In order to find relevant information, draw conclusions, and assist decision-making, data analysis entails the process of looking at, manipulating, and modeling data. It entails using a variety of methods and tools to examine large data collections and derive important insights
Preprocessing and data cleaning are important steps because raw data frequently has errors, missing values, discrepancies, or outliers. To maintain the quality and integrity of the data, data cleaning includes locating and fixing these problems. Making the data ready for analysis by converting it into a format or structure is another aspect of preprocessing.
Exploratory Data Analysis (EDA): EDA is the process of analyzing data in order to better understand its features, such as distribution, central tendencies, changes, and relationships between variables. Charts, graphs, and histograms are prominent visualization tools used in EDA to obtain insights and spot emerging patterns or trends.
Data modeling and analysis: In this stage, different statistical or machine learning approaches are applied to the data in order to find relationships, predict the future, or test various hypotheses. That mayinclude techniques such time series analysis, classification, clustering, regression analysis, or data mining algorithms. The type of data and the analysis's objectives influence the technique selection.
Interpretation and insights: Following data analysis, the findings must be understood in light of the current issue. This entails analyzing the data to derive relevant conclusions, highlighting important results, and gleaning takeaways. The ability to effectively communicate these insights is essential for promoting positive outcomes and decision-making.
Reporting and Visualization: The last phase entails succinctly and clearly presenting the findings and outcomes of the data analysis. This may entail developing presentations, dashboards, reports, or visualizations that effectively communicate the findings to stakehol