Does Data Science Include Machine Learning? - Data Science Degree Programs Guide

If you’re considering a degree in computer science or a related specialty in the field of information technology, you might be wondering if data science includes machine learning.

The short answer to this important question is yes, it does. Machine learning, which is a component of artificial intelligence, is a sub-field of data science.

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When thinking about a future career, it’s important to understand the different pathways you might take. In this case, it means getting familiar with machine learning as it could help you decide if it is a career field that’s right for you.

Of course, before you can have a career in machine learning, you’ll have to get the appropriate educational credentials so you have the requisite knowledge and skills to be a competent data scientist.

Let’s discuss machine learning in more detail so you can see if it’s a career that interests you.

What is Data Science?

A quick definition of data science might be a field that is the combination of math, statistics, programming, analytics, artificial intelligence, and the scientific method and whose aim is to reveal, analyze, and explain information contained within data.

In other words, there’s a lot going on in data science!

The key with data science is the desire to retrieve actionable insights from data. It’s one thing to find relevant information in the increasingly large wealth of data that’s available. But it’s another thing to develop an understanding of data that leads to actionable steps.

Data scientists don’t just examine and explain information, either. They are responsible for preparing data as well. This process can include many different steps, from cleansing the data to aggregating it to manipulating it for various kinds of processing.

To process and analyze data, analytics, algorithms, and artificial intelligence are often used to find patterns and make predictions about the data. Those predictions are used to help support organizations as they take actionable steps to make improvements based on the data. However, before an organization can utilize the predictions that are made, those predictions must be validated. This is done by means of scientific testing.

Another component of this is the process of data visualization. After data scientists cleanse and analyze data and make their predictions, the results are shared using data visualization tools – graphs, charts, and so forth.

The point of data visualization is to summarize the data and patterns within it in an easily digestible manner. So, rather than bombarding members of an organization with a spreadsheet that contains hundreds of data points in dozens of rows and columns, a data scientist would present the data in a graph, chart, or other visual medium that offers a “bird’s-eye view” of the data.

The point of this part of data science isn’t just to make neat-looking graphs. Instead, data visualization speeds up the process of understanding what the data is saying and enables stakeholders to make more informed decisions. What’s more, data visualization techniques enable laypersons that might not have a great familiarity with the data to quickly understand the information being revealed.

Of course, to undertake the process of data science, a wealth of different tools are needed. Programming languages like Python and R are central to data analysis as are tools like SQL, cloud computing, and, of course, artificial intelligence.

How Artificial Intelligence Fits Into Data Science

Artificial intelligence refers to computers that have a set of instructions that allows them to reason, make classifications, and perform decision-making activities, the point of which is to extract patterns and insights from datasets that include an incredible array of information.

In the past, all of this had to be done by a human. But AI has made this process far more streamlined and allows data scientists to sort through incredible amounts of data much, much faster.

Although artificial intelligence sounds like a relatively new concept, it has actually been in use since the 1950s. The term “artificial intelligence” was first used at a conference at Dartmouth College in 1956.

However, the study and development of artificial intelligence took a long time to gain traction. Progress was initially quite slow, and by the 1970s, governments were no longer interested in funding studies of AI. As a result, interest waned and the field entered what is referred to as the AI winter from 1974-1980.

However, interest was renewed in the early to mid-1980s as the British government began funding research into artificial intelligence as a means to keep pace with developments in AI in Japan. But, again, an AI winter set in, this time from 1987-1993.

Yet again, though, AI made a comeback in the late 1990s and the field has continuously grown (along with the amount of data we have available to analyze).

Artificial intelligence isn’t just used for high-powered data science research. It’s become an integrated part of our daily lives.

A common use of artificial intelligence includes image recognition and classification. For example, a person who takes a digital photo of an unusual insect and does a Google search of the image in order to find out the type of insect it is, is making use of artificial intelligence.

Artificial intelligence can be conducted in several ways, one of which is machine learning.

Machine Learning as a Method of Artificial Intelligence

To understand the relationship between data science, artificial intelligence, and machine learning, we need to quantify what machine learning is.

A good definition might be this: machine learning is the application of artificial intelligence that enables systems to learn on their own and improve from experience without being directly programmed to do so.

In other words, computers are programmed to access copious amounts of data and to use that data to learn on their own.

The process of machine learning starts with making observations of data. For example, it can utilize examples or instruction to look for patterns in data that enable better decision-making based on the examples provided.

There are a variety of machine learning methods that are used in data science. This includes:

Unsupervised machine learning, which entails using information that isn’t labeled or classified. This type of machine learning examines how systems infer functions in order to describe hidden structures from the unlabeled data. Rather than determining the correct output, machine learning examines the data and makes inferences from the data to describe the hidden structures.

- Semi-supervised machine learning, which utilizes algorithms that are neither supervised or unsupervised. For training purposes, this type of machine learning uses unlabeled as well as labeled data. Doing so helps to improve the learning accuracy by large margins. In many circumstances, this type of machine learning is used when the acquired labeled data needs skilled resources in order to learn and train from it.

Supervised machine learning, which uses algorithms that can apply what has already been learned to new data. This is done in order to predict future events. Beginning with a known training dataset, supervised machine learning creates an inferred function which predicts the output values. This type of algorithm can also compare the output with the intended output to find errors, and can then modify the model as needed.

Applications of Machine Learning

There are many applications of machine learning.

In classical machine learning, you can give a system certain features that are common to an everyday object, like a dog, along with photos of many different kinds of animals. Then, the system would sort through databases of photos to find which features it was supplied with fit with all the photos of cats in the dataset. Over the course of this process, the machine learning system improved more and more as it learns from working with the data – in this case pictures of cats and many other cats in order to identify the features of cats.

There are other examples of machine learning in everyday life.

For example, one application is email filtering. For people who use Gmail, their email is filtered by Google, which uses algorithms and statistical models in order to determine which emails are spam.

As another example, search engines use machine learning when determining which results are relevant to the words entered by a site user.

Speech recognition and natural language processing are further examples of machine learning. Anyone who does a voice search on their smartphone is making use of this application of machine learning. Credit card fraud detection, online advertising, and internet fraud detection all use machine learning as well.

Why Machine Learning is Important

Machine learning, and with it, data science and artificial intelligence, are capable of impacting virtually all aspects of our lives. As noted in the examples above, from email to internet fraud to credit card fraud, machine learning has had significant impacts on the way we live.

In the business world, artificial intelligence has changed the way that decision-making is made. For instance, managers and executives can extract data from many different sources to improve their ability to make predictions about the products and services that are most likely to sell well in the future. What’s more, machine learning can help businesses understand who is most likely to buy these products.

This allows businesses to tailor their marketing plans far more specifically. Doing so means that they are able to increase even further the likelihood that someone will buy their product or service.

What’s more, these processes enable businesses to improve the development of products and services in the first place. Using data from different sources, decision-makers in the development stage can identify which features are most desirable to consumers and which are not.

This means that machine learning and artificial intelligence plan a very important part in growing businesses and expanding their bottom line. Not only does it help executives drive their decision-making process, but it also helps them design better products that are more likely to sell. It’s a win-win situation: businesses develop more successful products that sell well while consumers get products and services that more directly meet their needs.

How to Get Involved in Machine Learning

If a career in machine learning sounds attractive to you, you’ll need to embark on a fairly lengthy course of education.

After getting a bachelor’s degree in computer science or a related field, you’ll need to get a master’s degree. While some entry-level positions are available for bachelor’s-level workers, the vast majority of jobs in this field are reserved for applicants with a master’s degree in software engineering, data science, computer science, or a closely related field.

If you want to maximize the opportunities you have, getting a Ph.D. is your best course of action. A Ph.D. in any of the aforementioned fields – software engineering, data science, or computer science, would be a good choice. Even better is to get a Ph.D. in machine learning or machine learning engineering.

Another option is to get a Ph.D. in a field like data science, and then do post-doctoral work specifically in machine learning.

The point is that this field requires copious amounts of education, so you should be prepared to be in school for a decade or more to complete the needed undergraduate, graduate, and doctoral studies.

While it is a long time to be in school, the reward of working in a dynamic field that is changing the way we live makes it a worthy course of study.

Data science, artificial intelligence, and machine learning are rapidly growing fields, and earning a degree in any of these areas with a specialty in machine learning could prove to be a lucrative career for a talented person who is willing to keep learning and stay up-to-date with technology.

Machine learning will grow in importance as corporations and governments seek to automate the analysis and collection of data from multiple streams, which means that job opportunities may be plentiful in the near future. Now that you have more details about machine learning and its relationship to data science, you can make a more informed decision about how to proceed with your education.

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