Probability Basics for Data Science

Probability in itself is a huge topic to study. Applications of probability are found everywhere whether it is medical science, share market trading, sports, gaming Industry and many more. However in this post my focus is on to explain the topics which are needed to understand data science and machine learning concepts.

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Variance, Standard Deviation and Other Measures of Variability and Spread

Variance and Standard Deviation are the most commonly used measures of variability and spread. Variability and spread are nothing but the process to know how much data is being varying from the mean point. And Variance tells us the average distance of all data points from the mean point. Standard deviation is just the square root of the variance. As variance is calculated in squared unit (explained below in the post) and hence to come up a value having unit equal to the data points, we take square root of the variance and it is called as Standard Deviation.

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Basic Statistics for Data Science – Part 1

The Science of collecting, organizing, presenting, analyzing and interpreting the data is statistics. It is one of the most important disciplines or methods to get a deeper insight into data. Statistical analysis is implemented to manipulate, summarize and investigate data so that useful information can be obtained.populationvssample

Take away from this post:

  • Types of Statistics: Descriptive vs Inferential
  • Basic terminology like Population vs Sample
  • Types of Variables: Numerical vs Categorical
  • Measures of central tendencies: Mean, Median and Mode and their specific use cases
  • Measures of dispersion/spread: Variance, standard deviation etc.

Continue reading “Basic Statistics for Data Science – Part 1”