Data Science vs. Artificial Intelligence – What are the Differences?

With technological advancement, there are so many career opportunities that have come up. Surely, you might be aware of Artificial intelligence and data science. Well, these two are the most crucial technologies that are trending in today’s time. It is highly in demand across the globe and which is why individuals with desired skills are also in demand. Since you may wonder what exactly the difference between the two is, let us explore this post in a better way.

It is data science that uses artificial intelligence in certain operations but not entirely. Data science also contributes to AI to some extent. Many people are in understanding that contemporary Data Science is nothing but Artificial Intelligence, but that is not true at all. Let us understand more about Data Science vs. Artificial Intelligence for clarity.

What is Data Science?


Data science is one trending sector that has been leading in the IT field today. It has been said to have made space in almost every industry. It is a broad version that usually is associated with the process of the data and its system. The focus of data science is taking on sets of data to get valuable information. In such a sector, the data works like fuel which helps to gather all the important information associated with the organization. This way it becomes easy to identify the trends that are ruling the market currently.

It includes different underlying fields such as Mathematics, Statics, and programming to name some. The role of a data scientist is to have a piece of good knowledge in these subjects along with machine learning algorithms knowledge to understand the patterns and trends in the data. This requires quite a lot of dedication, focus, and skills.

There is a certain process of data science that needs to be understood. It includes manipulation, data extraction, visualization, and data maintenance to name some. With the help of data scientists, industries can make data-driven decisions. Besides, they can also assess the performance and see if some changes need to be done for boosting their performance.

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What is Artificial Intelligence?


On contrary to Data science is Artificial intelligence (AI). It is machine-based intelligence. This kind of technology has been designed to post natural human intelligence. The best part about such a type of intelligence is that you can impose and even simulate human intelligence in the machine. Such type of technology makes use of many algorithms that helps in assisting autonomous actions. Many traditional Artificial Intelligence algorithms clearly stated their goals.

In today’s time, it is contemporary AI Algorithms trending which is like understanding the data patterns in-depth and then coming up with the right goal. Such kind of intelligence also makes use of many software engineering principles to create solutions to existing issues. You might be aware of giants such as Amazon, Google, and Facebook. Well, they are resulting in leveraging Artificial Intelligence technology to create an autonomous system.

Talking of which, one such finest example is the AlphaGo by Google. It is a Go-playing autonomous system that has even managed to defeat Ke Jie, who has been the number 1 expert AlphaGo player. This AlphaGo made complete use of the Artificial Neural Networks that were inspired by the neurosis of humans which grasped the information over time.

What are the Differences?


Now that you have a clear understanding of data science and Artificial Intelligence, you may have some doubts in your mind. More specifically you may wonder – which could be the right option to choose. Is Artificial Intelligence or Data Science? Given below information can help you understand the difference and jump on the decision.

1. Scope


There is a wide range of scope for Data science. This means, to gather data there are no limits. It includes different data operations which of course in Artificial Intelligence is not present. No matter from which source and through which means you gather the data, well, you will not be disappointed or restricted at any point in time.

In the case of artificial intelligence, well it is only restricted to the ML algorithms implementation. It does not have a wide range of scope like Data science which is why data science is more in demand considering the scope perspective in mind.

2. The Need


Data science is important to find out the hidden patterns that are available in the data. In the case of AI, it is completely different. AI is associated with the autonomy imparting that is being done to the data model. Data science is also used for creating models with the help of statistical insights.

Whereas the use of Ai is to build models that emulate the cognition and also the understanding of the human. Along with the scope, the need for data science is wider as well which is why it is more in demand.


3. Applications


The applications of Artificial Intelligence are used in different sectors such as the transportation industry, healthcare sector, automation sector, robotics industry, and even the manufacturing industry to name some.

If you count the perspective of data science in the different industries, well it is quite broader in its manner. It is used in the field of Internet search engines such as Yahoo, Google, marketing field, Bing, advertising field, and even the banking sector to name some. This means, at the global level in less period, Artificial Intelligence can be used.

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4. Payscale


“Things that we saw above were the overall perspective of getting data science in use or artificial Intelligence. But those who work in this sector also have better career opportunities.” – as discussed by Marcel Kasprzak, the Managing Director at NeuroSYS, in one of his recent blog posts on AI & Data Science payscale.

Talking of which, the data scientist can earn around US$113k per annum in the United States. There is also scope for such an expert to get a good hike in the future up to US$154k per annum. On contrary to this, Engineers who work on Artificial Intelligence can earn around US$107k per annum. There is also scope for such experts to get a good hike in the future up to US$107k per annum but that depends on their performance, experience, and the company where they work.

5. Data Type


Artificial Intelligence usually consists of data that is in a standardized form. Now that can either be in the type of embeddings or the vector forms. However, if you consider the data that data science consists of, well you will have quite a lot of options.

There are so many data types that you can see such as data that is in a structured format. Semi-structured format and in the format of unstructured type. This is the main reason why you must get quality data from data science and you can even rely on the same.

Data Science Artificial Intelligence

6. The Aim


“The focus of Artificial Intelligence is to generate a process that is automated in nature. It gets the autonomy of the data model.” – as explained by Vijay Pasupulati, the CEO of OdinSchool, in one of his recent interviews.

However, the primary aim of data science is to look for patterns that are ideally not so easily visible in the data. This means, there might be a certain code or pattern that needs to be found out. Only experts can reveal such data.

However, if you consider the purpose of both these technologies, well they have their own goals, and of course, they differ from one another to a great extent.

7. Tools Used


Moving further, data science uses the tools that are quite commonly used in AI as well. The reason is clear which was stated earlier too, the data science includes different steps to analyze the data and even gather better insights from the same.

Moving further in data science, the tools that are most used are Python, Keras, SPSS, and SAS to name some. In the case of artificial intelligence, the tools that are most used is Shogun, Mahout, Kaffe, and TensorFlow Scikit-learn to name some.

8. Process and Techniques


In terms of Processes and Techniques, both technologies work in many different ways. Artificial science has a process that includes future events. These events can be forecasted with the help of a predictive model. If we consider the process of data science, there are certain steps included such as analysis, visualization, prediction, and even data pre-processing to name some.

Other than this, the technologies that are used in Artificial Intelligence consist of the algorithms in computers. It helps in solving the problem. But if you count the data science, well, there are so many statistical methods that are being used.

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As we can see in this post on Data Science vs. Artificial Intelligence, both terms are somehow used interchangeably. No doubt that if you want a broad domain then it is artificial intelligence that is yet to be explored. But if you consider data science, well this is one such field that itself uses a part of AI for creating the event occurrences.

However, it also focuses on transferring the data for further visualization and analysis. That is why, if you want to conclude at the end, well, it is the data science that can perform data analysis whereas AI is just a tool that creates the products in a better way using autonomy.

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Published By: Souvik Banerjee

Souvik BanerjeeWeb developer and SEO specialist with 20+ years of experience in open-source web development, digital marketing, and search engine optimization. He is also the moderator of this blog "RS Web Solutions (RSWEBSOLS)".