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Practical Statistics for Data Scientists

50+ Essential Concepts Using R and Python

The book serves as a guide to the statistical methods that are essential for data science, providing over 50 key concepts with practical code examples in both R and Python. It covers topics such as exploratory data analysis, data visualization, probability, statistical modeling, and machine learning, aiming to equip readers with the statistical understanding necessary for real-world data analysis.

Key points:

1. Exploratory Data Analysis (EDA): EDA is the process of understanding data before applying statistical methods. It involves visualizing and summarizing data to identify patterns and anomalies.

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