Machine Learning vs Data Science: What’s the Difference? (Infographic)

Data science and machine learning are often equated and spoken about interchangeably, but if you’re planning to start a career in either field, it is important to understand the differences between them. 

This blog will help you gain a solid understanding of what the two different yet closely connected technologies do, and how they are applied in their respective fields.

What is Machine Learning?

Machine learning is the method of training a computer to identify and categorize the information provided. Handwriting identification is one application of machine learning that the United States Postal Service uses daily to decipher hard-to-read addresses.

Computers look for patterns and identify illegible data so the mail can get to the correct location. Using pattern recognition, the computer utilizes algorithms and the information it has on the file to create accurate projections.

Machine learning is a very powerful technology. When teaching a computer to use an algorithm to define patterns, you can then use those patterns to predict results without pre-defined concepts or pre-programmed rules. Machines can only enhance their own learning by using the information they have been provided, meaning that machine learning is not effective without a robust dataset.

Machine learning also has its restrictions. For example, if you train a machine with facial recognition software, and you only feed it pictures of a particular ethnic group, it may not be able to identify other races or features. Machine learning requires a lot of information to operate properly.

Because machine learning is growing at such a rapid pace, receiving a data science or analytics education from a boot camp is a great idea for career changers, up-skillers and other individuals looking to enter the field. Having a certificate of completion offers extra value to potential employers, as jobs in machine learning and data science are increasing every day.

What is Data Science?

Data science is a field that incorporates machine learning, statistics, advanced analysis and programming. This is a method for checking, cleansing, transforming and modeling data with the aim of finding helpful information, suggesting conclusions and encouraging data-driven decision making.

In other words, data science involves the processing and analysis of information to serve a variety of data analytics uses. Predictive analytics is then used to offer projections based off the data insights. 

Data professionals utilize a range of techniques to generate information, often by integrating machine learning, predictive analytics, statistics and computer science to analyze large sets of data to offer solutions that transform businesses and drive new results. 

The advantages offered by data science can range based on the objective of the company and sector. For example, sales and marketing departments can collect client information to improve conversion. Banking institutions are mining information to improve fraud detection. Other industries such as e-commerce may use data to determine which products or goods to suggest to shoppers. 

If you would like to learn more about this field, check out our Beginner’s Guide to Data Science.

Comparing Machine Learning and Data Science

Data science is very similar to machine learning. The two have a great deal of overlap but with a few key variations to keep in mind. The optimal data volume for machine learning is just thousands of data points, while data science objectives may require big data with millions of data points.

The output of machine learning is typically a numerical value, such as a score or classification. By contrast, data science encompasses all the ways in which information and knowledge are extracted from data.

The process of how each concept works is similar but slightly different. Machine learning relies on automated algorithms that learn how to model functions, then predict future actions by using the data provided. Data science relies on an infrastructure that can supply clean, reliable and relevant data in large volumes with reasonable speed.

Even the management of data science and machine learning is slightly different. With machine learning, data analysts direct the algorithms to look at particular variables in a set of data. Data science professionals have a range of management responsibilities across multiple disciplines. 

Learn More

The UofT SCS Data Analytics Boot Camp is designed for a wide range of individuals that are interested in acquiring technical skills within the data analytics field. Discover how both our online and in-class options can help you reach your professional goals.

View our Machine Learning vs Data Science infographic below:

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