Data analysis detailed process of analyzing cleaning transforming and presenting useful information with the goal of forming conclusions and supporting decision making. Data can be analyzed by multiple approaches for multiple domains. It is very essential for every business is today to analyze the data that is obtained from various means.
Data analysis is useful in drawing certain conclusions about the variables that are present in the research. The approach to analysis, however, depends on the research that is being carried out. Without using data analytics, it is difficult to determine the relationship between variables which would lead to a meaningful conclusion. Thus, data analysis is an important tool to arrive at a particular conclusion.
Data can be analyzed in various ways. Following are a few methods by which data can be analyzed :
1) Exploratory Data Analysis (EDA)
It is one of the types of analysis in research which is used to analyze data and established relationships which were previously unknown. They are specifically used to discover and for new connections and for defining future studies or answering the questions pertaining to future studies.
The answers provided by exploratory analysis are not definitive in nature but they provide little insight into what is coming. The approach to analyzing data sets with visual methods is the commonly used technique for EDA. Exploratory data analysis was promoted by John Tukey and was defined in 1961.
Graphical techniques of representation are used primarily in exploratory data analysis and most used graphical techniques are a histogram, Pareto chart, stem and leaf plot, scatter plot, box plot, etc. The drawback of exploratory analysis is that it cannot be used for generalizing or predicting precisely about the upcoming events. The data provides correlation which does not imply causation. Exploratory data analysis can be applied to study census along with convenience sample data set.
Software and machine-aided have become very common in EDA analysis. Few of them are Data Applied, Ggobi, JMP, KNIME, Python etc.
2) Descriptive data analysis
This method requires the least amount of effort amongst all other methods of data analysis. It describes the main features of the collection of data, quantitatively. This is usually the initial kind of data analysis that is performed on the available data set. Descriptive data analysis is usually applied to the volumes of data such as census data. Descriptive data analysis has different steps for description and interpretation. There are two methods of statistical descriptive analysis that is univariate and bivariate. Both are types of analysis in research.
A) Univariate descriptive data analysis
The analysis which involves the distribution of a single variable is called univariate analysis.
B) Bivariate and multivariate analysis
When the data analysis involves a description of the distribution of more than one variable it is termed as bivariate and multivariate analysis. Descriptive statistics, in such cases, may be used to describe the relationship between the pair of variables.
3) Causal data analysis
Causal data analysis is also known as explanatory Data Analysis. Causal determines the cause and effect relationship between the variables. The analysis is primarily carried out to see what would happen to another variable if one variable would change.
Application of causal studies usually requires randomized studies but there are also approaches to concluding causation even and non-randomized studies. Causal models set to be the gold standard amongst all other types of data analysis. It is considered to be very complex and the researcher cannot be certain that other variables influencing the causal relationship are constant especially when the research is dealing with the attitudes of customers in business.
Often, the researcher has to consider psychological impacts that even the respondent may not be aware of at any point and these unconsidered parameters impact the data that is analyzed and may affect the conclusions.
4) Predictive data analysis
As the name suggests Predictive data analysis involves employing methods which analyze the current trends along with the historical facts to arrive at a conclusion that makes predictions about the future trends of future events.
The prediction and the success of the model depend on choosing and measuring the right variables. Predicting future trends is very difficult and requires technical expertise in the subject. Machine learning is a modern tool used interactive analysis for better results. Prediction analysis is used to predict the rising and changing trends in various industries.
Analytical customer relationship management, clinical decision support systems, collection analytics, fraud detection, portfolio management are a few of the applications of Predictive Data Analysis. Forecasting about the future financial trends is also a very important application of predictive data analysis.
Few of the software used to Predictive analysis are Apache Mahout, GNU Octave, OpenNN, MATLAB etc.
5) Inferential data analysis
Inferential data analysis is amongst the types of analysis in research that helps to test theories of different subjects based on the sample taken from the group of subjects. A small part of a population is studied and the conclusions are extrapolated for the bigger chunk of the population.
The goals of statistical models are to provide an inference or a conclusion based on a study in the small amount of representative population. Since the process involves drawing conclusions or inferences, selecting a proper statistical model for the process is very important.
The success of inferential data analysis will depend on proper statistical models used for analysis. The results of inferential analysis depend on the population and the sampling technique. It is very crucial that a variety of representative subjects are taken to study to have better results.
The data analysis is applied to the cross-sectional study of time retrospective data set and observational data analysis. Inferential data analysis can determine and predict excellent results if and only if the proper sampling technique is followed along with good tools for data analysis.
6) Decision trees
This is classified as a modern classification algorithm in data mining and is a very popular type of analysis in research which requires machine learning. It is usually represented as a tree-shaped diagram of a figure that provides information about regression models or classification.
The decision tree may be subdivided into the smaller database is that has similar values. The branches determine how the tree is built where does one go with the current choices and where would those choices lead to next.
The primary advantage of a decision tree is the domain knowledge is not an essential requirement for analysis. Also, the classification of the decision tree is a very simple and fast process which consumes less time compared to other data analysis techniques.
7) Mechanistic data analysis
This method is exactly opposite to the descriptive data analysis, which required the least amount of effort, mechanistic data analysis requires a maximum amount of effort. The primary idea behind mechanistic data analysis is to understand the nature of exact changes in variables that affect other variables.
Mechanistic data analysis is exceptionally difficult to predict except when the situations are simpler. This analysis used by physical and engineering science in case of the deterministic set of equations. The applications of this type of analysis are randomized trial data set.
8) Evolutionary programming
It combines different types of analysis in research using evolutionary algorithms to form meaningful data and is a very common concept in data mining. Genetic algorithms and evolutionary algorithms are the most popular programs of revolutionary programming. These are an accident in case of independent techniques since they have the ability to search and explore large spaces for discovering good solutions.
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