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We are on the brink of a massive technological revolution as we slowly move from the water and steam-powered first industrial revolution to the artificial intelligence-powered fourth industrial revolution. The theories backing data science and machine learning have existed for hundreds of years. There used to be times when proto-computers would take almost forever to compute a billion calculations. No one dared think of artificial intelligence or related technology. All thanks to machine learning and data science, we can now calculate data at a capacity of 5 billion calculations per second.
Data science and machine learning are amongst the most popular disciplines that evaluate and analyze big data for beneficial purposes. Whenever big data or data, in general, is mentioned, our minds go straight to data science and machine learning. While both disciplines are noticeably different, they have a unique and symbiotic relationship. This article will explain in detail the concepts of data science and machine learning, their special relationship and practical examples.
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The science of data
As mentioned above, our world is about to be overrun by data. Data is fast becoming overwhelming and tedious to manage. Tons and tons of data are being generated every second. The advent of the internet further pushed this development to the edge. Everywhere you go, your data is being collected knowingly and unknowingly — from gestures as simple as opening a door through fingerprint sensor automation to shopping for groceries from a grocery store.
Data science is the study of data and the processes involved in extracting and analyzing data for problem-solving and predicting future trends. Data science is a broad discipline that is interconnected with other fields, such as machine learning, data analytics, data mining, visualizations, pattern recognition and neurocomputing, to mention a few.
Data scientists investigate, analyze, infer and present data that solve technology-related business problems. The science of data draws inferences, interpretations and conclusions from data that can be used for informed decision-making. This science is built on fundamental disciplines like statistics, mathematics and probability. In all its entirety, data science works to understand data and interpret it.
Machine learning studies data over time to create predictive models that can discern trends and solve problems without human intervention. Machine learning is a subset of data science. Through algorithms and development tools, machine learning engineers build expert systems that can be taught to work independently without human intervention. This is achieved through a series of algorithmic approaches divided into four categories: supervised, unsupervised, semi-supervised and reinforcement learning.
Machine learning engineers study big data to simulate machines to behave and think like humans. Machine learning utilizes fundamental disciplines like strong programming knowledge skills in languages, like python and R, as well as mathematics and data processing. Machine learning is extensive on data; machines rely on this input to gain knowledge and understanding and also to act independently of human information after complete simulation. Through machine learning, artificially intelligent systems continue to grow in numbers as more intelligent agents are being developed.
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The relationship between data science and machine learning
The relationship between data science and machine learning is symbiotic. They work hand in hand. Data is the big link bridge between the two fields, as both disciplines use data for advanced problem-solving …….