Introduction

In this article, we will go into details about Data Science and Statistics. Our experts have gathered a lot of information with respect to both of these fields. Knowing each of them in detail will help you in differentiating Data Science with Statistics. Indeed, Statistics and Data Science have similarities, but again they are different too. With the help of this article, you will be able to differentiate between both of these fields with ease. 

Why is it important to get into the details of Statistics and Data Science?

This is true that these fields gave a great deal of overlap between them, and there are a lot of definitions of one discipline that could just as easily describe the other discipline. But when talking practically, then both of these fields differ in a number of key ways. Both of these fields differ in their processes, the types of problems studied, and several other factors. This is why it is important to study both of these fields in detail. 

Data Science Vs Statistics

Let us first look into...

Data Science

When talking about data science, we can say that it is one of the rapidly emerging trends in computing. It is also a vast multi-disciplinary area. All of the subjects namely computer science, programming, economics software engineering, mathematics and statistics, and business management, come under the umbrella of Data Science. Data science involves the collection, analysis, management, preparation, visualization, and storage of large volumes of information.

To explain Data science in a much simpler way, it can be perceived as having strong links with databases including big data and computer science. A person who studies Data science is a Data Scientist. He can be addressed as an individual with adequate domain knowledge relevant to the question addressed.You can find IBM Data Science Professional Certificate Course and their detail review at TakeThisCourse.Net

Data Science is a Specialized Skill

Since Data Science is a specialized skill. It involves the understanding of the following:

• It is designed and implemented using 4A's. 4 A's are Data Architecture, Acquisition, Analysis, and Archival.

• It involves the application of advanced techniques in mathematics and statistics to model data for deep analysis

• It involves adequate programming and development skills, algorithm development skills

• Learning analytical and ethical reasoning skills

• Understanding communication and business skills

We can conclude that data science is an interdisciplinary area and needs diverse skill sets to gain mastery in this domain. Moreover, data scientists are supposed to be familiar with business models and paradigms. They can be considered as the professionals who can ask good business questions to obtain meaningful insights from given data sets.

Let's talk about...

Statistics

On defining the field of Statistics, one can say that it is another broad subject which deals with the study of data and is widely applied in various fields. Statistics can be considered as a methodology that helps in making conclusions from data.

Statistics can refer to the mathematical analysis which uses quantified models to represent a given set of data. By studying statistics, you will be able to understand different methods to gather data, analyze them and interpret results. These methods are commonly used by scientists, researchers, and mathematicians in solving problems. Even though statistics shows the procedures to collect data, it also helps in taking out the information from numerical and categorical data. Over here, the categorical data refers to unique data. That unique data can be the blood group of a person, marital status, etc. 

Statistics play an important role in data related studies

It plays an important role, as statistics can help us to:

• Decide what type of data required to address a given problem

• It reaches to organize and sum up data

• Moreover, its studies are used in the analysis to be done to conclude data

• It can help in knowing the effectiveness of results and to evaluate uncertainties

Moreover, statistics limits itself with tools such as frequency analysis, mean, median, variance analysis, correlation, and regression etc. With the help of Statistical studies, you will be able to focus on analysis using standard techniques involving mathematical formulas and methods.

Conclusion

In the concluding remarks, we cannot deny the fact that Data science and statistics are indistinguishable and are closely linked. It is also clear now that statistics is a tool or method for data science. On the other hand, data science is a wide domain where a statistical method is an essential component. Stay safe and keep learning.

1. data science
2. statistics