What is Data Analytics and then understand what exactly it is? Fundamentally when we deal with respect to any data, all that we are trying to analyze the data but exactly speaking, what data is available across the globe? The data that is available across the globe is actually the raw data. so what we are trying to do this we are trying to collect this raw data and then we are trying to analyze this raw data for various reasons. So fundamentally data analytics is the science of examining raw data with the purpose of drawing conclusions about that information. now why we are saying this as raw data; this is one important question that may arise, the data that actually we are encountering in the real world or raw facts these raw facts sometimes may not be clean or quality in nature what exactly mean by not clean or not quality , the data that is actually captured from the real world make contain lot of inconsistencies maybe in different data sources, maybe available in different data structures or formats and few of the data might be missing that is the reason why we call this as not quality or  a data that is not clean. So we take any raw data and when we want to perform analysis on raw data, we need to perform some level of processing on the data. The reason we require processing on the data is because when we do processing on the data, we actually are trying to eliminate the anomalies or say the unclean information or the unclean facts that are available in the data, sometimes even missing information. Pre-processing is therefore necessary in order to improve the quality of the data and subsequently to obtain quality knowledge from the data. Data analytics is the act of analyzing raw data with the goal of drawing conclusions about that information.

This data analytics can be broadly classified into three categories. The first category is exploratory data analysis. We will be analyzing the data with the goal of discovering new features that could aid in the decision-making process. This is what you call exploratory data analysis. New features in the data are discovered new features, certain features that are not already known to the real world, or certain features that are hidden in the data, so that is what we call as new features. EDA shortened.

Confirmatory data Analysis is the second kind. Verifying data analysis what do you mean by confirmatory data analysis? We actually try to prove certain things, such as whether or not there is a pattern that is present in the data. To do this, we will be proposing a hypothesis that will discuss a pattern and determining whether the hypothesis is true or false.

The third type of data analysis qualitative data analysis this is fundamental drawing conclusions from non-numerical data. So these are the Three Types of data analysis that we can do and all these three things put together is only called as Data Analytics.

But till date there is no exact definition for Data Analytics. Data Analytics actually defer from one person to another person in terms of the scope, in terms of the analysis, in terms of the data, that we are actually dealing with we will discuss about how it is different from data mining in coming para.

Previously mentioned Data analytics can be distinguished from data mining by the analysis’s score, purpose and focus. For instance, we can be looking for an analysis that must highlight a particular feature or pattern that is present in the data collection. Because of this, our analysis must be limited to this scope because our scope is only that one pattern. That is, we must consider the scope and do the analysis appropriately. Sometimes we will be working towards a goal, such as finding a pattern for a certain decision-making process, thus with that specific goal in mind, we will be conducting the analysis. Data analytics therefore varies from one person to another, as well as from one instrument to another. Why I called up the tool. Typically, when we conduct analytics, we will either use or create our own tools to carry out the analysis. Therefore, when we rely on a tool, that tool has been built for a specific purpose; as a result, the data we input into the tool will be assessed with respect to that goal. That is the reason why Data Analytics differ from typical data mining in terms of the analysis’s scope, goal, and focus. What does data mining entail when we discuss it? To find hidden patterns and links, they take the data and put it through various algorithms or just scan it. We cannot clearly distinguish between data mining and data analysis based on the volume of data because when it comes to data mining, we are dealing with a very large data set. The size of the data set with which we are actually dealing doesn’t matter here because fundamentally, in data mining, we are dealing with a very large data set. Then, what are the differences? Inferences, or the process of drawing a conclusion solely based on the knowledge a researcher has, are the main emphasis of data analytics. Fundamentally, we will only discuss the inferences that we will draw. However, when we run any analysis, we unavoidably find patterns that may highlight specific scenarios. However, the real value of these scenarios can only be understood when we apply this to decision-making environments, particularly in the business world.

For example I identify a pattern say purchase of an item A implies purchase of another item B now this might be a pattern which is a result of applying any data mining technique. Data Analytics and data mining are similar in that they both provide patterns, but they differ significantly in how they are used for analysis. Data mining gives you the patterns, whereas Data Analytics specifically assists you in inferring and using the inference to improve your knowledge. Therefore, while Data Analytics and data mining are similar, they are very different in how it is used to make inferences.

Ashwini Gadwal

Assistant Professor, ICA, SAGE University Indore