# Data Science

#### 1. What is Data Science?

Data science is the discipline that derives knowledge and insights by performing analysis from a data, including both structured and unstructured data. Data scientist is a position that performs data science tasks. From a wider perspective, however, data scientists perform variety of tasks in multiple areas and thus is difficult to define them. Structured data refers to data that is structured in a specific format. Most structured data can be calculated. It is easier to understand if you imagine the rows and columns with numerical data in an Excel file. Unstructured data refers to data that does not have a fixed frame or shape, such as texts, audios, images or videos. Most unstructured data cannot be computed. In recent years, the value of unstructured data analysis is skyrocketing.There is also semi-structured data that has mixed features of the two data types. Semi-structured data refers to data that cannot be operated on, such as XML, HTML, or JSON, but has a specific structure.

#### 2. Components of Data Science

##### 2.1 IT and Computer Science

To analyze data, you need to be proficient in processing data. You need to have a high level of understanding about algorithm and modeling structures. Specifically, the skills required for data analysis are:
- Programming language (Python, C++)
- Statistics programs (R, STATA)
- RDBMS programs (SQL)
- AlgebraXaaS (SaaS, PaaS, laaS)

##### 2.2 Mathematics and Statistics

However, data science is not exactly computer science. In order to understand data science and analyze data, a variety of mathematical and statistical knowledge, including algebra and linear algebra, is also necessary. Math and statistics can be called the basics of data science and machine learning. Solutions to various business problems involve building analytic models based on complex mathematics, and therefore it is easy to see many data scientists come from computer science, applied mathematics, statistics, and economics backgrounds. Important mathematical and statistical knowledge required for data analysis are:
- Machine learning and deep learning
- Statistical modeling methodology
- Bayesian reasoning

##### 2.3 Field expertise

Data scientist needs more than computer science and mathematical statistical abilities. The way how data science is used and applied in different industries are different. As a result, data scientists also need to have competency as business consultants in their field. Business insights are as important as insight into a system or algorithm. The ultimate value of data analysis does not come from the data or the technology itself. Rather, it is a result coming from the combination of all the skills and abilities mentioned above. Specifically, the skills required for data analysis include:
- Interest in various business industries
- Understanding of data
- Problem solving
- Strategic and creative thinking
- Active and cooperative attitude

#### 3. Data Science with CLICK AI

Click AI can generate artificial intelligence by processing both structured and unstructured data. All of the complex computer and mathematical statistical steps are taken care of by Click AI. Users can analyze data, create models, and visualize without having to write or run codes. Click AI offers simple and easy process for users to turn business data into real-life values.