2Department of Social Psychology and Quantitative Psychology, Faculty of Psychology, University of Barcelona, Barcelona, Spain. 1Department of Psychobiology and Behavioral Sciences Methodology, University of Málaga, Málaga, Spain. Blanca 1*, Rafael Alarcón 1 and Roser Bono 2,3 Here we discuss a brief overview of Data Analysis and various methodologies based on the use case and domain of the application.María J. This is a guide to Types of Data Analysis. But in most cases, we will start with descriptive and exploratory data analysis and develop predictive models to predict future outcomes. Do we need to use all of these methods or we can use any of them? Well, now it is based on the use case and domain of the application. In this article, we have discussed the various methodologies of data analysis. Instead of this we scientifically take samples from the population and by the help of statistical analysis, we derive the index. It is not feasible to reach each consumer one by one and calculate. For example, deriving the consumer price index or per capita income. Inferential analysis is the literature of data science, while we predict the referential outcome for multiple sectors. Machine learning models are developed based on historical data to predict the outcome of similar unseen future events. Predictive analysis helps us to give the approximated or most likely outcome of the important questions that then results in massive scaled business and socio-economical changes. Predictive analysis helps us to answer the following questions: ‘Can we predict if a buyer will purchase a specific product or not?’ Or ‘Can we estimate the total cost an Insurer has to pay for the claims? ‘Or ‘Can we estimate the amount of rainfall in upcoming monsoon?’ The heart of data science is based on predictive analysis. What happens if we know the mistakes we will make in the future in advance? We will try to avoid those right? Predictive analysis is nothing but the most scientific way to predict future outcomes by analyzing historical events. In python, there are many libraries to perform exploratory data analysis. It is an effective way to explain the incurred result to executives and non-technical stack holders. (i.e methods like PCA and t-SNE helps in dimensionality reduction). 
This is particularly useful when we deal with high dimensional data.
It helps us to identify which features are more important.Exploratory data analysis gives a visual way to describe the data, which helps to identify the characteristics of the data more clearly.For multivariate analysis, we use the scatter plot, contour plots, multi-dimensional plots, etc.īut why we need Exploratory Data Analysis? To analyze a single variable (univariate analysis) we can use a bar plot, histograms, box plot with whisker, violin plot, etc. In the visual way of data analysis, we use various kinds of plots and graphs for analyzing data. Multivariate analysis (comparative analysis of multiple variables, if we compare the correlation of two variables, it is called bivariate analysis).
Uni variate analysis (exploring characteristics of a single variable). We can also divide the exploratory data analysis into two parts: Once we have a basic understanding of the data at hand through descriptive analysis, we will move to exploratory data analysis. In contrast to descriptive data analysis where we analyze the data numerically, exploratory data analysis is the visual way to analyze the data. #Data analysis methods code
We also use other libraries like the stats model or can develop our code as per use case. In python, pandas library provides a method called ‘describe’, which provides descriptive information about the data frame.
Inter Quartile Range (values between 25 and 75 percentile of a list of numbers). Variance (square of standard deviation). Standard deviation (amount of variation of a set of values from the mean value). Median (middle value of a list of numbers). Mode (most frequent number in a list of numbers). Mean (average of all numbers in a list of numbers). Important terminologies of the descriptive analysis are: In most practical data science use cases, a descriptive analysis will help you to get the high-level information of the data and getting used to the data set. We can get the central tendency and the dispersion of the numerical variables of the data using this type of analysis. In the literature of descriptive analysis, you will seek questions like what is the mean, mode of the selling price of a car type, what was the revenue incurred by selling a particular type of car, etc. Suppose you are analyzing the sales data of a car manufacturer. In the descriptive analysis, we get a summarized value of the numerical variables. Hadoop, Data Science, Statistics & othersĭescriptive analysis is the numerical way to get insights about the data.
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