Advanced statistics

To become a successful Data Scientist you must know your basics. Math and Stats are the building blocks of Machine Learning algorithms. It is important to know the techniques behind various Machine Learning algorithms in order to know how and when to use them. Now the question arises, what exactly is Statistics?

Statistics is a Mathematical Science pertaining to data collection, analysis, interpretation, and presentation.

Statistics is used to process complex problems in the real world so that Data Scientists and Analysts can look for meaningful trends and changes in Data. In simple words, Statistics can be used to derive meaningful insights from data by performing mathematical computations on it.

Several Statistical functions, principles, and algorithms are implemented to analyze raw data, build a Statistical Model and infer or predict the result.

The field of Statistics has an influence over all domains of life, the Stock market, life sciences, weather, retail, insurance, and education are but to name a few.

There are two types

Descriptive Statistics

Descriptive statistics are typically distinguished from inferential statistics. With descriptive statistics, you are simply describing what is or what the data shows. With inferential statistics, you are trying to reach conclusions that extend beyond the immediate data alone.

Inferential Statistics

Inferential statistics are produced through complex mathematical calculations that allow scientists to infer trends about a larger population based on a study of a sample taken from it. Scientists use inferential statistics to examine the relationships between variables within a sample and then make generalizations or predictions about how those variables will relate to a larger population.

Inferential Statistics makes inferences and predictions about extensive data by considering sample data from the original data. It uses probability to reach conclusions.