Numerical datasets numerical datasets are the simplest type of datasets. Because, the content is just data in the form of numbers. That is why this type of dataset is also often call quantitative data. For example, if you are collecting weight data from a group of people, you are creating a numerical dataset. Because the data you collect only contains numbers. 2. Correlation datasets as the name implies, a correlation dataset is a type of dataset that contains data that is correlat, or connect to one another. For example, suppose you are collecting sales data at a store. Then, you find that as sales of toothbrushes increase, sales of toothpaste also increase. Well, the collection of data on sales of toothbrushes and toothpaste can be classifiE into the correlation dataset, because the two are interconnect. Oh yes, the correlations generated from this dataset can also be divid into three types, namely: no correlation – the data collected has nothing to do with it. Positive correlation – the data collect has the same effect. An example is the data on sales of toothbrushes and toothpaste which are both increasing.
Negative correlation – the data collected has the opposite effect. For example, suppose sales of juice decrease if sales of milk increase. 3. Categorical dataset categorical dataset is a type of dataset that is usually used to divide a set of data into two different categories. The easiest example is gender (male or female). Or data that is an answer to a question that can be responded to by agreeing or disagreeing. 4. Bivariate datasets bivariate means two variables. So, this type of dataset describes the relationship between two variables only. For example, suppose you want to determine bonuses for each member of the Germany Business Fax List sales team. So, the bonus given will be calculated based on the following two variables: number of products sold the amount of profit earned per month well, because the two data are interconnected, the data set is included in the bivariate dataset category. 5. Multivariate datasets if a bivariate dataset contains only two variables, a multivariate dataset contains more than two variables.
For example, suppose you are collecting data related to employees. So, you store data related to age , address , telephone number , and email address . Since the dataset you created contains four different variables, it means that the dataset you created is of type multivariate. Also read: big data and its role in shaping linkedin now let’s learn the dataset in deeper! You’ve now learned some basic insights about datasets. Starting from the meaning Mobile Lead of datasets, the difference between them and data and databases, to their types. In essence, by understanding the dataset, you will better understand how the role of data is in the database, the type of data , and the relationship between the variables that you are processing. Well, dataset is one part of data science. If you want to master data science more deeply, you can join the bitlabs academy data science community on discord.