Statistics : An Introduction for Data Science
In this article or say blog, I'll share an overview of statistics for Data Science. Here I'll talk about the types of statistics and some terms which will help you to understand further blogs. There'll be a series of blogs which will only be about Statistics and it's real life practical applications and that'll be along with code.
Statistics : The Science of Decisions
Statistics can be defined as the science which deals with the collection,
analysis, inspection and presentation of data.
Types of Statistics :
Descriptive Statistics: It can be defined as describing data. In other words, taking out some essential information from data through various techniques i.e., EDA (Exploratory Data Analysis). For example, from data of heights we can get
information like what is mean height, what is minimum height, what is maximum heights etc.Inferential Statistics: We usually perform and get information using descriptive statistics from sample data and not whole population. But we can use the information from sample data to estimate population parameter. For example, suppose you want to know average height of students in the school but if there are 10,000 students you can't just go and ask each of them there height, instead you'll take out some students and perform descriptive statistics on them and them use that information to estimate average height of students in the school.
Essential terms:
Population and Sample: Data of interest is known as population and some piece of data from population is known as sample. For example, if there are 10,000 students as population for height estimation from school and we took only 500 students as sample.
Frequency: The number of occurrences of any particular value is known as it's frequency. Suppose we took 500 students and we're having 50 students of 170cm, then the 50 if frequency of students with 170 cm.
Census: The estimate of interest from population is called census.
Thank you, for reading this blog. If you'll find something which can be improved here or which can be improved in further blogs then the suggestions are welcome.