The most critical steps in Data Science are finding structure in data and making predictions. Statistical approaches are particularly useful in this case because they can handle a wide variety of analytical tasks. The following are some important examples of statistical data analysis approaches.
- Hypothesis testing One of the foundations of statistical analysis is hypothesis testing. Many questions that arise as a result of data-driven problems can be converted into hypotheses. Hypotheses also serve as natural connections between underlying theory and statistics. Questions and theories may be checked with the available evidence when statistical assumptions are linked to statistical tests. When the same data is used in multiple studies, it’s normal to have to change the significance levels.
- Classification Finding and predicting subpopulations from data requires the use of classification methods. Such subpopulations are to be identified from a data set without a priori knowledge of any instances of such subpopulations in the so-called unsupervised situation. Clustering is a term used to describe this.
When only influential factors are available, classification rules should be found from a labelled data set for the prediction of unknown labels in the so-called supervised case.
Nowadays, there are various methods for both the unsupervised and supervised cases.
However, in the age of Big Data, a fresh look at traditional methods tends to be needed, as the calculation effort of complex analysis methods often grows more than linearly with the number of observations n or the number of features p.. In the case of Big Data, if n or p is high, this results in overly long calculation times and numerical issues.
- Regression When the target variable is evaluated, methods are the primary tool for determining global and local relationships between features. Different methods can be used depending on the distributional assumption for the underlying data. The most popular approach is linear regression, which is based on the concept of normality.
Functional regression for functional data quantile regression and regression based on loss functions other than squared error loss, such as Lasso regression, are more sophisticated approaches. The problems in Big Data are similar to those faced by classification methods when dealing with large numbers of observations n (e.g., in data streams) and/or features p.Time series analysis aims to comprehend and forecast temporal structure In studies of observational data, time series are very common, and the most difficult task for such data is prediction. Behavioral sciences and economics, as well as natural sciences and engineering