Data Science vs Artificial Intelligence: Understanding the Difference

While Data Science is closely related to the Artificial Intelligence field, there are many differences between the two disciplines. The differences are mostly in terms of their scope, methodologies, and objectives. There are also different applications of Data Science and AI. So to explore the differences between the two fields, let’s go through this comparison between Data Science and Artificial Intelligence.

What is Data Science?

Data Science relates to a discipline that analyzes data using various statistical techniques to draw insights and knowledge. It includes structuring and cleaning the data, running data analysis, generating visualization to communicate the insights better, and building predictive modeling based on the given data.

Data scientists also use various programming languages such as Pythion, R, and SQL which are core to statistical computing. Not just that, for predictive modeling, data scientists also use deep learning techniques which make it closely related to Artificial Intelligence. From forecasting sales trends to sentiment analysis, deep learning is widely used in Data Science.

Basically, the objective of data science is to find correlations, hidden patterns, trends, and anomalies that can help in robust, data-backed decision-making. Its application is seen in various domains such as marketing, finance, healthcare, and social sciences.

What is Artificial Intelligence?

Artificial Intelligence (AI) is a broad term, and it’s a subset of machine learning. AI relates to building intelligent models that can perform tasks similar to humans showcasing reasonable intelligence. General-purpose AI models are not only trained on structured data, but also on texts, images, videos, code, and more.

This allows AI models to perform several natural-language tasks and infer meaning from data, beyond just data analysis. AI models are not just trained for data analysis but still, they can reason with data and extract key insights, just like humans. It’s trained on massive data in a neural network to create an intelligent system.

In the context of data analysis, AI models can also be used to draw out insights and knowledge in several domains. However, the difference lies in their methodologies and objectives.

What’s the Difference Between Data Science and AI

As I said above, Data Science employs statistical methods to analyze data and infer insights. Methods such as regression analysis, hypothesis testing, and predictive statistics are some of the common statistical techniques. This leads to data-driven decisions and predictions.

Whereas AI aims to create an intelligent model, mimicking human intelligence which can be used to extract insights from both structured and unstructured data. AI is trained using large-scale neural networks, and it’s not limited to data analysis. ML researchers work with frameworks like TensorFlow, PyTorch, Keras, etc. to train the model.

Apart from that, in terms of objectives, Data Science’s scope is limited to data interpretation and data visualization, leading to better decision-making. On the other hand, an AI system is aimed at creating an overall intelligent model that can learn, reason with data, and make decisions.

Applications of Data Science and AI

Data Science is largely used in finance for sales trends, decision-making, risk assessment, and fraud detection. In marketing as well, it’s used for analyzing customer behavior to create personalized strategies tailored to customers. Besides that, data science is used for predicting weather conditions, stock prices; forecasting disease outbreaks in healthcare, and more.

On the other hand, AI is currently being used for many Generative AI applications. It can be used to create customer chatbots, natural language processing to create new content, data analysis and interpretation, diagnosing diseases using medical imaging, and more. Its application is pretty wide and we are slowly seeing AI being used in almost all the fields.