Summary Fundamendals of Statistics for Data Science

Statistics For Data Science | Data Science

Introduction to Statistics

Statistics is a mathematical science that involves the collection, presentation, analysis, and
interpretation of data. It is widely used to simplify complex problems and make
well-informed decisions. Statistical analysis can be done in two ways: statistical analysis
and non-statistical analysis. Statistical analysis involves collecting, exploring, and presenting
large amounts of data to identify patterns and trends. Non-statistical analysis provides
generic information and includes text, sound, still images, and moving images. Although
both forms of analysis provide results, statistical analysis gives more insights and a clearer
picture, making it vital for businesses.


Categories of Statistics


There are two major categories of statistics: descriptive statistics and inferential statistics.
Descriptive statistics help organize data and focuses on the main characteristics of the data.
It provides a summary of the data numerically or graphically. Inferential statistics generalizes
the larger data set and applies probability theory to draw a conclusion. It allows you to infer
population parameters based on the samples to sistex and to model relationships within the
data.


Statistical Terms


There are various statistical terms that one should be aware of while dealing with statistics,
including population, sample, variable, quantitative variable, qualitative variable, discrete
variable, and continuous variable.


Statistical Measures


Typically, four types of statistical measures are used to describe the data: measures of
frequency, measures of central tendency, measures of spread, and measures of position.