DS vs AI
  • Introduction
  • What is Data Science?
  • How Does Data Science Relate to Statistics?
  • What is Artificial Intelligence? 
  • What is Machine Learning?
  • How does a machine learn and work?
  • What is the difference between data science, machine learning, and artificial intelligence?
  • Takeaway



Data Science Versus Machine Learning And AI (What is the connection between Data Science, Machine Learning, and Artificial Intelligence?)

The terms, “data science, machine learning, and artificial intelligence,” are often used interchangeably. But what do they really mean?  Or are they completely different? For example, does data science mean using artificial intelligence (AI)? If yes, how can we use AI in data science?

This blog will first define the terms “data science” and “machine learning.” It shall then explain what artificial intelligence is and if and how it relates to data science and machine learning.


What is Data Science?

The term “data science” refers to the use of statistics, data mining, machine learning, and computer programming to analyse large data sets (typically thousands or millions of records) from a wide variety of sources. 

Data scientists use statistical methods such as regression/discriminant analysis, classical test theory, factor analysis, multiple linear regression, canonical correlation analysis, cluster analysis, multivariate analysis of variance (MANOVA), and discriminant function analysis to analyse data.


How Does Data Science Relate to Statistics?

Data science is not a statistical term because it does not refer exclusively to the field of statistics or statistical techniques. In fact, data science can be considered as an umbrella of Statistics, Machine Learning, Data Mining, and Artificial Intelligence

Data science is closely related to statistics or statistical methods because it uses a variety of statistical techniques as much as possible in its analyses and tries to come up with meaningful results from different kinds of data.


What is Artificial Intelligence?

It is very difficult for those who have been working on AI to define what Artificial Intelligence really is. This is because there is not just one AI, but rather many AIs. In general, we can say that the purpose of AI is to try and make computers act like humans do by learning from experience.  

AIs have the ability to learn independently and use that information for different purposes. They can make decisions based on their experiences, which is called machine learning. AIs progress through the processes of natural selection, Darwinian evolution to improve themselves with data and information just like humans do. 

The AI approach includes rule-based systems which work through predefined rules and processes, like the ‘if..then’ statements in a simple computer program. Rule-based is non-adaptive – it needs human intervention to change its own rules if they are incorrect or inadequate. 

For example, when you play Kishore Kumar’s songs on YouTube they may recommend old songs or related searches the next time you log into your account. Food delivery services, like Swiggy and Zomato, store your data automatically after your order. Next time you order food, based on your preference of food (including vegetarian or non-vegetarian), recommendations will be available for you.

In any case, ensure that you have enough information for AI to learn from. If you have an exceptionally small dataset which you are using to prepare your AI model, the precision of the prediction or decision could be lower. So, the more information, the better is your preparation for an AI model. This will ensure that predictions are as accurate as possible. The calculations you can perform on your dataset depend on the size of the data. Here, AI and Deep Learning play a significant role.


What is Machine Learning?

(ML) is viewed as a subset of AI.  ML comes with the use of algorithms which are a step below complex AI models. ML is a set of techniques. These techniques are then applied to solve problems and make decisions for specific tasks efficiently.

In simple words, ML solves two basic factors:

  • Apply a math process to the datasets (obtained from raw data) using models. It gives you a model of your data.
  • Apply algorithms to the datasets obtained from the basic math process. Generate a model which is capable of taking new data and making decisions based on these datasets i.e, for classification and identification problems, we get an algorithm that takes in raw data and provides a label (classification) or a value (identification).

Stepping back into the whole AI-ML equation, we can broadly define AI as somewhat of a more advanced and complex version of ML. As mentioned before, you can take any ML model and tweak it to make it an AI model.


How does a machine learn and work?

There are several methods for making a machine learn:

  1. Supervised learning,
  2. Unsupervised learning, and 
  3. Reinforcement.

Some of these strategies mention the difference between dependent and independent variables. The machine learns which variables are linked to each other by the data given. This information that is given is known as the preparation set. In short, machine learning focuses on algorithms and gathering insights. It makes predictions using data.


What is the difference between data science, machine learning, and artificial intelligence?

The terms ‘Data Science,’ ‘machine learning‘ and ‘artificial intelligence‘ are often used interchangeably. However, these three disciplines have some key differences. 

While these terms are key to the advancement of AI, they have been traditionally defined differently. While some use different definitions than those given here, most people understand these terms in ways that closely align with this breakdown:

Data Science provides insights through Machine Learning. Other fields that can help with Data Science are Cloud Computing and Big Data Analytics. It provides you with a pragmatic approach to machine learning and focuses on using the learned skills to solve real-life problems.

Data Science is the application of artificial intelligence (AI) and algorithmic processes to design software and create predictive models on top of large data sets. Data scientists use machine learning techniques like clustering or neural networks, but their focus is on the application of the techniques to solve business problems.

The implications of Artificial Intelligence and machine learning are vast. The technology will provide many solutions to previously intractable problems, but will also raise many new questions that society will need to deal with.

Artificial Intelligence involves the use of artificially intelligent computer systems for tasks that typically require human intelligence (such as language translation, finding patterns in large data sets, etc.). Machine learning is a subfield of AI involving algorithms that improve with examples and experience.

Artificial Intelligence is a general term used for technologies that possess some degree of machine learning and/or natural language processing. AI is not limited to any one field, but rather can be used in a variety of ways.

One example of artificial intelligence would be Apple’s Siri or Amazon’s Alexa; these virtual assistants utilize machine learning algorithms to parse spoken voice commands and return results based on what they understand.

Another example of AI is Google’s DeepMind, a machine learning algorithm that can learn for itself how to play Atari video games by watching an expert player. This technology has the potential to impact many different industries and sectors and will be discussed later in this article.

One could also argue that is inherently a form of artificial intelligence. 

Machine learning is a subset of AI that relies on a different range of activities. It is, in fact, the only real artificial intelligence. And some of its applications deal with real-world problems. In short, it is responsible for predicting. Machine learning algorithms are designed in a way that they can “think” for themselves to find patterns and anomalies in large data sets. 



Any number of blog posts or newspaper articles will not be enough to learn about these trending technologies. The terminology of each field is so broad that it takes years of research and experience working in their fields to fully understand their concepts.

The latest advances in tech – like Siri, Alexa, and automatic cars – have the potential to be seen as a blessing or a bane. Some people argue that these devices have made humans lazy. And some quote to back themselves up saying, “These technologies are life-changers for us.” 

Data science, machine learning, and artificial intelligence are the next big things in a world of technology and science that are ever-evolving.

There is no better time than now to start your career in Data Science, Business Analysis or AIML. 

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