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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 and prediction.
Machine learning is a development tool for data science. Data scientists research, evaluate and interpret big data, while machine learning engineers, on the other hand, build predictive and simulative models that use decrypted data to further solve problems — for example, the betting companies.
These companies use data science to study and interpret tons of data from decades of football games. They observe each club’s strengths, the footballers’ skills and consistency. This data was then used to build algorithmic solutions and models that predict the outcome of these games even before they are played. The odds and probability of occurrence are calculated even down to which player scores in these games and the number of shots that could be fired. You can also predict which player will be featured full-time and who will be played as substitutes. Another excellent example of the symbiotic relationship between data science and machine learning is natural language processing. Data from different backgrounds and cultures were collected and studied by data scientists. The data machine learning engineers utilized this data in the development of intelligent agents such as Alexa and Siri.
You can not think of data without data science and machine learning coming to mind. They carry out specific activities but are strongly interwoven with each other. One is only complete with the other. Yes, you can perform some data analytics activities in data science, but you can only fully utilize that data with machine learning.
On the other hand, machine learning is supposedly based on building models with this data rather than interpreting it, which can only happen with big data. Both disciplines work with data and work to solve problems with data. Data scientists create and clean these data, analyze them and use them for problem-solving, according to the subject matter. In contrast, machine learning experts study these data over time and build an algorithmic predictive model that uses these data to mimic human thinking, solve advanced problems and predict future trends.
If I may add a subtext, a data scientist would be the senior colleague of a machine learning engineer. This is because data science is more encompassing and interwoven with different aspects of technology. A machine learning engineer would report to a data scientist because they have the interpreted model of what the machine learning engineer wants to build. The data scientist has a futuristic view of what the predictive model should do, so naturally, the machine learning engineer should report for a clearer picture and alignment of the model with the entire business objective of building the model.
Having seen the unique and symbiotic relationship between data science and machine learning, let’s look at some use-case scenarios of these power disciplines.
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Use cases for data science
Data science can be used in business for different beneficial purposes. Some of them are highlighted below:
Simple data analytics with Excel (eg, creating clusters, data collection and organization into structured and unstructured data).
Root cause analysis. Several organizations adopt data science for root cause analysis and resolution. This is done by investigating all collected data on the subject matter and tracing down the root of the problem through different data analysis models/algorithms like classification, binary trees and clustering.
Prediction of future trends by researching and interpreting big data
Design and delivery of user/customer-focused business solutions
User-centered product development and management
Expert decision-making and inferences
Building and development of strategic business models
Use cases for machine learning
Machine learning is the propelling force behind artificial intelligence. Highlighted below are some of the use case scenarios for machine learning:
Design and construction of robotics
Design and implementation of natural language processing
Design and building of expert knowledge databases and inference engines
Structure of predictive models for problem-solving
Simulation and construction of artificially intelligent agents (eg, facial recognition machines and lie detectors).
Data scientists and machine learning experts are using the plethora of data produced daily to move our world rapidly into the machine age. Here is an era where machines might be as intelligent as human beings or even more intelligent than human beings — a time when devices have evolved beyond every scientific principle. While some believe that time is much closer than farther, it is almost here. In all, data science and machine learning are the two front wheels that are moving us toward singularity in technology.