RaghavDesai
1 post
Jun 13, 2025
11:59 PM
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Data analytics is a wide range of processes and techniques employed to analyze data to infer meaningful conclusions, inform decision-making, and resolve issues. It encompasses the following essential components:
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1. Data Collection
Obtaining raw data from sources (e.g., databases, sensors, social media, surveys).
Sources are structured (such as spreadsheets or SQL databases) or unstructured (such as emails or images).
2. Data Cleaning and Preparation
Deletion or correction of errors, duplicates, and inconsistencies.
Processing missing data, changing formats, and building new variables/features.
Utilizing statistics and graphical tools (e.g., histograms, scatter plots, heatmaps) to grasp patterns, trends, and outliers.
Assists in developing hypotheses or questions to investigate further.
4. Statistical Analysis
Utilizing descriptive statistics (mean, median, standard deviation).
Inferential statistics (hypothesis testing, confidence intervals, correlation) are used to make conclusions about populations from sample data.
5. Modeling and Algorithms (Predictive and Prescriptive Analytics)
Predictive analytics: Applying historical data to make predictions (e.g., regression, classification, time series analysis, machine learning).
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Prescriptive analytics: Recommending actions from data (e.g., optimization models, decision trees).
6. Data Interpretation and Insight Generation
Converting analysis results to actionable business insights.
Producing dashboards, reports, or storytelling visuals to report findings to stakeholders.
7. Tools and Technologies.
Technologies: Big Data platforms (Hadoop, Spark), cloud services (AWS, Azure), and databases (MySQL, MongoDB).
8. Data Governance and Ethics
Preserving data privacy, security, regulation compliance (such as GDPR).
Ethical aspects of data collection, analysis, and usage.
Do you want examples of how this works in a particular industry (such as healthcare, finance, or marketing)?
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