# The differences between data analytics, machine learning and AI

Discover how data analytics, machine learning and artificial intelligence are shaping the future and the differences between each discipline.

We are living in a time of rapid technological advancement. Computing power has been increasing exponentially, meaning that we can harness this processing power for ever more complex tasks. Three fields that have emerged alongside this rapid growth are data analytics, machine learning and AI. But what’s the difference between these three closely linked technologies?

As well as taking a look at how these topics overlap, we’ll also explore what makes them unique. We’ll examine the main differences between each topic, as well as some of the careers they can lead to and the skills required for each one.

## What is data analytics?

Let’s begin by looking at what each term means, starting with a data analytics definition. At its heart, data analytics is the science of analysing data sets to find trends, answer questions, and draw conclusions. It’s a varied and complex field that often relies on specialist software, algorithms and automation.

The principles of data analytics can be applied across just about any industry. Organisations of all kinds employ data analysts to help them make informed and data-driven decisions about different areas of their businesses. Usually, existing data from past events are analysed, meaning existing trends can be identified.

There are several different types of data analytics, including descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics.

### Data science vs data analytics

These two terms are sometimes, incorrectly, used interchangeably. Data analytics focuses on the examination of data sets to identify and explain trends. Data science looks more at the processes for data modelling and production, creating algorithms and predictive models.

There is some interchange between the two disciplines, however. The meaning of data science relates to a wider field that focuses on discovering large sets of data. Within that scope is data analytics, a more focused area that looks at the insights offered by examining existing information.

## What is artificial intelligence?

Artificial intelligence (or AI) is a concept that’s been around for a while. However, it’s only in recent years that we’ve truly had the processing power to actually make it a reality. In its simplest terms, AI is the ability to give computers the ability to replicate human intelligence.

By creating computers that are capable of learning, it’s possible to teach them from experience. Such artificial intelligence systems have three qualities; intentionality, intelligence, and adaptability. These qualities give them the ability to make decisions that traditionally require a human level of experience and expertise.

## What is machine learning?

We’ve already covered machine learning in more detail in a separate article. This field is a subset of artificial intelligence whereby computers are programmed to learn automatically. These computers can act in a similar way to humans, improving their learning as they encounter additional data.

Much of the focus of machine learning is to create programs and software that can learn to make predictions and decisions without being directly programmed to do so. The technology can be used for all kinds of purposes, from powering search engines to diagnosing medical conditions.

### Machine learning vs deep learning

Digging deeper into the topic of machine learning, we have the subset of deep learning. As the layers of machine learning algorithms build up, they form complex networks that mimic the structure of the human brain. These artificial neural networks can learn to make intelligent decisions without additional human input.

You’ll often find that the most ‘human-like’ artificial intelligence systems are powered by deep learning. This is because they can process unstructured data (data without clear labels). In contrast, other types of machine learning focus mainly on structured data (that which is pre-labelled).

## Where do they overlap?

So, we have three distinct areas of expertise we’ve outlined there. Each has its own applications, subsets, and specialisations, making them very different fields. However, as you may have noticed already, there are certainly some areas where they overlap.

Below, we’ve outlined just some of the ways in which machine learning, data analytics, and AI overlap.

• Data-driven. Each of these areas relies on analysing huge amounts of data. The more information available, the more effective they are at producing results. It often takes a lot of computer processing power to manage such large data sets.
• Insights. Data analytics, AI, and machine learning can all be used to produce detailed insights in particular areas. By examining data, each can identify patterns, highlight trends, and provide valuable and actionable outcomes.
• Predictive models. These technologies can also help to create forecasts and predictions based on existing data. Again, this process can help organisations of all kinds plan for the future and make informed decisions.

## Other key fields

Of course, many other areas relate closely to those of AI, ML, and data analytics. Across fields as diverse as statistics, mathematics, computer science and information science, there are overlaps in the techniques and technologies used. Some of the other, closely linked areas of specialisation include:

• Robotics. Building and programming robots to operate in real-world situations is seen as the holy grail of artificial intelligence. Machine learning plays a particularly important role here, allowing computers to react to visual and speech cues and respond accordingly.
• Data mining and statistical analysis. Data mining deals with massive and complex data sets. Some of the foundations of machine learning are used to delve into this information to form conclusions and predictions from it.
• Cloud computing. Technology such as machine learning and artificial intelligence often require a vast amount of processing power. Cloud computing, the process of delivering on-demand computing services via the internet, can contribute to that power.
• Big data. Central to many of these fields is the concept of big data. This term refers to the large sets of structured and unstructured data that are hard to process by traditional means.

## What’s the difference between machine learning and AI?

One of the questions that are often asked is where the difference between AI and machine learning is seen. Yet this doesn’t mean that there is a kind of AI vs machine learning dichotomy. In fact, it’s more of a case that machine learning is an application of artificial intelligence.

Despite the two terms sometimes being used interchangeably, there are some differences worth noting. Most of these focus on the purpose, goals, and scope of each field:

Artificial intelligenceMachine learning
PurposeTechnology that allows computers or machines to emulate human behaviour.A type of artificial intelligence that allows computers or machines to automatically learn from data without being specifically programmed to.
GoalsTo create smart, human-like computer systems that can solve complex problems.To create computer systems that can continually learn from data, allowing them to perform a particular task and give an accurate output.
ScopeAI has a broad scope and can be applied to a wide variety of tasks.ML is narrower in scope and is usually applied to very specific tasks.

These differences mean that the applications for each field are slightly different. However, many advanced AI systems use some elements of machine or deep learning.

## The different jobs in machine learning, data analytics and AI

If you’re intrigued about these data-driven areas of interest, you might be considering a related career path. But what kinds of jobs are there in the different fields? We’ve picked out just a few examples for each:

### Data analytics jobs

• Data analyst. The role of data analyst focuses on processing raw data to create meaningful insights. They work to identify trends and present them in a meaningful and easy-to-understand way.
• BI analyst. Business intelligence analysts work to provide data insights that can inform business decisions. They use a variety of techniques and technologies to allow organisations to make informed choices backed by data.

### Artificial intelligence jobs

• Robotics engineer. This role focuses on the design and building of machines to automate jobs. When it comes to robotics and AI, the latter is needed when creating robots to perform complex tasks.
• AI programmer. An artificial intelligence programmer works to develop software that’s used for AI applications. It’s a role very much focused on the software development perspective.

### Machine learning jobs

• Machine learning engineer. With this role, elements of software engineering and data science overlap. Machine learning engineers create algorithms and programs that help computers to learn automatically.
• NLP scientist. Natural language processing (NLP) is the technology used to help computers understand natural human language. NLP scientists create algorithms that help with this process of understanding human language.

## The difference in average salaries

As you might expect given the highly technical nature of some of these roles, average salaries tend to be reasonably high. However, it’s worth knowing how the different specialities compare to each other in general. We’ve picked out some of the relevant data below.

### Machine learning salary

In the UK, the average machine learning engineer salary is around £50,000 per year, according to PayScale. Indeed’s estimate is similar, placing the figure at £56,000 per year. For the US, their base salary estimate is \$150,000 per year.

## The different skills needed

As we’ve seen, there are several similarities and differences across the fields of data analytics, machine learning and artificial intelligence. As you might expect, many of the skills needed to progress in each follow a similar pattern. We’ve picked out some of the hard and soft skills that jobs in these sectors often require.

### Common skills

When it comes to the types of skills that are useful across machine learning, data analytics and AI, there are several that come to mind. Some of these are industry-specific abilities, while others are transferrable skills that are universally useful:

• Programming. Whichever type of role you work in, you’ll likely need to know the basics of a few programming languages. Python, C++, Java, and others can help with many different aspects.
• Data analytics and data modelling. As you might expect, having a working knowledge of data analysis and creating data models is a must-have to work in these industries.
• Communication. Many of the roles in these areas require you to explain your reasoning and findings to non-experts. You’ll also have to work across a diverse range of organisations and teams. As such, effective communication is vital.
• Teamwork. Professionals in data science, ML and AI tend to collaborate with others to produce results. Teamwork and leadership are highly desirable qualities for this reason.

### Data analytics skills

For jobs in data analytics specifically, some of the following skills can come in handy:

• SQL. Structured Query Language (SQL) is used to manage and store large amounts of data. It’s an in-demand skill for many reasons and is particularly useful in data analysis.
• Data visualisation. As well as understanding and interpreting data, a vital skill is to be able to present it in a readable format. Data visualisation is growing in popularity across many niches.
• Critical thinking. The ability to think critically and analytically is another valuable asset. It allows you to apply the right tools and methods to reach your goals.

### Artificial intelligence skills

When it comes to the types of AI skills you’ll need, there are several important ones to have:

• Applied mathematics. Advanced maths skills are often at the heart of AI projects. A working knowledge of linear algebra, calculus and probability is a good place to start.
• Python. The Python programming language is often a must-have for work in the field of artificial intelligence. It’s frequently used as a relatively simple way to construct AI models.
• Creativity. Being able to creatively approach problems can be hugely beneficial in the world of AI. Creative problem-solving is a skill that many employers look for.

### Machine learning skills

• Algorithms. At the heart of machine learning are algorithms, sets of instructions that computers follow. Understanding how they work and how to create them will be essential to work in ML.
• Distributed computing. This technology uses multiple computers to perform tasks and complete functions. Systems such as Hadoop and Spark are worthwhile knowing.
• Independent learning. The field of machine learning is developing and changing at a rapid pace. Being able to keep up with and understand these changes is essential.

## Where to get started

If you’re interested in getting into machine learning, data analytics or artificial intelligence, there are several routes you can take. Usually, you’ll require a combination of education, experience and self-taught knowledge.

You’ll want to start by focusing on some data, AI, and machine learning hard skills such as mathematics and computer programming. Subjects such as linear algebra, calculus, Python, SQL and Java are some particularly useful places to start.

At FutureLearn, we have several online courses and opportunities that can help you start making progress in each of these fields. Here are some of the machine learning, data analytics, and AI courses that can set you on the right path:

## Final thoughts

We’ve seen that there are similarities as well as differences between data analytics, machine learning, and AI. Although the fields are closely linked in many respects, each has its own particular applications, scope, and areas of specialisation.

With endless career opportunities in these fields, we’re sure you’ll find something that fulfils your goals and matches your skills and interests. Now you’ve learnt the basics about AI, ML and analytics, it’s time to gain experience in these exciting and fast-developing technologies.

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