AI vs. Data Science

If you are an IT professional or a student of Computer Science, you must be familiar with the terms Artificial Intelligence (AI) and Data Science.

In this digital world, AI and Data Science are used in everyday lives and application scenarios. Though often used interchangeably, AI and Data Science are distinctly different terms, with varying techniques of programming, algorithms, and predictive modeling.

Artificial Intelligence models make machines behave more intuitively by simulating human intelligence for business goals. AI models use training datasets to implement Machine Learning and Deep Learning algorithms for predicting outcomes. However, Data Science leverages real-time and historical Big Data for data-driven insights. 

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This article discusses the differences between AI and Data Science to help you make an informed decision about your career path in the tech space.

But before we explore the difference between AI and Data Science, let us discover more about the two disciplines.

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What is Artificial Intelligence (AI)?

AI is the artificially constructed intelligence of machines that mimics human intelligence to solve business problems with speed and cognition.  It uses algorithms to perform autonomous actions and understand the patterns in the data for problem-solving. AI systems also use Deep Learning and Artificial Neural Networks to learn from given information and achieve desired goals.

What is Data Science?

Data Science is the technique of harnessing massive data for decision-making and prediction. It implements the principles of Statistics, Mathematics, and Programming to gain insights into the data for trends and patterns.

Data Science also requires knowledge of Machine Learning algorithms or AI to analyze customer behavior and build products.

AI vs. Data Science

There are many differences between AI and Data Science. A key difference is that while AI is part of Data Science technology, Data Science is a more comprehensive discipline using many tools, techniques and technologies, one of which is AI.

AI processes data autonomously using training data for iterative learning. Data Let us examine how the two differ, from their underlying operations, modeling, and processing techniques to applications and career scopes.

Here are some ways AI differs from Data Science:

  • Science discovers hidden patterns and trends in data for decisions. 
  • They differ in scope. While AI is limited to the implementation of Machine Learning algorithms, Data Science involves many more operations on the data.
  • AI models are built to mirror human intelligence. Data Science models are meant to deliver statistical insights.
  • AI builds predictive models that simulate human intelligence and reasoning to eliminate human involvement. Data Science develops models for fact-finding and predictions.
  • AI deploys a predictive model to forecast future possible outcomes and events.  Data Science includes pre-processing and cleaning data, visualization, prediction, and analysis.
  • AI implements standardized data, but Data Science conducts operations in various kinds of data, such as structured, semi-structured, and unstructured.
  • AI uses computer algorithms in computers to solve problems. Data Science implements different statistical principles.
  • AI implements the design, development, and deployment of algorithms. Data Science uses statistical techniques and data processing methods.
  • AI is to do with Machine Learning and Deep Learning. Examples of tools used are TensorFlow, Mahout, Shogun, and sci-kit-learn. Data Science uses historical and real-time data streams for data analysis and modeling. Some tools used are Python, Keras, SPSS, and R. 
  • The tools in Data Science are more than those used in AI, as Data Science tasks involve multiple steps for data preparation, visualization, and analysis.
  • AI is a tool for the Data Scientist, used along with other methods and tools. Data Science extracts data using SQL and NoSQL queries and AI tools like Deep Learning algorithms for classification and prediction.
  •  AI uses a high level of scientific processing compared to Data Science, which primarily sifts through data to find patterns and insights.
  • AI is used when you want precision, automation, minimal or nil human involvement, and speedy outcomes with logical decision-making. It is the preferred technology when tasks are repetitive, training data is available, and you want to conduct a risk analysis. You use Data Science when you need to identify patterns and trends from historical data, and speedy mathematical or statistical processing, for making a prediction.  It is the preferred discipline when you want exploratory data analysis (EDA) for predictive modeling. 
  • Some AI applications are chatbots, robots, news curation, fraud prevention, autonomous vehicles, and so on. Data Science finds applications in image recognition, website recommendations, Internet search speech recognition, identification of cyber threats, and so on.
  • If you want to build a career in AI, a sound knowledge of programming is a must-have. Expertise in algorithms is also necessary as Machine Learning differs from traditional programming. A career in Data Science focuses on Statistical techniques and Mathematics. Knowledge of data architectures and the use of processing tools. AI and Machine Learning also constitute a part of the Data Science learning curve.
  • AI engineers are in high demand, just as much as Data Scientists. However, the former has a better scope of grossing high salary packages because of the specialized expertise. The average salary of an AI Engineer is above $100,000 and can be as high as $304,500. The average salary of a Data Scientist is approximately $116,654, with experienced and senior professionals earning as much as $142,131.
  • Many companies advertise AI job roles like Machine Learning Scientists, AI engineers, Deep Learning scientists, or NLP Scientists. Data Scientist job roles can range from the Data Architect, Machine Learning Engineer, Data Science Generalist, Data Engineer, or Actuarial Scientist.

Summary

Ultimately, it is the use case scenarios that differentiate between AI and Data Science. While AI functions as a tool for modeling better products and automating systems, Data Science performs analysis of data. 

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