Data Science vs. Machine Learning

We often see that machine learning and data science are used interchangeably. Interestingly, there are some significant differences between these two concepts. In this lesson, we’ll discuss their relationship, similarities, and differences. Let’s start by revisiting their definitions.

Defining data science and machine learning

Data science involves various tasks to discover important insights from large and complex datasets. This includes collecting, organizing, and analyzing data and using statistics to understand a specific problem. Data scientists apply their skills to define and solve real business problems, using tools like statistics, data modeling, and data analytics to make informed decisions based on data.

Machine learning, on the other hand, aims to make machines learn from data by creating algorithms and models. This helps machines become smarter, make predictions, and learn independently. Unlike data science, machine learning doesn’t need a lot of human help. It deals with big data to make predictions and improves through learning.

Machine learning is a subfield of AI that deals with the required preprocessing, training, and testing of a model on the given data. We can say that it’s one of the most important parts of the data science project. In summary, data science involves data collection, cleaning, modeling, analyzing, and decision-making, while machine learning is a specialized subset of data science that focuses on building models.

Key differences

Let’s look at the differences between data science and machine learning in the following illustration: