Edgar Radjabli Presents Information About Data Analytics
Companies collect a lot of data every time and most of the data is usually useless in its raw form. Data needs to be analyzed in order to make it useful and this is what is referred to as data analytics. The insight that is gathered from data through analytics is used in making smarter business decisions in order to drive success. The analysis is done by professionals known as data analysts. What data analysts do is that they extract the raw data and then organize it before they analyze and transform it to intelligible or actionable information. If that is that data analytics is all about, how does it differ from data science? Let us take a look at what Edgar Radjabli would tell you.
What’s the difference between data analytics and data science?
If you have been in the field of data analytics for some time, you must have noticed that people tend to use the terms “data science” and “data analytics” interchangeably. It is even possible to come across people who will try to convince you that the two terms mean the same thing. Well, that couldn’t be any further from the truth because data analytics and data science are two different fields and if you choose either of them, you will end up on a very different career path. In fact, the impact that these two fields have on an organization is very different.
The main differences between data science and data analytics lies in what professionals in this field do with the data they have and the results they achieve. Data analysts work to answer specific questions and/or address challenges that the business already knows exist. They achieve this by examining large amounts of data and then visualizing the results they obtain through dashboards, graphs, and charts among other data visualizations techniques.
On the other hand, data scientists consider the questions that businesses need to be asking and the challenges they should be tackling but aren’t yet. The work that these scientists do involves designing processes that are used to model data, running custom analyses, devising predictive models, and writing algorithms. From this explanation, it is clear that data analysts work with problems that are already known, while data scientists find the problems themselves.
What are the different types of data analysis?
Data analytics can be classified into different types depending on what the results of the analytics is used to do. If the result is meant for providing a description of the data, it is referred to as descriptive analytics. Analytics that is meant to find problems is referred to as diagnostic analytics. Data analytics that is meant to predict outcomes of certain events is referred to as predictive analytics and lastly, data analytics that is meant to provide a solution to a certain situation is referred to as prescriptive analytics. Each type of data analytics is useful in different situations and as a data analyst, it is important to know which one will be the best in the situation you want to apply it. Data analysts are also required to be proficient in all the different forms of data analytics.