Data is the first piece of information needed to reach a discussion conclusion in a research. Data can be obtained by measurement, counting, experimentation, observation or research. Data collected by measurement or counting and reporting a numerical value are quantitative data, data that do not report a numerical value are qualitative data.
Who is a data scientist? It is a discipline that combines domain knowledge, programming abilities, understanding of mathematics and statistics to extract useful insights from data. It combines multiple fields, including artificial intelligence and data analytics, to extract value from data. It covers every topic related to data. Today, this branch of science has become more popular with the adaptation of concepts such as machine learning, case analysis, artificial intelligence to industry and technology. People who deal with this branch are called data scientists.
What Does a Data Scientist Do? A data scientist is a person who can describe the data and make inferences with various tools. The data scientist knows where to get the data, otherwise he produces it. Knows the structure of the data, what it means, what kind of deficiencies it has. Knows where and when to apply all kinds of descriptive, inferential, clustering, classificatory, predictive, predictive and preventive approaches.
What Skills Should a Data Scientist Have? Programming; Mathematics, Statistics, Probability, Linear Algebra; Machine Learning; Deep Learning; Statistical Learning; Asking the right questions; problem solving ability; Effective communication; Presentation Ability; Analytical Perspective.
What is the difference between Data Science and Artificial Intelligence and Machine Learning? In order to better understand and actively use this branch of science, it is very important to know other terms in this field and to understand the difference between them. These terms are often used interchangeably in everyday life. However, there is quite a big difference between them. To summarize these three terms: -Artificial Intelligence: It can be defined as the ability of a computer or a computer-controlled robot to perform various activities similar to intelligent creatures. -Data Science: It is a subset of artificial intelligence. Extracting meaningful information from data, using scientific methods to extract value from data and gaining insight. -Machine Learning: It is the other subset of artificial intelligence. It focuses on the development of computer programs that can access data and use it for themselves.
How Is Data Science Done? Project planning: This is an important step to understand whether the project is necessary. It provides the right management of cost and time. The right approach Analysis of data Sharing with the team: All data should be shared with the team in order to analyze the data correctly and receive feedback. Uploading the data to the system: The data must be transferred to the computer completely. Visualizing data Model development Testing the model Model monitoring: The model should be monitored continuously.
Who Controls the Data Science Process? IT managers: Responsible for the infrastructure to support data science operations. Data science is working closely with IT managers to keep the project going. Business managers: They can be marketing, sales or finance department managers. There is a data science team working with these managers. They strategize to identify the problem and produce a solution to the problem. Data science managers: These managers oversee the data science team. Systematically monitors the development and workflow of the project.
Who Should Be on the Data Science Team? Data Engineer Data Analyst Machine Learning Engineer Data Visualization Developer Data Translator Data Architect
Why is Data Science Important? Data science plays an important role in nearly all aspects of business operations and strategies. For example, it provides information about customers, which helps companies create stronger marketing campaigns and targeted advertising to increase product sales. It helps manage financial risks, detect fraudulent transactions, and prevent equipment failures in manufacturing facilities and other industrial environments. It helps prevent cyber attacks and other security threats on IT systems. From an operational perspective, data science initiatives can optimize the management of supply chains, product inventories, distribution networks, and customer service. At a more fundamental level, they point the way to increased productivity and reduced costs. It also enables companies to create business plans and strategies based on informed analysis of customer behavior, market trends and competition. Without it, businesses can miss opportunities and make erroneous decisions.