Data science is a “concept for unifying statistics, data analysis, machine learning and its related methods” to “understand and analyze real phenomena” with data. It uses techniques and theories drawn from many fields within the context of mathematics, statistics, information science and computer science.
Understanding Data Science
Regardless of the assertion of the problem, the data scientist performs a course of action set most of the time to get a better solution to the problem.
Understand the problem
Learn about the problem in the field, ask the right questions, which are the focus of what a data scientist does, and form the basis for later steps in the role of the data scientist. Define the problem and make it a concrete framework in which you can work.
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Collect enough data
As the name implies, Data Scientist needs to gather enough data to understand the problem in question and gain better control over the time, money, and resources needed to make the process successful.
Process raw data
Data can rarely be used in its original form. It must be rendered and there are several methods for converting it into a usable format. This is an essential part of every data scientist’s work routine and consumes a large part of your time and resources.
Explore the data
Once the data has been processed and converted into a form that can be used for later stages, you should explore them further to get the characteristics of the data and get more information about the obvious trends, correlation, and not-so Relationships hidden, and more.
Analyze the data
This is where the magic happens. The data scientist deploys the various arsenals in his repositories such as automatic learning, statistics and probability, linear and logistic regression, time series analysis, and more to make sense of the data. At the end of this step, the data scientist can gain valuable business information, such as forecasts, business process optimization, and find new ways to do the same old things, among other things.
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Communicate the results
At the end of the whole process, it is necessary to communicate the findings to the right stakeholders so that the bases for the action to be taken and the deployment of the decisions are taken.
Benefits of Data Science
The main advantage of having data science in an organization is empowerment and facilitation of decision making. Organizations with data scientists can take into account quantifiable and data-based evidence in their business decisions. These data-driven decisions can ultimately lead to greater profitability and improved operational efficiency, business performance, and workflows. In customer-oriented organizations, data science helps identify and refine target audiences. Data science can also help with recruiting: internal application processing and data-driven tests and fitness games can help an organization’s human resources team make faster and more accurate selections during the hiring process.
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The specific benefits of data science vary according to the purpose of the company and the industry. Sales and marketing departments, for example, can extract customer data to improve conversion rates or create individual marketing campaigns. Banking institutions are mining data to improve fraud detection. Broadcast services, such as Netflix mining data, to determine user interest and use that data to determine which television programs or movies to produce. Data-based algorithms are also used in Netflix to create custom recommendations based on a user’s viewing history. Shipping companies like DHL, FedEx and UPS use data science to find the best routes and delivery times, as well as the best modes of transportation for their shipments.
Data science remains an emerging field within the enterprise because identifying and analyzing large amounts of unstructured data can be very complex, expensive, and time-consuming for business.
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