Data is the main tool of data science technology, as the name of this technology is based on this word. Today’s world is a world of information production. They come in many forms such as temporal data, sensor signals, images, tags, text, and more.
Data is a set of values in the form of qualitative or quantitative variables that are sometimes in the form of raw and unorganized facts that need to be processed and are sometimes available in a ready and structured form. Using much software such as SQL, which are data programming languages, they can be turned into the structure needed to process and extract information from them. Data can be something simple and seemingly random and useless as long as it is organized.
Statistics works on data!
One of the main concepts of data science is gaining insight from data. Statistics is a great tool for opening up such insights into it. Statistics is a kind of mathematics and it contains formulas, but even if you have never encountered it before, you do not need to be afraid because it is not so scary that some people complicate it with their eyes 🙂
Machine learning is derived from statistics. The algorithms and models used in machine learning all come from what is called statistical learning. Knowing some basic statistical techniques and models is very useful, whether you have in-depth knowledge of machine learning algorithms or just be up to date on the latest machine learning research.
Even if you do not want to get so caught up in machine learning, these principles will put you on the right track to exploring and extracting meaning from your information through data science. Doing this alone can make your business better by knowing if the dataset is statistically significant.
Knowing the types of data will definitely come in handy!
The figure above gives us a good mental picture of the introduction of data types. As you can see, there are two general types of it, qualitative and quantitative. Its qualitative type includes text, charts, maps, and other items, and its quantitative type includes two main numerical and classified variables.
The numerical type can be divided into continuous or discrete values, and the categorized type can be divided into nominal and sequential values. In the following, you will see examples of them, dear ones.
The first image gives examples of nominal numerical data and the second gives an example of the ordinal numbers. In the discrete numerical type, we can count them but we cannot measure, like the number of loaves we can count but we cannot measure. In the case of continuous numbers, they can be measured but not counted, such as temperature, which can be measured and given a numerical value for it, but we can not count the temperature.
Data is the basis of statistical techniques that are the model of machine learning and ultimately data science technology. There are different types of it that can be structured and processed, and knowing them well can help us make the right decisions. I sincerely welcome your comments and suggestions 🙂
An interested and active person in the field of data science and molecular dynamics simulation