesses use different data analytics for prediction and
verify the reliability of the outputs using the correct metrics and right data.

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How Data Science Works?

Data science involves
tools and technologies, ranging from multiple disciplines to large collected
data sets. These tools derive insights, extract meaningful data from data sets,
and interpret that data for decision-making purposes. Disciplinary fields that
make up the data science field include mining, statistics, analysis, machine
learning, and some programming.

Data mining applies algorithms to complex
data sets to reveal patterns that are used to extract usable and relevant data
from the sets. Statistical measures such as predictive analysis use this
extracted data to measure events based on future events. Machine learning
is an artificial intelligence device that processes large amounts of data that
humans will be unable to process over a lifetime. Machine learning affects
the decision model presented under the approximate analysis by matching the
probability of the event to what actually happened at the estimated time.

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Steps of Data Science Process

Data science process
will be divided into the following categories.

**1. Exploratory Data Analysis:**It is said that 90% of the work done by a data scientist is related to data analysis. The term "data analysis" refers to the cleaning and pre-processing of data before the construction of a statistical model. This step includes outliers, duplicate data, null values , and many other anomalies, which do not fall within the data agreement required for business purposes.

**2. Understanding the problem:**It is imperative that the details of the problem are clear before you dive into the actual implementation part. It is important to find out what is right to get the right data and get the right solution.

**3. Getting the right data:**Once the problem is understood, it is mandatory to get the right data to perform the operation.

**4. Using the correct metrics:**Depending on the business domain, the metric that will determine the completeness of a model should be selected.

**5. Data visualization:**Data visualization is a general term which refers to a graphical representation of information and data using visual elements such as graphs, charts, and maps. Data visualization tools help people understand the importance of data and provide an accessible way to see the trends, patterns, and relationships in data.

Once data has been cleaned and processed in advance, it is necessary to visualize the data to determine the correct features or columns to use in the statistical model.

**6. Model Selection:**The selection of the correct model is necessary for a particular problem statement because each model may not fit perfectly to each data set.

**7. Hierarchical Encoding:**This step of the data science process is applicable for instances where input attributes are explicit and need to be converted into numbers used in the model because the machine cannot function properly with some ranges.

**8. Communication:**businessmen, salesmen or shareholders, usually do not understand the technical knowledge of data science, and therefore it is necessary for their business to communicate the findings, products, and services to their customers in simple terms, which can then come up with measures to alleviate any potential risk.

**9. Deployment:**Sometimes, the word "implementation" is used to mean the same thing. Once the statistical model is built, and the business domain is satisfied with the findings and results, this model can be deployed and implemented to build analytical tools and improve business efficiency.

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Benefits of Data Science

##### Importance of Data Science:

⇒ Data Science
and Big Data are very important to help improve the company's operations in the
future. Data science is very beneficial for better marketing forecasting.

⇒Data science can help
reduce the constraints of time and budget allocation and help in the
development of business.
⇒Data science has
determined the results of many manual tasks that could be superior to human
influences.

⇒Data science helps
prevent a debt default, is used in fraud detection, and many other cases in the
financial domain.

⇒Data science generates
insights from raw, unstructured text data.

⇒Data science helps
predict future results that can prevent the financial losses of many large
corporations.

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Breathtaking Applications of Data Science

⇢Data science has helped
bring the financial industry into the tech-savvy era. Through the use of data
science, companies are using big data to derive value from their consumers.

⇢Asset management firms
are using big data to predict the probability of data security going up or down
in the future.

⇢Banking institutions are
capitalizing on big data to enhance their fraud detection success.

⇢Netflix—An American
media-services provider and production company—uses data science and analytics
to determine what its users are interested in and uses this information to
decide on the creation and host of TV shows. The company also uses existing
algorithms to create personalized recommendations on what to watch based on the
user's viewing history.

⇢Under analytics, the
data analyst collects and processes structured data from the machine learning
phase using algorithms. He interprets the data into a cohesive language,
converts, and summarizes what the decision-making team can understand. As
the role of a data scientist is better understood, more skill sets will be
added to the field that includes areas such as data architecture, data
engineering, and data administrators.

**Read more:**

What Do Data Scientists Do and How to Become a Data Scientist?

Applications of Data Science in Finance and Business Analytics

Importance of Data Science| How Data Science Works: Steps of Data Science Process
Reviewed by The Scientific World
on
September 21, 2019
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