What is Data Science?

Data science is the science of analyzing raw data using statistics and machine learning techniques with the purpose of drawing conclusions about that information.

Why Data Science? 

Data Science has become a revolutionary technology that everyone seems to talk about. Hailed as the ‘sexiest job of the 21st century’, Data Science is a buzzword with very few people knowing about the technology in its true sense. While many people wish to become Data Scientists, it is essential to weigh the pros and cons of data science and give out a real picture. In this article, we will discuss these points in detail and provide you with the necessary insights about Data Science.

How to Learn Data Science?

Usually, data scientists come from various educational and work experience backgrounds, most should be proficient in, or in an ideal case be masters in four key areas.

1. Domain Knowledge
2. Math Skills
3. Computer Science
4. Communication Skill

1. Domain Knowledge:

Most people thinking that domain knowledge is not important in data science, but it is very important. Let’s take an example: If you want to be a data scientist in the banking sector, and you have much more information about the banking sector like stock trading, know about finance, etc. so this is going to be very beneficial for you and the bank itself will give more preference to these type of applicants more than a normal applicant. 

2. Math Skills:

Linear Algebra, Multivariable Calculus & Optimization Technique, these three things are very important as they help us in understanding various machine learning algorithms that play an important role in Data Science. Similarly, understanding Statistics is very significant as this is a part of Data analysis. Probability is also significant to statistics and it is considered a prerequisite for mastering machine learning.

3. Computer Science:

There is much more to learn in computer science. But when it comes to the programming language one of the major questions that arise is: 

Python or R for Data Science?

There are various reasons to choose which language for Data Science as both have a rich set of libraries to implement the complex machine learning algorithm, visualization, data cleaning. Please refer to R/Python to know more about this.

But my recommendation is one must have knowledge of both the programming language to become a successful data scientist.

Apart from the programming language the other computer science skills you have to learn are:

• Basics of Data Structure and Algorithm
• SQL
• MongoDB
• Excel
• Linux
• Git
• Distributed Computing
• Machine Learning and Deep Learning, etc.

4. Communication Skill:

It includes both written and verbal communication. What happens in a data science project is after drawing conclusions from the analysis, the project has to be communicated to others. Sometimes this may be a report you send to your boss or team at work. Other times it may be a blog post. Often it may be a presentation to a group of colleagues. Regardless, a data science project always involves some form of communication of the projects’ findings. So it’s necessary to have communication skills for becoming a data scientist.