Introduction

Data Science is a popular term these days. What does it mean? What are the different types of data science? Where can you study data science? How do you get a job in Data Science? What are the possible applications for Data Scientists in research? Join us as we explore the field of Data Science, and it’s future in research.

Here is your chance to learn about this exciting future field! In this post, we will discuss what we think is going to happen with data science in research. Hopefully by the end, you will see what an exciting future our field has and will want to get involved.

Data Science is a hot topic right now, but it’s not the same as the data analysis of a few years ago. This new term is based on a lot of mathematical theory that has progressed over years and decades. Just like other scientific fields, there will be people who study the field, people who contribute heavily to the field, and people who use tools that have been created in this field. We are going to try and explain these different roles in my explanation below.

What is data science?

So what is data science? We see data science as the role of using mathematics and statistics to help solve research problems. This definition encompasses everything from pure math/statistics all the way through machine learning. In the future, it may be possible to create some machine learning algorithms that can read and interpret research data but we are not there yet. For the purposes of this blog post, we will focus on statistics and data analysis.

We will also define the word data scientist as someone who focuses on using statistics and mathematics to solve research problems. We will describe data scientist in two ways for this post: The Path of the Data Scientist and The Path of Data Science . This is a very important distinction because it tells us how a data scientist learns their tools. If a data scientist learns their tools on the job, they have taken a path of learning that most researchers do not get to see. Here are the definitions:

The Path of The Data Scientist

The tool kit of a person who is a data scientist is quite extensive. Many of these people have either some level of formal education in statistics and/or computer science, or they have spent many hours learning on their own.

If you want to know more about why this is important, we highly recommend to check out Data Science training in Kochi. For now, we will just assume that a person has put in the time and feels confident using these tools: R, Python, SQL, and maybe even C++ or C#. These people may be doing their research at a university, or they may be working in industry. They have fairly good knowledge of the tools they use, and they know how to apply them.

The Path of Data Science

There is another group of people that I will call data scientists as well. This group is made up of students that are looking toward a career in data science, software developers building data science tools, and statisticians who want to learn Python so that they can code more easily.

The way these people are different from the first group is that their toolkit is much smaller. They have probably taken some courses on statistics and/or maybe Python, but not all three like the first group. They have also not spent all of their time working with the tools. They may not be familiar with all the nuances of Python, and maybe they don’t even know SQL.

Why is this important?

What are the big differences between these two groups? Why is this distinction so important?

The answer to that question is simple: they have very different goals.

Let’s go over them here:

The Path of the Data Scientist – Most data scientists fall into this group. They are looking to advance their skills and build a career in this field. They are confident with their toolkit, know how to apply it, and would like to move into the industry.

The Path of Data Science – The career path of the data scientist who knows R, Python, SQL and maybe even C++ is very different from that of the person who is just learning these tools. They are much more likely to be working in industry already or at a university. They are more focused on learning about the latest methods and algorithms for data science and applying that knowledge to their research.

The Challenge of Learning Data Science

Data science is really exciting but it has a huge additional challenge over many other fields. This challenge is that you need a very broad set of skills to be considered even moderately competent as a data scientist. You have to have programming skills, statistics knowledge, and mathematics/algorithms knowledge. Most people aren’t strong in all of these areas. If you are working in industry, you may even need to be able to work with people from other disciplines.

A lot of people are either not confident enough to learn the tools or they don’t want to learn the tools because it’s easier just to use a tool than it is to learn the tool so they can use it exclusively. They feel that they already know how to do the research with this method and they don’t need or want any additional skills. We hope you find this helpful. If you want learn more about data science and AI you can checkout Data Science training in Kochi.