Pie charts reflect the proportional representation of different categories within a whole. They are called ‘pie’ charts because of their round shape, which is divided into “slices” or sectors representing different segments. Each slice of the pie corresponds to a category from your data set, ...
Ordinal data is qualitative data for which their values have some kind of relative position. These kinds of data can be considered “in-between” qualitative and quantitative data. The ordinal data only shows the sequences and cannot use for statistical analysis. Compared to nominal data, ordinal...
Data analysis in research is an illustrative method of applying the right statistical or logical technique so that the raw data makes sense.
They facilitate global work.Any permitted user can access the data management tools from any device and location, and continue to work under any circumstance. This centralized functionality of data management programs is perfectly suited to the needs of working remotely and synchronizing international te...
There are myriad different types of charts, graphs and other visualization techniques that can help analysts represent and relay important data. Let’s take a look at 10 of the most common ones: 1. Column Chart This is one of the most common types of data visualization tools. There’s a ...
Understanding how data is structured and stored is a critical step that occurs at the beginning of every analytics project, during requirements gathering. Both structured and unstructured data are suitable for analysis, but the tools the data team will use to ingest, transform, and ...
When we have PETA-Bytes or TERA-Bytes of data, it is hard to understand the rows and columns. The alternative way is the graphical way which is easy to understand. The terminology of the data representation in graphical form is called Data Visualization. There are more than 37 types of da...
Data extraction is the process of extracting data from a variety of sources. It is a complex and important process, as it allows us to collect the data we need to make informed decisions.
2) Quantitative Data Type Quantitative data have numerical values that's why it's countable and suitable for statistical data analysis. This data answers questions like "how much" and "how many". The price of a phone, the ram of that mobile, number of ratings of a product are examples of...
Structured Data Unstructured Data Semi-structured Data The above three types of Big Data are technically applicable at all levels of analytics. It is critical to understand the source of raw data and its treatment before analysis while working with large volumes of big data. Because there is so...