Normalizing data involves transforming it to a standard scale without distorting differences in the ranges of values. 1. Min-Max Scaling (Normalization) This technique scales the data to a fixed range, usually 0 to 1 or -1 to 1. 2. Z-Score Standardization (Standardization) This technique transforms the data to have a mean of 0 and a standard deviation of 1. 3. Robust Scaler This technique uses the median and the interquartile range (IQR) for scaling, making it robust to outliers. 4. Max Abs Scaler This technique scales the data by its maximum absolute value, preserving sparsity in data (i.e., useful for sparse data like text data represented as TF-IDF). 5. Decimal Scaling This technique involves moving the decimal point of values of the feature. The number of decimal points moved depends on the maximum absolute value of the feature.

The normal distribution is crucial in data science and data analysis for several reasons: Central Limit Theorem (CLT): Statistical Inference: Simplification and Approximation: Prediction and Error Analysis: Natural Phenomena: Probabilistic Interpretations: Parameter Estimation:

Done

Done

Yes

DONE SIR

I like it

Why normal distribution is important in data science/data analysis? In summary, normal distribution is important in data science because it is a fundamental concept that is used in many statistical analyses, including hypothesis testing, regression analysis, and confidence intervals.

Q1. Why normal distribution is important in data science/ data analysis? The normal distribution, also known as the Gaussian distribution or bell curve, is essential in data science and data analysis for several reasons Common Occurrence in Nature: Central Limit Theorem (CLT): Statistical Inference: Parameter Estimation: Z-Scores and Percentiles: Machine Learning Algorithms: Quality Control and Six Sigma:

learn distributions of data done