**Data Science **is a sub-field of computer science, which empathizes using scientific methods, processes, algorithms, and systems to extract knowledge and insights from processed and unprocessed data. Data Science differs from other computer science fields as the emphasis of the field-work is on data rather than the methods of *achieving/extracting* them.

This makes data science a discrete computer science unit by itself. There are several other sub-fields that branch out of Data Science such as Big Data and Data Mining. With the advancement in technology, consequently leading to increased data production, data science has received a lot of attention and recognition in recent times.

If you want to know about Data Science, the article aims to provide an overview of data sciences, Educational Requirements for Data Science, Elements of Data Science, and much more.

**Also Read: ****Data Science Syllabus**

Table of Contents

### Overview

Data Science revolves around extracting and interpreting data from an array of sources such as log files, social media, sensors, customer transactions, to unlock useful information to influence the decisions of business and stimulate competitive advantage over other counterparts. This requires the Data scientists to be good at not one but many of the field disciplines.

### Educational Requirements for Data Science

The aspirants of Data Science must have graduated with a certain specialization in Engineering or subjects essential to the field, in order to pursue a successful career in Data Science.

**Given below are the educational requirements which candidates need to fulfill :**

- Must have cleared 12th standard in PCM stream, where
**Physics, Chemistry, and Maths**are compulsory subjects. - Students must have completed a
**B.Eng or B.Tech**degree in Statistics or Engineering, Physical Science, Mathematics, Computer Science. - In Masters, students can pursue
**M.Sc, M.Tech, M.Eng**in Data Science, Mathematics, or related fields to Data Science.

**Also Read: ****Data Science Course**

## Elements of Data Science

**Data Science is composed of 3 principal elements. They are:-**

### Machine Learning

Machine Learning is an application of a Computer Science concept named “**Artificial Intelligence**” (**AI**). The aim of the field is to create/produce self-learning abilities in Digital systems, i.e. they won’t have to be explicitly programmed for each use case.

Machine learning is one of the work-fields which we enjoy the benefit of each day, but barely ever realize that. All the way from Search recommendations, market analysis to Weather speculation and Virtual Personal assistants such as **Siri, Cortana**, etc. nowadays fall under the domain of Machine learning applications.

**The field strives to create more Human-like Artificial Intelligence each day.**

**Also Read: ****Data Science Certification**

### Mathematics and Statistics

Mathematics and Statistics is a part of Data Science to which all of its subfields learn from to increase the efficiency of doing a specific task. Almost all of Machine Learning algorithms (**Linear analysis, quadratic analysis**, etc) have mathematical roots. Statistical modes are required in order to compute/improve the efficiency of a given algorithm, which overall leads to improved runtime.

### Business Intelligence

Each organization produces a large amount of data every day, as every business is using computing technologies to operate. Data Scientist uses different graphs and charts, from the generated data obtained through computing technologies.

Thus it helps the management make the best business decisions based on the basis of what the stats and graphs are representing.

**Also Read: ****How to do a Masters in Data Science in Canada? (Updated 2021)**

## Technical skills required to be a Data Scientist

**In order for one to be good at data science, he/she must be acquainted with technical skills and knowledge of an array of Computer Science disciplines. Some of which being:-**

- Programming skills (knowing languages such as Python, C++, R, Julia, etc)
- Mathematical Statistics
- Data Visualization & Communication
- Machine Learning
- Data Wrangling

I hope the article has helped in providing an overview of Data Science, educational requirements for Data Science, and technical skills required by students to be a Data Scientist.

## Comments (0)