Data
Analysis.
Data analysis is the process of systematically examining, cleaning, transforming, and modeling data with the goal of discovering useful information, drawing conclusions, and supporting decision-making. It involves the use of statistical and computational methods to extract insights and knowledge from data and can be applied to a wide range of domains and industries. The end goal of data analysis is to generate actionable recommendations based on the insights gained from the data.
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Understand the Business Request
& User Stories
This step involves understanding the requirements and needs of the business and users in order to properly plan and design a solution that meets those needs. It involves breaking down the business request or user demand into specific and actionable tasks, called "user stories, by breaking down the business request intospecific user stories and defining the acceptance criteria for each, theteam can better understand the requirements, prioritize tasks, and build asolution that meets the needs of the business and users.
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Data Collection
& Preprocessing
Once you have a clear understanding of the problem, the next step is to collect the data we need and preprocessing step typically includes a combination of data cleaning, normalization, feature extraction, and feature selection. Data cleaning involves identifying and dealing with errors, missing values, and inconsistencies in the data. Feature extraction involves creating new features or variables based on the existing data, and feature selection involves selecting the most relevant features to use in the analysis.
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Data.
Exploration
Data exploration is an essential step in the data analysis process where the analyst discovers patterns, relationships, and trends in the data. In this step, the analyst uses descriptive statistics, visualizations, and other techniques to gain a preliminary understanding of the data. This helps in identifying any potential issues, such as outliers or missing values, and preparing the data for further analysis. Data exploration provides crucial insights into the data that inform the approach for further analysis and help to ensure that the results of the analysis are accurate and meaningful.
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Data
Interpretation
Finally, the results of the analysis are interpreted and insights are drawn. This step involves communicating the results in a clear and concise manner, including visualizations and summaries, and drawing conclusions based on the results. The goal is to present the results in a way that is easy to understand and actionable.
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