Data Science vs Artificial Intelligence: What's the Difference?

Rajesh, a thrill-seeking 22-year-old boy, had just finished his degree. He was out on a solo road trip to Jodhpur from Bangalore in his brand new Mahindra Thar. As he was shuffling through different radio stations, he stumbled upon an interesting podcast about data science. This was so relevant to him as he was looking to start his career as a data scientist by taking up a data science course.

Introduction

Viswa hosted this data science podcast, and the guest was Mr. Ananth Tambre, a renowned data science and generative AI industry thought leader and CEO of Fester Technologies. Ananth started off by briefing the listeners about AI and data science being umbrella terms for methods and techniques related to understanding digital data. As organizations rely on gathering information from systems on all aspects of everyday life, there is an increase in demand for skilled data scientists who are well-versed in generative AI. This is because AI integration with data analytics applications has been proven to improve operational efficiency.

Ananth continued to shed light on how data science combines the use of tools and technology to process a large volume of audio, video, text, and images to resolve cognitive problems associated with human intelligence. When data science works hand in glove with AI, deep learning, and machine learning, this helps in resolving complex issues by predicting various outcomes to make organizations prepared to mitigate risks and potential threats.

Data Science and AI, How are they Similar?

Viswa was intrigued by the connection between AI and data science and asked how they are similar. Ananth explained the similarities in predictive applications of AI and data science technologies. Both technologies apply methods and models to make predictions based on analyzed data. Here’s a gist of the similarities.

•   ● AI and data science technologies need to be trained with consistent data to provide consistent results
•   ● They filter new data which is not in the original dataset
•   ● They prioritize certain appropriate attributes in the dataset over others if the input data lacks variation
•   ● They create fictional information (assumptions) if the input data is false.

Key Difference between Data Science and AI

Viswa asked Ananth about the key differences between AI and data science. Ananth explained that data science analyzes data to determine patterns that help in making predictions. He emphasized the need to understand applied data science, where models and methods used in data analysis are applied in real-world situations to provide suitable outputs. This is where AI differs from data science, as AI uses algorithms to run complicated machine-based systems that resemble human intelligence. Here are the key differences between AI and data science.

Data Science	AI
● Less scope with a pre-defined outcome ● Confined to data collection, pre processing and uses models and algorithms to interpret results	● Wide-scope of applications and helps in identifying manual tasks and optimizes them ● Performs exploratory analysis and divides complex tasks into algorithmic components, forming a system

Applications of Data Science and AI

Thank you for explaining the core difference between data science and AI, this was quite insightful, could you please shed light on its applications, Viswa asked. Sure, with pleasure, said Ananth.

The applications of data science and AI is truly vast and nearly endless as it is used across several industries like healthcare, e-commerce/retail, BFSI, business, HR and more. Here are some of the core applications.

Applications of data science	Applications of AI (Gen AI)
● Used in the business for forecasting sales demand and optimizing revenue ● Used in BFSI for fraud detection and assessing risks ● Used in HR for candidate screening and forecasting resource consumption	● Used in manufacturing for implementing robotic production lines and predictive maintenance ● Used in retail and e-commerce for optimizing customer service through chatbots and marketing personalization ● Used in corporate organizations for biometric recognition ● Used in healthcare for medical imaging analysis

Career Prospects of AI and Data Science

Viswa was impressed by the wealth of experience that Ananth had shared in the podcast and said, “Thank you so much for sharing your knowledge with us, now that our listeners have a clear picture of the difference between data science and AI, could you please share a few tips on how a fresher or an aspiring data science professional can boost his career?”

“Certainly, due to the vast applications of data science and generative AI across several industries, several organizations are on the lookout for candidates who are well-versed in data science and generative AI. This will make young professionals stand out from the competition and become a nearly indispensable resource in the industry that they are working for. Did you know that the global demand for data scientists is predicted to increase by 200% in 2026? With the wide scope for gen AI and data science jobs, you can start by taking up the data science course and generative artificial intelligence course at Eduinx, a renowned institute in the edtech industry.”

Eduinx provides the right platform for aspiring data scientists and gen AI professionals to upskill in their careers and land their dream job. Whether you are a fresher or an experienced professional, you can get mentored by industry thought leaders and experienced mentors who are non-academicians. Take up the data science with generative AI course and kick-start your career.

Rajesh had just parked his car at Mehrangarh fort in Jodhpur and explored the place. He felt a sense of satisfaction as the podcast with Mr. Ananth had provided him with clarity to start his career as a data scientist. He immediately reached out to EduinxEduinx and registered for their data science with generative AI course.