Data is constantly being pulled from many different channels and platforms, including mobile phones, social media apps, e-commerce sites and internet searches. The huge increase in the amount of data available has paved the way for the creation of a new working model based on big data to create better operational tools in all sectors. This can be called data science for short.

In recent years, the ability to continuously access data has become possible thanks to advances in technology and data collection techniques. Through the data obtained today, people's buying patterns can be easily tracked and various predictions can be made based on the information collected. However, data needs to be decomposed in order to structure the ever-increasing data set and create effective decision-making processes. The whole process involved also requires taking advantage of the discipline of data science. So, what is data science and what are the techniques used in data science? In this article you can learn curious details about data science.

What is Data Science?

Data science is about gaining actionable insights into the data a company owns, supported by a variety of applications. Data science itself combines the study of mathematics and statistics, specialized programming languages, advanced analytics, artificial intelligence (AI) and machine learning. With all these helpful tools, the resulting data can be used to provide meaningful insights for companies, support decision-making and strategic planning processes.

What Are the Prerequisites for Data Science?

Before examining how the concept of data science is applied, it is necessary to address some terms that need to be known for data science. Knowledge in the field of data science is as follows:

• Machine Learning: Machine learning forms the basis of data science. It is important that data science teams have a good amount of machine learning training in addition to statistical infrastructure.
• Modeling: Mathematical models allow making quick calculations and predictions based on general inferences about the data. Modeling is also part of machine learning and involves determining which algorithm is suitable for solving a particular problem, how these models should be addressed.
• Databases: A good data science analyst should know how systems operate databases, how to derive meaningful results from them, and manage the process correctly.

• Statistics: Statistical science is often used in data science interpretation techniques. Accurately handling statistics can help gain more information and draw more meaningful conclusions.
• Programming: A prerequisite for running a good data science project requires having a sufficient level of programming knowledge. The most preferred languages in the field of programming are Python and R Python. The reason these languages are so common is that they can be easily learned by data scientists.

How is Data Science Applied?

Data science has a series of processes such as obtaining various sets of information, processing them, reaching qualified conclusions, and interpreting information in management processes. So multiple techniques and disciplines have a say in the emergence of data science. Among the disciplines closely related to data science, branches such as programming, machine learning and statistics are in the first place.

Data mining resorts to a variety of algorithms to extract useful data from the dataset. Statistical measurements or predictive analytical tools use data mining results to measure events likely to occur in the future based on the past state of the data. Machine learning is, in short, AI tools that process large amounts of data that humans cannot analyze. Machine learning aims to perfect the predictive analytical decision model by matching the probability of an event happening again that actually happens. Using said data science applications, data analysts collect and process structured data in the machine learning phase by resorting to algorithms. Analysts translate, transform, and summarize data into a coherent language that the decision maker can understand.

What are Data Science Techniques?

Data science techniques encompass the process of obtaining support from some computing systems to facilitate the analysis of data. In order to contribute to the relevant process, it is necessary to make use of certain data science techniques. The techniques in question can be listed as follows:

Classification

This technique involves the division of data into several groups. By means of computers, the data is determined and divided into classes according to some points. With the classification method, the products/services owned by companies are sorted according to various factors such as customer demand and cost. In addition, through the relevant method, customer feedback, social media comments can also be divided into specific categories, positive or negative. On the other hand, the classification method is also among the tools used in insurance and loan applications to identify risk ratios and divide them into various branches.

Regression

Thanks to regression, one of the data science techniques, a meaningful set of data can be obtained with two data that do not correlate between each other. Modelling can be constructed between two seemingly unrelated data through various mathematical formulas. Depending on this, relevant graphical tables and curves may appear. The regression technique is preferred to predict the effect of the other data when one of the data is valued. For example, organizations can achieve meaningful results in the relationship between customer satisfaction and employee quality using the regression technique.

Clustering

In the clustering method, the latest technique of data science, the relevant data is divided into groups and clustered to detect normal or abnormal points on the data. Unlike the classification method, the method in question does not simply divide the data into fixed arms. With the clustering technique, the relationships of the data at the highest level are taken into account and the classification is carried out on it. Thanks to the relevant technique, it becomes possible to discover new relationships between data and obtain different results. For example, firms can categorize and make meaningful inferences about the behavior of customers with similar attitudes to improve the customer experience.

Who Supervises the Data Science Process?

Data science is one of the processes that must be managed precisely for businesses. Every organization needs a variety of professionals in this field for the sake of security and efficient use of its data. Responsible people within the company in data science management can be listed as follows:

Business Managers

Business managers are the people responsible for overseeing data science trainings within the company. The primary responsibilities of business owners are to collaborate with the data science team to identify potential problems and create an analytical method for this. The professional data scientist assigned by business managers can oversee marketing, finance, or sales departments and report to an executive in charge of the department. The business manager, on the other hand, has the responsibility to manage all these processes and use the data obtained for the benefit of the company.

IT Managers

A sub-position of business managers in data science includes IT managers. The information technology manager is the person responsible for developing the infrastructure and architecture necessary to take data science activities to the next level. The IT manager ensures that they are working efficiently by constantly supervising data science teams, providing resources to teams for data science if needed. In addition, IT professionals are also responsible for creating secure environments for data science teams.

Data Science Managers

Data science managers, on the other hand, are located at the bottom of the hierarchy. Administrators in charge of data science primarily monitor and supervise the working procedures of all data science team members. They are also responsible for managing the day-to-day activities of the data science team. Data science managers are team founders who integrate the project planning and auditing phases necessary for the company with workforce growth.

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