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Exploratory Data Analysis Video Lecture | Mathematics for IIT JAM, GATE, CSIR NET, UGC NET
FAQs on Exploratory Data Analysis Video Lecture - Mathematics for IIT JAM, GATE, CSIR NET, UGC NET
| 1. What is exploratory data analysis? |  |
Ans. Exploratory data analysis is a process of examining and visualizing data in order to understand its main characteristics, patterns, and relationships. It involves techniques such as summarizing data, creating visualizations, and identifying outliers or missing values.
| 2. Why is exploratory data analysis important in data analytics? | |
Ans. Exploratory data analysis is important in data analytics because it helps in gaining insights and understanding the data before applying any statistical or machine learning techniques. It allows data analysts to identify patterns, trends, and outliers, which can guide further analysis and decision-making.
| 3. What are some common techniques used in exploratory data analysis? | |
Ans. Some common techniques used in exploratory data analysis include data summarization (such as calculating means, medians, or standard deviations), data visualization (such as histograms, scatter plots, or box plots), and identifying missing values or outliers.
| 4. How does exploratory data analysis help in data preprocessing? | |
Ans. Exploratory data analysis helps in data preprocessing by providing insights into the quality and structure of the data. It helps in identifying missing values, outliers, or inconsistencies in the data, which can then be addressed through techniques like data imputation or outlier treatment before further analysis.
| 5. Can exploratory data analysis be used for predictive modeling? | |
Ans. Yes, exploratory data analysis can be used for predictive modeling. By analyzing the relationships between variables, identifying important features, and understanding the distribution of the target variable, exploratory data analysis can provide valuable insights for building predictive models. It helps in selecting appropriate variables, detecting multicollinearity, and understanding the data requirements for modeling purposes.
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