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What is data science; ‌ History, working method and its applications

Study guide

What is data science? Have you heard anything about him? Data science is a field of applied mathematics and statistics that provides useful information. This information is based on large volumes of complex data or big data. Data science or data-driven combines different dimensions of different disciplines with the help of calculations to make decisions based on available data. In the following, we will tell you more about this fascinating branch of science.

Important Notes:

  • Data science uses methods such as machine learning and artificial intelligence to extract meaningful information and predict future patterns and behaviors.
  • Advances in technology, the Internet, social networks, and the widespread use of technology have made access to big data easier and more accessible.
  • The field of data science is growing with the advancement of technology. Big data collection and analysis methods are also becoming increasingly complex.

What is data science?

Data is obtained from various sources, for example:

  • Cell phones;
  • Social Networks;
  • Commercial websites;
  • Health system surveys;
  • Searches that are done on search engines like Google.

The increase in existing data has opened a new door to studies based on big data. Big data is a collection of large and extensive data that allows us to produce better operational tools.

Access to data has also increased due to technological advances and methods of data collection. Ordinary people can make better decisions for their business by buying data about behaviors and patterns. In the field of business, the use of data and analysis and forecasting of customer behavior and behavioral economics is highly used.

Of course, the increasing growth of data requires structures that make it possible to use them and use them to make better decisions. Such a process (data structuring) is complex and time consuming for companies, so data science has emerged to take on this important task.

Summary of the history of data mining

The use of the term “data science” began in the early 1960s. In those days, the term was considered synonymous with computer science. Later, a more precise definition was introduced, which said that data science means the study of data processing methods that are used in a wide range of fields.

In 2001, William S. Cleveland first used the term data science as a distinct, independent term and term that we have defined. Harvard Business Magazine also published an article in 2012 noting that the job of data analytics is one of the most exciting jobs in the 21st century.

How is data science used?

Data Science combines tools from different disciplines. Its purpose is to collect a set of data, processes and information and gain practical insight into the data set. Extracting data and gaining valuable and meaningful information from them makes it easier to make decisions in different areas.

The disciplines and contexts that make up data science are:

  • Data mining and statistics;
  • Machine learning;
  • Statistical Analysis;
  • Programming.

1. Data extraction and statistics

Data mining means, with the help of various algorithms, to identify the patterns in a complex data set and obtain meaningful and useful data from them. Statistical measures or predictive analytics use this data to measure events that are likely to occur in the future. In fact, it is possible to predict the future based on what the data show about the past.

۲. Machine learning

Machine learning is a tool in the field of artificial intelligence that processes large amounts of data. Humans can never process and process such vast amounts of data. Machine learning complements decision-making models based on predictive analytics. This is done by matching the probability of an event in the present with an event that has already occurred.

3. Statistical analysis and programming

Analysts use statistical analysis to collect and process structured data using machine learning and various algorithms. Experts in this field interpret and summarize data in a coherent and comprehensible language for decision-making groups.

Data science is used in all fields, including architecture, engineering and data management. Obviously, in advancing all these processes, the use of programming science is also necessary.

Demand for data science experts is projected to grow by 15% from 2019 to 2029, according to forecasts. This growth is faster than any other discipline.

United States Labor Statistics Center

What do data science experts do?

Experts in the field collect, analyze, and interpret large amounts of data. Professionals in this field are involved in improving the performance of various companies and by providing models, they can analyze data, identify patterns and trends, and examine relationships in datasets.

Information from data science experts has many benefits, including:

  • Predicting customer behavior;
  • Description of the status of businesses;
  • Investigating the operational risks of different projects.

These people help decision makers in different businesses to make better decisions and take important steps to solve problems by providing appropriate information obtained from applied data.

Application of data science in various fields

Almost all scientific disciplines use data. What distinguishes data mining science is the use of sophisticated computational methods and machine learning that can use very large datasets for analysis. Sometimes the data studied in the field of data mining are so large and complex that it is not possible to use traditional methods to analyze them.

Data science can predict patterns for better decision making and future events. It does all this by examining data that is initially unstructured and sometimes even seemingly irrelevant. Businesses that believe in the science of data mining can execute various lucrative projects by receiving very useful information.

Examples of the application of data science

These days, a lot of data is obtained from different channels and networks. Assume how much unstructured and complex data each business faces to analyze its situation and that of its customers.

In such a situation, traditional tools of analysis are no longer useful and we need methods and tools with which we can obtain useful information from existing data. Data mining can analyze multiple and voluminous data with a suitable structure in such a way that it provides good information for decision making in different fields. For example:

  • Businesses can better identify customers by analyzing big data. Better customer identification also means better advertising programs and increased sales.
  • New devices and devices, such as electric vehicles, are changing the way we live our lives with the help of data science, like a vehicle that can take you to your destination without the need for a driver and with a high degree of intelligence by data mining information about its environment.
  • Receiving data and using it to extract information in the field of meteorology and space also has amazing results and can lead to predictions that are very practical and useful in life.

What are the steps of data mining?

Stages of data mining in data science

  • Initial detection and evaluation: Before starting any data science project, you should consider how you have access to the financial resources, people, and technologies needed to advance the project. Examining the problem and defining the problem to be solved with data mining is also done at this stage of the work.
  • Data preparation: The data required for each project and the work to be done with it are reviewed at this stage.
  • Planning for modeling: In this part of the work, the relationship between variables is examined to make modeling possible.
  • modeling: In modeling, data sets are created for each project. Various methods of obtaining information, such as clustering and classification are also used in this field.
  • Project operation: Before the full data mining result is applied to a project, it is used on a smaller scale and the project is operationalized.
  • Output evaluation and results: At this stage, based on the results of the data mining, it is determined what achievements, failures and successes the project will bring.

What is the risk of misuse of data science?

Using data to obtain useful information in various fields is a very interesting idea, but this science also provides the basis for the formation of some abuses. For example, in the context of social networks, there is a vast sea of ​​user data.

Some companies intervene in matters such as political elections or business activities of different companies by using users’ data. It is not very interesting to misuse users’ data without their permission and to advance political, commercial and the like.

Cambridge Analytica, for example, is one of the companies active in the field of data mining that has illegally used its ability to understand and analyze conditions in political elections.

at the end

Various companies and businesses these days are making great use of data science to improve their status. They want to use existing data related to their work to create more value for customers by gaining useful information. For example, banks and financial institutions are trying to prevent fraud by focusing on data science. Asset management companies also use data mining to estimate the value of various assets.

The growth of data science is very high and will have much more dramatic effects on our lives in the near future. Data science is also used in Iran. Of course, we have a long way to go to benefit from the extraordinary potential of this scientific trend. However, there are good specialists and experts in this field in Iran, and many companies and organizations also use data mining to advance their projects.




What is data science; ‌ History, working method and its applications

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