I started my journey into data science in 2012, at that time data science, machine learning, and artificial intelligence, all these terms looked similar to me. It took me some time to understand the nuances of these similar terminologies.
I still see newbies and enthusiasts getting confused between these terms. In this article, I am going to cover my understanding of these terminologies and how these similar-looking fields are different. So let's get started…
Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains. ~ Wikipedia
In simple terms, data science is ‘answering questions using data’. We can also say that data science is applying scientific methods on data to get meaningful insights.
Machine learning is the study of computer algorithms that improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence. ~ Wikipedia
In plain English, machine learning is ‘automating learning from data’. In broader terms, machine learning is a subset of data science.
Deep learning is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised. ~ Wikipedia
Simply put, Deep learning is mostly used with certain kinds of neural networks, the structures that are organized in a way that there is at least one intermediate layer (or hidden layer), between the input layer and the output layer.
Basically, deep learning sits inside of machine learning, which sits inside of artificial intelligence.
Artificial intelligence is intelligence demonstrated by machines, unlike the natural intelligence displayed by humans and animals, which involves consciousness and emotionality. The distinction between the former and the latter categories is often revealed by the acronym chosen. ~ Wikipedia
In layman's terms, artificial intelligence is ‘mimicking human intelligence of learning and problem-solving’. Machine learning is a subset of artificial intelligence in terms of gaining intelligence from data, while data science and artificial intelligence fields overlap to some extent.
Data mining is a process of extracting and discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. ~ Wikipedia
In simple terms, data mining is ‘manually discovering and extracting patterns’, which can be further used by data and analytics systems. Compared to data mining, data science is a broader field where we try to ‘answer interesting questions’ by ‘discovering and extracting patterns’ from data.
Business intelligence comprises the strategies and technologies used by enterprises for the data analysis of business information. BI technologies provide historical, current, and predictive views of business operations. ~ Wikipedia
In a nutshell, business intelligence is ‘interpreting past data’. If we compare business intelligence with data science, we extrapolate patterns from past data to make predictions for the future.
So we have covered most of the nearest neighbors of data science and learned the similarities and differences. I hope you now understand the nuances and will be able to explain the difference between these terminologies better.
Stay tuned for more interesting topics related to data science in the future.
Ankit Rathi is a Principal Data Scientist, published author & well-known speaker. His interest lies primarily in building end-to-end AI applications/products following best practices of Data Engineering and Architecture.