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As we slowly transition from the first industrial revolution powered by water and steam to the fourth industrial revolution powered by artificial intelligence, we are on the brink of a massive technological revolution. The theories underpinning data science and machine learning have been around for hundreds of years. There was a time when a prototype computer would take almost forever to perform a billion calculations. No one dared to think of artificial intelligence or related technologies.Thanks to machine learning and data science, we can now compute a capacity of 5 billion calculations per second.
Data science and machine learning are among the most popular disciplines that evaluate and analyze big data for beneficial purposes. Whenever big data or data in general is mentioned, our thoughts go directly to data science and machine learning. Although the two disciplines are distinctly different, they share a unique symbiotic relationship. This article will explain in detail the concepts of data science and machine learning, their special relationships and examples.
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As mentioned above, our world is about to be flooded with data. Data is rapidly becoming unmanageable and cumbersome. Huge amounts of data are generated every second. The advent of the internet has pushed this development further to the edge. Everywhere you go, your data is being collected, intentionally or not – from simple gestures to automating door openings via fingerprint sensors to buying groceries from the grocery store.
Data science is the study of data and the processes involved in extracting and analyzing data to solve problems and predict future trends. Data science is a broad discipline that is interrelated with other fields such as machine learning, data analysis, data mining, visualization, pattern recognition, and neural computing, among others.
Data scientists investigate, analyze, infer and present data to solve technology-related business problems. Data science draws inferences, explanations, and conclusions from data that can be used for informed decision-making. This science builds on foundational disciplines such as statistics, mathematics, and probability. Overall, data science is about understanding data and interpreting it.
Machine learning studies data over time to create predictive models that can identify trends and solve problems without human intervention. Machine learning is a subset of data science. Using 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 range of algorithmic approaches that fall into four categories: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.
Machine learning engineers study big data to simulate machines acting and thinking like humans. Machine learning leverages foundational disciplines such as strong programming knowledge skills in languages like Python and R, as well as mathematics and data manipulation. Machine learning involves large amounts of data; machines rely on this input to gain knowledge and understanding, and act independently of human information when fully simulated. Through machine learning, the number of AI systems continues to grow as more intelligent agents are 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 bridge connecting two fields, Because both disciplines use data to solve high-level problems and make predictions.
Machine learning is a development tool for data science. Data scientists study, evaluate and interpret big data, while machine learning engineers, on the other hand, build predictive and simulation models that use deciphered data to further solve problems – for example, bookmakers.
These companies use data science to study and interpret vast amounts of data from decades of soccer games. They look at each club’s strengths, players’ skills and stability. This data is then used to build algorithmic solutions and models that can predict the outcome of these games before they even start. Calculate the odds and probabilities of that happening, and even figure out which players score points in these games and how many shots they can take. You can also predict which players will play full-time and which will come off the bench. Another great example of the symbiotic relationship between data science and machine learning is natural language processing. Data from diverse backgrounds and cultures is collected and studied by data scientists. Data machine learning engineers use this data to develop intelligent agents such as Alexa and Siri.
You can’t think about data without data science and machine learning. They perform specific activities, yet are closely intertwined. One is only complete with the other. Yes, you can perform some data analysis activities in data science, but you can only make good use of this data through machine learning.
Machine learning, on the other hand, is considered to be 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 on solving data problems. Data scientists create and clean this data by subject, analyze it and use it to solve problems. In contrast, machine learning experts study this data over time and build an algorithmic predictive model that uses this data to mimic human thinking, solve high-level problems and predict future trends.
If I may add a subtext, a data scientist would be a senior colleague of a machine learning engineer. This is because data science is more inclusive and intertwined with different aspects of technology. Machine Learning Engineers report to Data Scientists because they have the explanatory models that Machine Learning Engineers want to build. Data scientists have a futuristic view of what a predictive model should do, so it is natural for a machine learning engineer to report a clearer picture and align the model with the overall business goals for which it was built.
With an understanding of the unique symbiotic relationship between data science and machine learning, let’s look at some use-case scenarios for these powerful disciplines.
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Data Science Use Cases
Data science can be used in business for different beneficial purposes. Some of them are highlighted below:
Use Excel for simple data analysis (eg, create clusters, collect and organize data into structured and unstructured data).
Root Cause Analysis. Some organizations employ data science for root cause analysis and resolution. This is done by investigating all the collected data on the subject and tracing the root of the problem through different data analysis models/algorithms such as classification, binary tree and clustering.
Predict future trends by researching and interpreting big data
Design and deliver user/customer-centric business solutions
User-centric product development and management
expert decision making and reasoning
Establish and develop a strategic business model
Use Cases for Machine Learning
Machine learning is the driving force behind artificial intelligence. Highlighted below are some use case scenarios for machine learning:
Robot design and construction
Design and Implementation of Natural Language Processing
Design and construction of expert knowledge base and inference engine
The structure of the predictive model used to solve the problem
Simulation and construction of artificial intelligence agents such as facial recognition machines and lie detectors.
Data scientists and machine learning experts are harnessing the vast amounts of data generated every day to rapidly bring our world into the machine age. This is an age in which machines may be as smart as or even smarter than humans—an age in which devices have transcended every scientific principle. While some would argue that time is closer rather than farther than time, it’s almost here. In conclusion, data science and machine learning are the two front wheels propelling us towards the technological singularity.
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