Math and Statistics For Data Science

Posted by ExcelR on February 26th, 2021

Math and statistics are very crucial in the transition process of becoming a successful scientist for the basic foundation of machine learning algorithms. Mathematics is the backbone for data science, and you need to master different skills of data science - statistics, probability, and linear algebra. 

Although having in-depth knowledge and understanding of programming languages (R and Python), machine earning algorithms, and cutting-edge data-driven approaches are highly essential to become a successful data scientist. But data science is not about these things at all. Therefore, you will read about the importance of mathematics and statistics in Data Science, along with how mathematics is used in machine learning algorithms. 

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To become a successful data scientist, you need to know all the basics. Math and statistics are like bricks or building blocks of machine learning algorithms. Therefore, it’s very crucial to understand data science techniques and master them in less time.

For all the complex problems and databases full of structured and unstructured data, data science professionals use statistics, probability, and linear algebra to solve varieties of complex problems and issues. In other words, data scientists use statistics to derive meaningful insights from data using different mathematical operations. The various mathematical operation that data science professionals use is analyzing raw data, building a statistical model, analyzing data, and predicting the results. 

Terminologies In Statistics For Data Science 

Population - Population is the storehouse of different sources of data where data scientists collect the data. 

Sample - A sample is a subset of a population that contains some data for experiment purposes.

Variable - A variable is a characteristic, a number, or any quantity that has no fixed value but is considered as a data item. 

Types of Analysis 

In statistics, there are two types of data analysis. One is the quantitative analysis, and the other one is qualitative analysis. 

In quantitative analysis or statistical analysis, the process involves the collection of data based on numbers, graphs to identify different patterns and trends. And In qualitative analysis or non-statistical analysis, the process involved in the collection of generic data that includes - text and other forms of media. 

Categories of Statistics

There are two categories in statistics based on the collection and analysis of data. They are: 

Descriptive statistics 

Inferential statistics 

Data Science professionals use descriptive statistics to describe the population either through numerical calculations, graphs or in table form. 

In Inferential Statistics, data science professionals make inferences and predictions about the population based on data taken from populations.

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