Fields
There are many ways we may classify the fields, subsets & roles that represent this type of work, from generalizations to granular explanations.
In some cases, professionals working in these fields may assume roles or responsibilities across multiple disciplines, thus further blurring the lines between classifications.
How can we visualize their relationship?
Data Engineering
The act of designing and implementing functional systems or architecture that acquire, organize, clean, sort, move and maintain data.
Big Data:
Managing data that is too large or complex to be dealt with by traditional data-processing application software.
Database Administration:
The function of managing and maintaining Database Management Systems (DBMS) software.
Programming:
The process of designing and building an executable computer program to accomplish a specific computing result.
Data Science
The process of extracting useful insights from data by identifying a problem, gathering, cleaning, exploring, modeling & interpreting data and communicating the results.
Data Mining:
The process of digging through prepared data to find value and actionable information using various modeling techniques.
Data Analysis:
The process of inspecting, cleaning, transforming and modeling data with the goal of discovering useful information to deliver conclusions and support decision-making. Data Analysis focuses on understanding the past; WHAT happened and WHY it happened.
Data Analytics:
The discovery, interpretation and communication of meaningful patterns in data to support effective decision-making. Data Analytics focuses on WHY it happened and what will happen NEXT.
Artificial Intelligence
Also known as A.I., is the automation of intellectual, perceptual and repetitive tasks normally performed by humans; using machine learning, deep learning, artificial neural networks and other non-learning approaches.
Machine Learning:
The scientific study of algorithms and statistical models that computer systems use in-order to train, learn, improve & perform a specific task without using explicit instructions, and instead rely on patterns and inference. Machine learning algorithms usually require structured data to function.
Deep Learning:
A machine learning method that specializes in using neural networks with many layers (i.e. Deep) through which the data is transformed. Each layer can extract features of the data at progressively higher levels from the raw input. Deep Learning can use either structured or unstructured data which makes it ideal when structured data is not available.
Neural Networks:
Also referred to as 'Artificial Neural Networks' since they are not biological, are a set of algorithms designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling or clustering raw input. The patterns they recognize are numerical & contained in vectors into which, all real-world data - like images, sound, text or time series can be translated.