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Data Science vs Machine Learning; Which Is Better?
Data science and machine learning are buzzwords in the technology world. Both enhance AI operations across the business and industry spectrum. But which is best?
Technology is the backbone of the world. It's evolving at an unprecedented pace and serves as an enabler across all industries. In the last decade or so, data science and machine learning have become popular terms, from tiny start-ups working on the next big app, to giants like Google, Facebook, and Netflix.
Data science and machine learning are terms that are sometimes (wrongly) used interchangeably, but they have several fundamental differences and applications.
Both terms and their functions exist as a part of Artificial Intelligence (AI). Machines use AI to make decisions, just like a human would, based on experiences and heuristics. These experiences are based on data, which is where Machine Learning (ML) comes in. Humans learn from their everyday experiences, while machines learn from data.
The data needed for machine-based learning comes from big data. One organization alone can produce petabytes of data in a short timeframe. While the accessibility of cloud-based storage makes it easier to store data, the problem now is squarely on the processing of this data to make better business decisions. Data science and machine learning play a critical role in this process.
Modern AI can take massive amounts of data, and analyze and process it to unearth patterns of human consumption and behavior, or to answer other questions a business may want to explore. Data science powers the data analysis performed by machines, providing all the inputs needed to create relevant algorithms and models. Simply put, data science utilizes various algorithms, protocols, and methods to extract insights from raw data.
With this understanding of data science and machine learning, it’s now easier to understand their differences.
The Differences Between Data Science vs. Machine Learning
Data science and machine learning each have practical applications that are quite different. However, both are used to carry out everyday activities — some which happen millions of times a day, such as online shopping.
Consider a business called ABC that’s selling a new product, such as a pair of sunglasses. The sunglasses are readily available from Company ABC — but also a range of competitors. When a potential customer visits ABC’s website for the first time and browses through all the versions of sunglasses that are available, they often use filters provided by ABC to narrow down options based on their preferences. Common filter options include size, color, price, and style.
After filtering the sunglasses by features, the customer is left with three options that fit their criteria. Once the customer makes a choice, they might add it to their cart.
ABC’s website will then offer various options and recommendations to the potential customer based on their preferences and insights gained from processing vast amounts of big data. Customers might see additional products listed under headlines such as, “We Also Recommend”, or “Customers Who Bought This Also Bought”. These recommendations are based on information gathered from millions of previous purchases.
Purchasing a tablet? You might want to buy a new case or an extra-long charging cable.
The suggestions not only provide the customer with helpful products, they also provide the business with a successful upselling model. This is data science: The entire process of collecting, sifting, processing, extracting actionable trend patterns, and creating a model to arrive at an answer to a question. In this case, the model provides the customer with better alternatives or may influence them to buy a related product.
The model, on the other hand, is the machine learning function. Data scientists build the model with algorithms that convert data into a learning experience — in this case, providing customers with recommendations based on their search criteria. These models enable a machine to learn what product options to show a new customer based on knowledge gleaned from earlier purchasers. It makes a suggestion based on its “experience” from the provided data.
The example above is just one example of an ML application, but there are millions more for every industry — from medical and research fields to retail and insurance.
For example, in Fintech, ML is used to predict a range of behaviors. It analyses transactions in real time and identifies complex patterns that predict fraudulent behavior. ML also assesses past financial transactions from individuals during the loan application process. It combines knowledge gained from previous loan defaulters and uses them to make accurate predictions about the likelihood of someone paying their loan as agreed.
And this segues into data modeling — the next stage of machine learning within the data science cycle.
The quality of the model determines how much the machine learns about customer buying habits. The better the model, the better the machine can predict future decisions. The ideal machine model ensures progress for both the business model and the learning process of the machine, which leads to businesses seeing an improvement in targeted outcomes.
Data science deals with the visualization of processed data based on certain parameters, enhancing business decisions. Machine learning places the spotlight on enhancing its experience, from learning algorithms and from learning derived from its experience with data in real-time. Data will always remain central to data science and machine learning.
Comparing Data Science and Machine Learning
With this understanding of their application in real life, this is how these two concepts differ from each other.
|Data Science||Machine Learning|
|Data science revolves around processes and protocols to extract data from sources that are structured (like names, ages, locations, and addresses) or unstructured (qualitative data such as social media posts, audio-video files, and text). It involves many disciplines and advanced analytics.||Machine learning is a process that enables computers to learn from processed data to create a working model for a specific requirement, without being programmed to do so. It fits within the data science universe and primarily needs structured data to work with.|
|Data science involves the entire gamut of processes associated with analytics.||Machine learning is a specific process within data science. It uses techniques such as regression and supervised clustering.|
|Data science can work with manual processing methods, though with reduced efficiency compared to machine-based algorithms.||Machine learning cannot exist without data science. Data must be collected, cleansed, and analyzed in order to create a model.|
|Data science is not classified as an AI subset. It is a complete process in itself.||Machine learning is not only an AI subset but also acts as a conduit between data science and AI. It is constantly evolving with the processing of data. It is a step within the data science process.|
|Data science is used to analyze data and unearth patterns and insights that prove useful to a business that is looking to improve its products and customer services. It enables smart business decisions.||Machine learning treats the patterns that are found through data science as learned experience, based on which it creates models for a company to apply to its processes. These models classify new data that comes in and make related predictions based on their experiences.|
|In terms of applications, data science has vast potential and applies to several fields.||Machine learning remains within the data modeling stage, which is part of data science.|
|Data science enables a business to identify problems that were so far unknown, allowing them to work towards a solution.||Machine learning always focuses on a problem that is known. All its related tools and techniques are used to come up with an intelligent solution model.|
Choosing Between Data Science and Machine Learning
How does a company choose between data science and machine learning? The answer is that an organization can’t have one without the other. Both these processes are a part of each other. Machines can’t gain experience without data, and data is always better analyzed when processed within the standards of data science. In the future, specialists such as data scientists and machine learning engineers will need to have at least a working understanding of each other’s fields to improve the quality of the work that they do.
As AI increasingly becomes essential for organizations to succeed in the real world, data science and machine learning both have the spotlight on them. Advancement in the field is moving into deep learning, a part of AI and a subset of machine learning. Modeled on the way the neurons of the human brain fire and function, deep learning makes use of digital neural networks to operate. It offers multiple layers of solutions to solve complex business challenges. Self-driving cars are a great example of deep learning. Sources of data are constantly expanding and the need to collect and analyze it will continue to grow.
How To Capitalize On Data Science and Machine Learning In Your Organization
Your organization needs data science and machine learning to remain competitive, relevant, and productive. The insights gained from application of data science principles can guide the organization forward into the future; accurate predictions allow data informed decisions that guarantee results. If your organization has amassed data that it doesn’t know what to do with, or if you’re falling behind the competition, Alteryx will give you the data science jumpstart you need.
Start today to realize the benefits of data science and machine learning in your organization.