Terms In Data Analysis

Terms In Data Analysis

 

The technical transformation of data into business information utilizes many standard terminologies that may not be used in routine life but definitely used in real-life data analysis.

Structured data:

One of the preliminary steps in data analysis is the grouping and sorting of the huge amounts of data from diverse sources and of diverse forms and finally arranging them in a comparable tabular format to be interpreted in a single platform. This is structured data.

Data cluster:

Data get grouped together into clusters, each having relatable features or attributes and the analyst can then quickly find the data cluster catering to the attributes he is looking for.

Contextualization:

The choice of the data set depends on the demands of the user and to find the data relevant to a particular preference is contextualization.

Data product:

It is a kind of application running on the computer which takes in the data fed to it and uses it to produce an output to suit the user’s purpose. For example, Crypto Code algorithm takes in the historical currency trading data and trader demands and recommends an end result that is best suited to earn maximum returns for the trader.

Data visualization:

The raw data reaching the data analyst will be of theoretical and quantitative nature in massive numbers. Aggregating all the relevant data into a single productive unit for observing, interpreting and analyzing requires an effective and appealing form of presentation. It can be a pictorial representation, tabular format or any other graphical form so that the users can accurately pick out the essentials. On the data analysis tool, there may be numerous options to visualize the data and you just need to click here to get the simplest visual data.

Hypothesis testing:

A study proceeds by assuming a particular condition. It may be a positive hypothesis stating that the event will occur or a null hypothesis stating the absence of that even. Subjecting this hypothesis to check its possibility or accuracy is hypothesis testing.

Regression analysis:

This is a widely used method to predict the future impact of one independent variable on another variable that is dependent on the former. The method studies the changes in the dependent variable when the independent variable changes over a reference time.

Inferential statistics:

An inference is to correlate between the variables in the study and what can be derived out of them. When mathematical techniques are applied to find out the relationships between these variables, it becomes inferential statistics.

Now, when you try to find the nearest pizza hangouts in your locality using a mobile app, you may be visualizing the result of a data analyst.