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  • Importance of Statistics for Data Science | Statistics and Data Science

Importance of Statistics for Data Science | Statistics and Data Science

 July 13  | 0 Comments

In the modern world, everything has become data-driven. The amount of data produced every second in the world multiples into terabytes, and this implies that the field of data science has also grown at a similar pace simultaneously. Analyzing such large amounts of data require capable data scientists too, so it is no surprise that there is a huge demand for great data analysts and that it has become such a lucrative field today.

The main aim of data science is to analyse the unstructured data being produced today, but this is often impossible to do qualitatively – it has to be done quantitatively. After analyzing this data, organisations need to obtain real insights about their customers and their needs, so that these insights can be translated into proper business value quickly. Therefore, the onus is on data scientists to carry out their analyses properly, so as to improve and optimise the way business is conducted. Organisations in a variety of fields, ranging from health care to entertainment currently follow this model.

Data scientists must have a deep understanding of statistical concepts in order to carry out quantitative analysis on the available data. Therefore, they must learn statistics for data science to be successful – this is a given. However, there are a lot of statistics for data science tutorials available online, and the ones by Acadgild are comprehensive enough to provide you with a thorough understanding of what is discussed here.

Let us take a look at some statistical concepts that every data scientist must know, to make his job easier.

Linear Regression

Linear regression is a lynch-pin of statistics and is used to predict the value of a variable based on the values of the other variables present in the analysis. This is done by fitting the best linear relationship in the scatter-plot of the values of two variables – the dependent and the independent ones. The best fit is obtained by ensuring that the sum of all distances between the obtained shape and the values of each point is as less as possible.

There are two types of linear regressions – simple and multiple. In the former, there are only two variables used – a dependent one and an independent one. In the latter, more than one independent variable is used in a bid to predict the value of the dependent variable more accurately.


This is a general term, which is used to refer to data mining methods which categorize the available data to obtain correct and accurate analysis and predictions from them. It is also called a Decision Tree, and there are two main classification methods – Logistic Regression and Discriminant Analysis. For more information on these complex statistical methods, you should check out the course by Acadgild.

Resampling Methods

In this method, samples are drawn from the original data samples repeatedly to obtain a unique sampling distribution which follows the actual data set. This is usually done when the data set is far too large to be analysed entirely, as is the case in most big data analysis. The estimates obtained from this method is unbiased, as it is from the unbiased samples which are from all possible results of the data that the researcher has.

In order to learn more about basic statistics for data science, the best thing to do would be to enroll for an online course and complete it. Acadgild offers high quality and highly rated data science courses which can put you on your way to a successful career as a data scientist.