The differences between data science machine learning and deep learning

Data science is a broad field that spans the collection, management, analysis and interpretation of large amounts of data with a wide range of applications. It integrates all the terms above and more to summarize or extract insights from data (exploratory data analysis) and make predictions from large datasets (predictive analytics).

The field involves many different disciplines and tools, including statistical inference, domain knowledge (expertise), data visualization, experiment design, and communication. Data science helps answer the question “what if?” and it plays a crucial role in building ML and AI systems, and vice versa.
Deep Learning
Deep learning is one of many approaches to ML. It implements an Artificial Neural Network (ANN), which has multiple layers between its input and output layers. The “deep” in deep learning refers to the many layers in a network that allows for more complex processing
Machine learning
Machine learning essentially is a building block for AI. By doing machine learning, you are teaching a machine to learn how to perform a task, such as image recognition, recommender systems, fraud detection, etc.