Machine learning and data science are often either used in conjunction or interchangeably, but there are significant differences between the two disciplines. Data science is more concerned with processing large reams of data to generate better insights for business purposes, and machine learning concerns itself with implementing algorithms so that machines can find and extract certain patterns from given data.
While they have similarities, they are separate fields that utilize different techniques to meet different ends. Many companies employ both data scientists and machine learning engineers to achieve their data analysis needs better. An example of such a company is https://litslink.com/services/machine-learning-services.
What is Data Science?
Data science is a broad name for multiple disciplines, one of which is machine learning. Data science is essentially the umbrella of data analysis under which machine learning resides.
Data science processes and analyzes information that is generated from insights that serve business purposes or goals. Every time someone logs into Amazon and starts browsing through products, they are generating data. That information is then used by data scientists on the backend to understand your behavior better and why you looked at what you did. They can then push targeted advertisements at you to convince you to buy what you looked at. The targeted advertisement process is one of the more simplistic processes of data science. The field can get much more complex, like when it’s used to analyze cart abandonment on eCommerce platforms.
Altogether, data science encompasses data extraction, data cleansing, analysis, and visualization. Data scientists use all these procedures to generate actionable insights on the data set. The goal is to make connections and predictions, and shed light on user behavior. Data science is a necessary discipline for companies of all sizes to gain new customers, retain old customers, and stay relevant in their industries.
Data scientists also incorporate techniques outside of data analysis, including data integration, data engineering, visualization, deployment, and using the information to make business decisions.
What is Machine Learning?
Machine learning is a subset of artificial intelligence where groups of data-driven algorithms allow software applications to predict outcomes or forecast trends without the explicit need for programming. There is a human element involved, namely machine learning engineers who use their expertise to develop algorithms and further analyze the given data.
Machine learning uses a different approach to statistics compared to other fields that fall under data science. Machine learning is the practice of enabling algorithms to extract information from anywhere, analyze it, and then use the data to forecast upcoming trends. The technology is currently used on social media platforms like Facebook, where machine learning tracks what you’re doing, determines what you’re interested in, and puts information that matches your trends onto your home page. Machine learning is also on sites like Netflix, where it predicts what movies and shows you’re most interested in based on what you’ve already watched.
Algorithms can quickly and efficiently find and remove data on what people do online and then predict what they’re going to do next. The power behind those algorithms is machine learning. Conventional machine learning software applies predictive and statistical analysis to find patterns based on sets of perceived data. Supervised clustering and regression are applied to ensure the data is processed accurately.
In machine learning, the software uses various aspects like statistics and algorithms to analyze and process data patterns. The machine itself can produce the algorithms needed to remove and analyze data. The process does not always require the intervention or programming function of a human. Many devices can improvise and improve on the learning programs by themselves.
What is the difference?
Data science, again, is a vague term that covers many things, not just one area of data analysis. Machine learning, as a discipline, fits inside of data science, and the main difference between them is that data science is more focused on algorithms and statistics, including the full data lifecycle, while machine learning solely focuses on data extraction and forecasting.
What often happens is that data is extracted in such large volumes that it becomes entirely too much for a data scientist to handle alone. The information is then handed over to a machine, which can learn from and process data without human interaction. Again, machine learning utilizes complex algorithms, regression, supervised clustering, and naïve Bayes to meet its needs.
While there are many similarities between machine learning and data science, they are separate entities when it comes to data analysis. However, data science includes machine learning under its umbrella of associated fields and processes, and machine learning cannot function without data science.
It’s easy to get the two intermingled or use the terms interchangeably, but each field has its own processes and techniques that make them individual practices. Data science is a sophisticated technology field that finds and implements information for business purposes, like eCommerce stores. Amazon uses data science to track what you look at, understand the data behind your choices, and push targeted ads onto your screen.
Machine learning is a subset of data science and more closely works with algorithms that follow trends and make forecasts or predictions about future trends. In many cases, machine learning can run autonomously, meaning that the machines running machine learning software can do their programmed jobs without human interactions. In contrast, data science is human-driven. Machine learning is used by companies like Netflix and Facebook to track what you’re interested in and give you related movies, shows, or articles at which to look.
Machine learning and data science do have a lot in common, but at the end of the day, they are unique in their tasks and goals and typically should not be used interchangeably.