Data Science Has Changed, Not Died!

Do we still need data science, or are tools such as ChatGPT taking over the majority of tasks?

Data Science Has Changed, Not Died!
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With the constant development in technology and the use of AI in our everyday lives, many are worried about job displacements. Some are even speaking on data science dying. Many are saying that machine learning is replacing data science, stating that data science is an oversaturated field. With the heavy use of tools such as ChatGPT and their use in coding tasks and more, we are questioning if data science is dying. 


But is it? Is it really dying?


Well no of course. We’re getting more data, which is producing valuable insights that drive decisions. These insights can’t be generated from a computer and we need them for data science. Machine learning models can be built and with data can be used to find valuable insight, but the key element is the need for data and what to do with the data. 

And in order to understand what to do with data, you need humans. You need data scientists! But what has changed?


Changes in Data Science


Various elements are changing in data science due to generative AI and the boom of everybody wanting to break into the tech industry. Let's go over some changes in data science.




Tasks such as exploratory data analysis which provided great insight have drastically changed. It typically required data scientists and data analysts to come in and help with the process. However, now with tools such as ChatGPT, and fast courses to become a data scientist - everybody believes they can code and are technically proficient in Python. 

However, that’s not true. If you have the right skill set and are truly proficient in programming languages such as Python - you will stand out. Organizations will still be seeking highly qualified data scientists to help them get the job done, over ChatGPT’s responses and people who did a quick course in data science. 

As a data scientist, it will be your job to adapt to the current market. Continuously learning and improving your skillset is the way you will remain competitive and be truly valued for your skills. 

This includes constantly learning about different software architecture, libraries, frameworks, different programming languages, and more. 


Building Full Applications


Many people are using ChatGPT to help them with coding tasks. But the important thing to understand with ChatGPT is that it can help you build the blocks to your full application, but it can’t bring those blocks together to build the whole foundation. 

Organizations will require somebody who understands all the different blocks and how they come together. They will be able to piece together all the blocks as they understand what each of them does and put them together to build a foundation. 

It doesn’t mean that ChatGPT is not helpful - it is. A lot of programmers are making use of ChatGPT to help them with code blocks, which is helping speed up their code-writing process. At the same time, it is also helping improve the skills of programmers by learning new things and allowing them to be more proficient in their coding. 

So the key point to take from this is that as a data scientist, you will need to know more, if not all. You will need to know each element of data science as well as how to build a full application. 


Merging of Roles


There will be a lot of roles in data science, however, the important thing to note is that a lot of roles will be merging. Before you may have been the go-to person for data analytics, but now you will need to essentially be a jack of all trades - and be a master in overall data science. For example, you will be using your analytical skills to build applications. 

The reason for this is that more and more organizations are looking into the efficiency of job roles, and how many people they really require. For example, should I hire somebody who is good at creating data visualizations and presenting them, or should I find a data scientist who can do it all? From a business perspective, you know who the company is going to choose. 

The best advice I can give you is to be REALLY good at what YOU do. Be the best that you can be, that you don’t feel like you’re getting pushed out. 


Job Market


The job landscape in data science has changed. For many years, a lot of people were trying to break into the tech industry with a quick boot camp and a few Jupyter Notebook projects. Unfortunately, that is not going to help you with this current market. Having a proficient skill set, with years of experience and a high-level understanding of data science is imperative. 

Understanding machine learning architectures and high-level data analytics are areas you want to perfect! You want to stand out!


Wrapping it up


I hope this blog will help you understand how the world of data science has changed, and if you’re looking to enter or grow in the sector - what you need to do! Rather than feeling pushed out, all you need to do is understand what your next steps need to be in order to remain competitive!
Nisha Arya is a Data Scientist, Freelance Technical Writer and Community Manager at KDnuggets. She is particularly interested in providing Data Science career advice or tutorials and theory based knowledge around Data Science. She also wishes to explore the different ways Artificial Intelligence is/can benefit the longevity of human life. A keen learner, seeking to broaden her tech knowledge and writing skills, whilst helping guide others.