**Data science** requires math and statistics because these students are the basic foundation of all machine learning methods. In fact, mathematics is behind everything around us, from shapes, forms and colors to the number of petals in a flower. Mathematics covers all areas of our lives.

While programming languages, machine learning methods, and a data-based approach are essential to becoming a data scientist, data science is not about these areas. In this blog post, you will learn the importance of mathematics and statistics for data science and how to use them to build machine learning models.

To gain in-depth knowledge of **Data science** and various machine learning methods, you can join Naresh I Technology **data science certification training .**

**The following topics are covered in this blog :**

- Introduction To Statistics
- Terminologies In Statistics
- Categories In Statistics
- Understanding Descriptive Analysis
- Descriptive Statistics In R
- Understanding Inferential Analysis

**Do you want to become a Data Science Expert join the Live Training Program on Naresh I Technologies .**

**Introduction To Statistics**

- You need to know your basics to be a successful
**data scientist**. Mathematics and Statistics Construction modules of machine learning methods. - It is essential to know the techniques behind the various machine learning methods to know how and when to use them.
- Now the question arises, what are the statistics? Statistics is the mathematics of data collection, analysis, interpretation, and presentation.
- Because statistics are used to implement complex problems in the real world, data scientists and researchers can find meaningful trends and changes in data.
- Simply put, statistics can be used to obtain meaningful statistics from data by performing mathematical calculations.
- Implements a number of statistical functions, policies, and algorithms for analyzing source data, creating a statistical model, and predicting or predicting results.

**Terminologies In Statistics – Statistics For Data Science**

One should be aware of some important statistical terms when dealing with **statistics for data science**. I discussed these words below :

- Population is the set of sources from which data should be collected.
- A Sample is a subset of the population
- The Variable is any property, number or quantity that can be measured or quantified. Also called variable data item.
- The statistical model or population parameter is a measure of the family of probability distributions. For example, population average, mean, and so on.

**Types Of Analysis**

**Analysis of any event can be done in one of two ways :**

**Quantitative Analysis :** Quantitative analysis or statistical analysis is the science of collecting and interpreting data using numbers and graphs to identify patterns and trends.

**Qualitative Analysis :** Quality or statistical analysis provides general information and uses text, audio, and other media.

- For example, if I want to buy a coffee from Starbucks, it is available in short, tall and grande. This is an example of a quality analysis. But if a store sells 70 regular coffees a week, that is quantitative analysis, because we have the number that represents the coffees sold per week.
- Although the purpose of these two analyzes is to give results, quantitative analysis gives a clear picture, so it is important in analyzes.

**Categories In Statistics**

**Two main categories of statistics are found , these are :**

- Descriptive Statistics
- Inferential Statistics

**Descriptive Statistics**

- Descriptive statistics use data to provide descriptions of a population based on the number of calculations, maps, or tables.
- Descriptive Statistics help to organize the data and focus on the characteristics of the parameters that the data presents.

**Do you want to become a Data Science Expert join the Live Training Program on Naresh I Technologies .**

**Inferential Statistics**

- Inferential Statistics provide assumptions and predictions about a population based on a sample of data taken from a suspicious population.
- Inferential Statistics generalize a large data set and come to a conclusion .Use the possibility of occurrence. It allows you to assess the population parameters and create models based on the sample statistics.

**Understanding Descriptive Analysis**

When trying to represent data in the form of maps such as histograms and line plots, the data is represented on the basis of a kind of central trend. For statistical analysis, centralized measures such as mean, mean, or variance measures are used.

**Statistics In R**

- R is open source and readily available. Unlike SAS or Matlab, R can be installed, used, updated, cloned, modified, redistributed and resold.
- R Cross-platform compatible. It is compatible with Windows, Mac OS X and Linux.
- It can import data from Microsoft Excel, Microsoft Access, MySQL, S.Culite, Oracle, and other programs.
- R is a powerful scripting language and can handle large and complex data sets.
- R is very flexible and developed. Many new developments in statistics first appear as R sets.

**Understanding Inferential Analysis**

**Statistics**use the hypothetical test to systematically verify whether a theory is accepted or rejected. Hypothesis testing is a hypothetical statistical technique used to determine whether there is sufficient evidence in a data model to determine whether a particular condition is true for an entire population.- Under the characteristics of a general population, we take a random sample and analyze the characteristics of the sample.
- We examine whether the identified results accurately represent the population and finally interpret their results. Whether or not to accept the concept depends on the percentage value we get from the assumption.

**Looking for a Data Science Mock Interview Test Join Naresh I Technologies.**