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6 Types of Data Analysis That Data Scientists Are Talking About

Did you know that we produce around 2.5 quintillion bytes of data every single day? And not just that, soon, this mind-boggling number is just going to increase. A lot has been said and written about the interpretation of data and data analysis and its enormous digital revolution. The fact is data has the power to make your business flourish and strengthen your decision-making prowess. Every day, data science is exploring newer horizons and thankfully, the good part is that companies across the globe have understood this gigantic digital reality and have already set courses on the data analysis journey.

We are delving into the world of data analysis and its different types. Let’s start by understanding the concept of data analysis.

What is data analysis?                                                                                   

Data analysis can be defined as the process of inspecting, sorting, and transforming raw data to extract useful information and insights to support data-related decision-making. Data analysis is enabling organizations to become more efficient and customer-centric in their approach.

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Statistician John Tukey, defined data analysis as:

Procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of (mathematical) statistics which apply to analyzing data.

Where does data analysis come into play?

Data analysis uses a host of techniques and methodologies and is prevalent in different fields including science, social science, and business. It helps to minimize human biases, enables a faster and more efficient system, and reduces risks.

6 Types of Data Analysis

Here’s what you need to know about the types of data analysis.

Exploratory Analysis (EDA)

Data scientists use exploratory analysis to examine and explore different kinds of data sets and discover new connections and create a hypothesis. Under this type, scientists attempt to go beyond formal modeling and reveal a better understanding of data set variables. EDA determines whether the statistical technique you have considered is appropriate or not. Scientists rely on this method to look at the data before making any kind of assumptions. Common data science tools such as Python and R are used to create an EDA.

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Exploratory data analysis can be further classified under 4 primary types:

  • Univariate non-graphical: This type is the simplest of all as it involves just one particular variable, which means, you don’t have to deal with causes or relationships.
  • Univariate graphical: The full picture is often hidden in this type of data and so, the need for the graphical method is counted upon. Steam-and-leaf plots, histograms, and box plots are common examples in univariate graphs.
  • Multivariate nongraphical: Multivariate comes into effect from more than one variable. With the use of statistics and cross-tabulation, the relationship between two or more variables is revealed.
  • Multivariate graphical: Graphics such as grouped bar plots, bar charts, and run charts, are used to display the relationship between two (or more) sets of data
Descriptive Analysis

The descriptive analysis offers simple summaries of samples and measurements. This basic type consists of common statistics like measures of central tendency, frequency, position, and measures of dispersion. A simple presentation of the data is the result of a descriptive analysis. You can identify techniques and patterns with the help of this analysis.

Causal Analysis

This largely looks at the cause and effect of the relationship between variables. The main goal is to find the cause of the correlation between variables and so, you are often driven to question if the observed correlations are valid or not. Scientists use this type of analysis in random studies focused on identifying causation.

Predictive Analysis

In predictive analysis, scientists use historical data or current data to predict the future. The accuracy highlight depends on the input variables and the types of models involved. This model was widely used during the 2020 US Elections to predict the winning candidate. In this example, input variables include historical polling data, trends, and current polling data.

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Mechanistic Analysis

This kind of data analysis comes into effect when scientists try to understand the specific changes one variable brings in another variable. Scientists use this type of analysis in cases where a very high level of precision is required and the chances of error are very bleak. For instance, this is applied in physical or engineering sciences like understanding biological or behavioral patterns or the mechanism of action.

Inferential Analysis

This type of data analysis does not involve big data sets or big predictions. Here, a small sample of data is used to estimate information about a larger group of data. Scientists use this form of analysis to make general conclusions about a larger population by using a population sample.

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