Data Science and its Nearest-Neighbours

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

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

In plain English, machine learning is ‘automating learning from data’. In broader terms, machine learning is a subset of data science.

Deep Learning

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

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

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

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.

Data Science Architect | ankitrathi.com/blog

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