AI / Data Science / Machine Learning / Deep Learning / Data Analysis / Data Engineering / Big Data

1. Data Science / Data Analysis

Simply put, a data analyst makes sense out of existing data, whereas a data scientist works on new ways of capturing and analyzing data to be used by the analysts. ... Although each role is focused on analyzing data to gain actionable insights for their organization, they're sometimes defined by the tools they use.

2. Data Science / Machine Learning 

At its core, data science is a field of study that aims to use a scientific approach to extract meaning and insights from data. ... Machine learning, on the other hand, refers to a group of techniques used by data scientists that allow computers to learn from data.

3. Machine learning / Artificial intelligence

Artificial intelligence is a technology which enables a machine to simulate human behavior. Machine learning is a subset of AI which allows a machine to automatically learn from past data without programming explicitly. The goal of AI is to make a smart computer system like humans to solve complex problems.

4. Data Engineering

Data engineers are expected to know how to build and maintain database systems. Data engineering is the complex task of making raw data usable to data scientists and groups within an organization. ... In addition to making data accessible, data engineers create raw data analyses to provide predictive models and show trends for the short- and long-term.

5. Deep learning / Machine learning

Deep learning is just a subset of machine learning. Deep learning can analyze images, videos, and unstructured data in ways machine learning can't easily do. Deep learning applications use a layered structure of algorithms called an artificial neural network.

All in one picture

More:

1. https://github.com/Rustam-Z/machine-learning-area
2. https://github.com/Rustam-Z/deep-learning-notes
3. https://github.com/Rustam-Z/data-engineering-python