Data science, AI, and ML – what’s the difference?

You may have heard the terms data science, AI, and ML used interchangeably, but they are distinct concepts with unique characteristics.

AI is a broad field that focuses on developing computer systems that can perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. ML is a subset of AI that involves training computer systems to learn from data and improve their performance on a specific task without being explicitly programmed.

ML algorithms enable computer systems to learn from data and identify patterns, which can then be used to make predictions or decisions. While all ML falls under the umbrella of AI, not all AI encompasses ML, as some AI systems may rely on rule-based or symbolic reasoning approaches.

Deep learning is a specific type of ML that utilizes artificial neural networks with multiple layers to extract higher-level features from raw data. This technique is highly effective for tasks such as image and speech recognition.

Data science is a multidisciplinary field that involves extracting and analyzing relevant insights from data. It focuses on discovering hidden patterns and relationships in data to derive meaningful conclusions. A data scientist leverages ML algorithms to make predictions and inform decision-making.

All these fields are grounded in the foundations of mathematics, probability theory, and statistics. Understanding these core concepts is essential for anyone interested in pursuing a career or leading initiatives in data science, AI, or ML.

The following is an attempt to visualize the relationship between these fields:

Figure 1.1: A visual representation of the relationship between data science, ML, and AI

Here, deep learning is a subset of machine learning, and artificial intelligence is a broader field which includes machine learning and other methods to perform intelligent tasks.

Data science, as a practice, overlaps with all these fields, as it can make use of whichever methods are most appropriate to extract insight, predictions, and recommendations from data.

All these fields are built upon the foundation of mathematics, probability, and statistics. For this reason, in the next section, we will investigate these mathematical and statistical underpinnings of data science.

The mathematical and statistical underpinnings of data science

This book is aimed at the business-focused decision maker, not the technical expert, so you might be wondering why are we starting by talking about mathematics.

Well, at its core, data science is based on mathematical and statistical foundations, so even if you aren’t working as a data scientist or ML/AI engineer, having a basic understanding of the important mathematical and statistical concepts that are used within data science is one of the most important tools you can have at your disposal when working with data scientists or leading data science, ML, or AI initiatives, whether that’s interpreting the models and results that data scientists and ML engineers bring your way, better understanding the limitations of certain data and models, and being able to evaluate which business use cases may or may not be appropriate for data science.

Research has found that 87% of data science projects never make it into production. In other words, only around one in ten projects get to the stage where they can provide bottom-line value for a company.

These results seem poor at first glance, but there is a silver lining. In many cases, the missing piece of the puzzle is strong executive leadership, knowing which use cases are appropriate for data science, providing the data science teams with good-quality, relevant data, and framing the use cases in a way where data science can be applied successfully.

Knowing some of the core concepts around mathematics and statistics for data science will not only give you a better appreciation of data science but also the compass to plan and navigate data science projects from the outset to reach more successful results.

Within this book, we won’t be attempting to provide anything like a comprehensive foundation into mathematics required for AI and ML as this would require an entire degree to achieve. However, within this chapter, we will provide you with an understanding of the fundamentals.