Data Analytics

Interview Questions

Data Analytics involves examining and interpreting data to uncover meaningful patterns, trends, and insights that can inform decision-making.
Descriptive Analytics examines historical data, Predictive Analytics forecasts future trends, and Prescriptive Analytics recommends actions based on analysis.
CRISP-DM (Cross-Industry Standard Process for Data Mining) is a widely used process model for data analytics, consisting of six phases: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment.
Structured data is organized and follows a predefined model, while unstructured data lacks a predefined data model and is often text-heavy or multimedia.
Exploratory Data Analysis involves visualizing and summarizing data to understand its characteristics, identify patterns, and guide further analysis.
Correlation is a statistical measure of the relationship between two variables, while causation implies that one variable causes the other.
Outliers are data points significantly different from the majority. They can distort analysis results, affect statistical measures, and impact model accuracy.
A Box Plot displays the distribution of data, indicating the median, quartiles, and potential outliers.
Data cleansing involves identifying and correcting errors or inconsistencies in datasets to improve data quality.
Data normalization scales numerical data to a standard range, preventing features with large magnitudes from dominating the analysis and ensuring fair comparisons.

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