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I would like to discuss about  Data exploration : Using exploratory analysis and research techniques to learn 

            Data exploration is the first step in data analysis and typically involves summarizing the main characteristics of a dataset. It is about describing the data by means of statistical and visualization techniques. We explore data to bring important aspects of that data into focus for further analysis. Before a formal data analysis can be conducted, the analyst must know how many cases are in the dataset, what variables are included, how many missing observations there are and what general hypotheses the data is likely to support. An initial exploration of the dataset helps answer these questions by familiarizing analysts about the data with which they are working (Rouse, 2015). Analysts commonly use data visualization software for data exploration because it allows users to quickly and simply view most of the relevant features of their dataset(Kirk, 2016).

Data exploration is an informative search used by data consumers to form true analysis from the information gathered. Often, data is gathered in a non-rigid or controlled manner in large bulks. For true analysis, this unorganized bulk of data needs to be narrowed down. This is where data exploration is used to analyze the data and information from the data to form further analysis.Data often converges in a central warehouse called a data warehouse. This data can come from various sources using various formats. Relevant data is needed for tasks such as statistical reporting, trend spotting and pattern spotting. Data exploration is the process of gathering such relevant data. his process makes deeper analysis easier because it can help target future searches and begin the process of excluding irrelevant data points and search paths that may turn up no results. More importantly, it helps build a familiarity with the existing information that makes finding better answers much simpler.

Many times, data exploration uses visualization because it creates a more straightforward view of data sets than simply examining thousands of individual numbers or names.

In any data exploration, the manual and automated aspects also look at different sides of the same coin. Manual analysis helps users familiarize themselves with information and can point to broad trends.

These methods are also by definition unstructured so that users can examine a whole set without any preconceptions. Automated tools, on the other hand, are excellent at pruning out less applicable data points, reorganizing data into sets that are easier to analyze, and scrubbing data sets to make their findings relevant.