Home > Posts > BigData > Types of Data Analysis Methods

# Types of Data Analysis Methods

Data Analysis is the process of systematically applying statistical and/or logical techniques to describe and illustrate, condense and recap, and evaluate data. it is is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has two prominent methods we will explain them

• qualitative research is primarily exploratory research. It is used to gain an understanding of underlying reasons, opinions, and motivations. It provides insights into the problem or helps to develop ideas or hypotheses for potential quantitative research.
• quantitative research s a structured way of collecting and analyzing data obtained from different sources. it involves the use of computational, statistical, and mathematical tools to derive results.

Types of Data Analysis Methods

There are many types of data analysis methods. we will explain them in this article.

Descriptive it describes a set of data which is

• the first kind of data analysis performed on a dataset.
• applied to large volumes of data, such as census data.
• The description and interpretation processes are different steps.
• Univariate and Bivariate are two types of statistical descriptive analyses.
• Type of data set applied to a whole population.

Exploratory is research conducted for a problem that has not been studied more clearly, intended to establish priorities, develop operational definitions and improve the final research design. Exploratory research helps determine the best research design, data collection method, selection of subjects and analyze data sets to find unknown relationships.

• Exploratory models are good for discovering new connections.
• They are also useful for defining future studies/questions.
•  Exploratory analyses are usually not the definitive answer to the question at hand, but only the start.
•  Exploratory analyses alone should not be used for generalizing and/or predicting.
•  correlation does not imply causation.
• Type of data set applied to a random sample with many variables measured.

Inferential is the process of using data analysis to deduce properties of an underlying probability distribution. Inferential statistical analysis infers properties of a population, for example by testing hypotheses and deriving estimates. use a relatively small sample of data to say something about a bigger population.

• Inference is commonly the goal of statistical models.
• Inference involves estimating both the quantity you care about and your uncertainty about your estimate.
• Inference depends heavily on both the population and the sampling scheme.
• Type of data set applied to Observational, Time Study, and Retrospective Data Set the right, randomly sampled population.

Predictive encompasses a variety of statistical techniques from data mining, predictive modeling, and machine learning, that analyze current and historical facts to make predictions about the future or otherwise unknown events. it uses the data on some objects to predict values for another object.

• The models predict, but it does not mean that the independent variables cause.
•  Accurate prediction depends heavily on measuring the right variables.
•  Although there are better and worse prediction models, more data and a simple model work really well.
•  Prediction is very hard, especially about the future references.
•  Type of data set applied to train and test dataset from the same population.

Causal is what connects one process with another processor state, where the first is partly responsible for the second, and the second is partly dependent on the first. In general, a process has many causes, which are said to be causal factors for it, and all lie in its past. it used to find out what happens to one variable when you change another.

• Implementation usually requires randomized studies.
•  There are approaches to inferring causation in non-randomized studies.
•  Causal models are said to be the “gold standard” for data analysis.
•  Type of data set applied to Randomized Trial DataSet from a randomized study.

Mechanistic Understand the exact changes in variables that lead to changes in other variables for individual objects.

• Incredibly hard to infer, except in simple situations.
•  modeled by a deterministic set of equations.
•  the random component of the data is measurement error.
•  If the equations are known but the parameters are not, they may be inferred with data analysis.
•  Type of data set applied to Randomized Trial DataSet about all components of the system.
error: Content is protected !!