Data analysis is the process of inspecting cleansing, transforming and modeling data with the intention of gaining useful information, drawing conclusions, and assisting decision-making. Data analysis employs a variety of techniques including descriptive statistics as well as inferential statistics (modeling relationships between variables), and cluster analysis.
Data analytics collects raw data and shines light on trends and insights. This makes it easier to make informed choices in virtually every aspect of business. From sales, marketing, and supply chain management to human resources and finance Data analysis can improve efficiency across all departments, making it easier to see the forest for the trees.
The first step in data analysis is to collect, organize and store the data. This involves the identification of duplicates and elimination from the dataset. Duplicates could affect the outcome of an analysis by giving certain values greater weight. It is also crucial to note any missing data in the analysis. Missing data can also affect results by causing uncertainty. This can be avoided by employing statistical techniques like the imputation method or removing rows that have missing values altogether.
Once the data has been collected the next step is performing an inferential analysis to find out if there are relevant patterns in the data. Comparing the data to other information, like historical trends or past behavior can aid in doing this. This can be used to pinpoint the factors that lead to success or failure, and also to improve processes. It can also be used to forecast the future direction of events data analysis or trends by using regression analysis to find the relationship between the dependent and independent variables. Other popular predictive techniques include multivariate linear or binomial regression as well as decision trees and linear or binary regression.