The last two decades of technology development has led to several major innovations, including machine learning and data science breakthroughs. As these systems become widely available to the public for use in business, there seems to be some confusion about what both of the systems are. Machine learning and data science are distinct disciplines that can work together but should be treated as their own focus areas in business. In this article, we discuss the differences between machine learning and data science as well as anything else you need to know.
What is Machine Learning?
Machine learning is the study of algorithms for computers that are given supervised access to data and programmed to generate relevant, personalized results by using deep learning techniques and mathematical algorithms, much like how a child learns to recognize patterns. Machine learning can create a general solution to a problem. Businesses large and small are finding that applying machine learning techniques and data science is the best way to leverage the data they have and provide smart business solutions to their customers.
The best machine learning programs can reduce the time and money spent trying to achieve business results that are not viable. There are many applications of machine learning within data science, including pre-defined business targets, user profiling, real-time dashboards, predictive analytics, and much more. While many of the challenges faced by data science teams can be found in traditional project management and planning, with machine learning and data analytics, companies can overcome many of the difficulties of implementing their models, such as data quality, because these applications are specifically designed to solve these problems.
Machine learning deals with a wide variety of tasks like image and video recognition, speech recognition, natural language processing, speech transcription, web browsing, text mining, product cataloging, image and video analytics, time tracking, artificial intelligence, and marketing. Machine learning algorithms are being used in applications like clinical research, financial services, online marketing, e-commerce, transportation safety, manufacturing, and healthcare.
What is Data Science?
Data science is the multi-disciplinary approach to extracting useful insights out of the vast and continuously growing amounts of data produced and created every day by today’s businesses. Data science applies the modeling, processing, and analysis of large amounts of data obtained through a range of scientific and business methods, ranging from traditional applied research to computer science and the emerging science of data visualization.
Data science is also inherently concerned with the implications of data mining, which refers to the process of finding new or unanticipated ways to extract useful information from large quantities of data. Data science uses a variety of methods, including traditional programming, code generation, and optimization, to build predictive models and make them work in real-world business situations. Essentially, it is a field where technology is used to make the most sense out of data.
Machine learning and data science are very much related and often mistaken to be the same thing. Both machine learning technology and data science are used together within the scope of software development. With data science and machine learning, businesses can quickly and easily leverage massive amounts of information they have accumulated in order to make smarter decisions.
Amazon, for example, is using its extensive data set to provide its customers with personalized recommendations. This personalized service is one of the latest examples of how people are using their digital libraries to help businesses succeed. As organizations continue to implement dynamic pricing models and other business strategies based on real-time data, companies will continue to see what is machine learning and what it can do for them.
While both terms are sometimes used interchangeably, there is a lot of differences between these two technologies. Basically, data science is a discipline that involves using machine learning and statistical analysis techniques to extract practical insights from large, complex data sets. However, this is just one application of data science. There are other methods and principles in the field of data science.
The main goal of data science is to efficiently extract relevant insights from large, complex data sets by using predictive analysis or neural networks. This allows businesses to make informed decisions based on large amounts of data which is usually gathered from diverse sources. On the other hand, machine learning deals with massive amounts of data, typically from a supervised learning environment.
With this, businesses can use pre-existing databases to provide better insight into the real business intelligence. For example, if a business owner wants to know which coffee brands are best and why, with the help of predictive machine learning technology they can quickly and accurately make smarter decisions with the help of big data and their own personal intelligence.
As companies become more reliant on data science and machine learning in their operations, customers will see a major shift in how they interact. If handled correctly, the results could be beneficial for everyone involved. At KitelyTech, we work with companies to master their use of machine learning and data science so that they get the most out of their efforts. Call us at (800) 274 2908 to discuss how we can help you with your next project.