What’s the difference between data science, data analytics, and machine learning? And how do they relate to one another?
With the dawn of the digital age and the explosion of affordable processing power, a flood of data entered our lives. From the links we click to the boxes we tick, data are everywhere. Organizations use data to perfect their products, improve their services, and to offer highly-tailored user experiences. But it’s not as simple as having data and applying it. First, we need to make sense of it and extract useful information from it. And this is a complex, multifaceted journey.
The emergence of data science, data analytics, and machine learning are part of this journey. While these terms are often tossed around interchangeably, they’re not synonymous. In this post, we’ll cover what they mean, how they relate to each other, and how they differ.
To jump straight to a topic, use the clickable headings below.
But first, let’s start with some basic definitions.
1. Data science vs. data analytics vs. machine learning
Before comparing data science, data analytics, and machine learning in detail, let’s define them. This section offers some at-a-glance definitions to broadly distinguish between the terms.
Quick definition: Data science
Data science is a field of scientific study, focusing on data. Its broad goal is to extract useful information from big data and to ask important questions based on that information. Data science is a multidisciplinary field. This means it uses a wide variety of different techniques and toolsets to support its ultimate goal. Two of these tools include data analytics and machine learning.
Quick definition: Data analytics
Data analytics is a discipline within the broader field of data science. It is a methodical process used to extract, organize, interpret, visualize, and draw conclusions from data. Whereas data science is about asking strategic questions, data analytics supports specific decision-making, using actionable, data-driven insights.
Quick definition: Machine learning
Machine learning is a tool used to construct algorithms that learn to spot patterns in data and make predictions based on those patterns. Within the field of data science, it’s often applied to data sets that are too complex for a person to analyze. For this reason, it’s commonly used when it’s impossible to design or program specific algorithms, i.e. if you know your goal, but are unable to define a means of reaching that goal.
In reality, the lines between data science, data analytics, and machine learning are more complex. In the sections that follow, we’ll explore the nuances in more detail.
2. In-depth: What is data science?
If data science was an entire road trip, you could think of data analytics and machine learning as stopping points along the way. While the latter are important skills to have, they’re just two of many techniques and processes that a data scientist uses.
Ultimately, the goal of data science is to bring order to big data, using it to ask questions and to support changes in the way a business or organization runs.
Key skills for data scientists
A data scientist’s roleis very complex. As skilled mathematicians, they need in-depth expertise in data modeling and the ability to create custom data tools from scratch. They also require intimate knowledge of software like Apache Spark and Hadoop, and to be comfortable using a wide range of programming languages and concepts.
Due to the complexity of the role, most data scientists have a Ph.D. or other graduate degree. Their main skills usually include:
- Master’s or Ph.D. in computer science, statistics, or similar.
- Domain expertise, i.e. subject matter expertise of a particular field.
- Expert in complex mathematical modeling.
- In-depth knowledge of machine learning, deep learning, data analytics, etc.
- Familiar with big data tools, e.g. Apache Spark, Hadoop (Hive and Pig), SQL, etc.
- A wide spectrum of different software and programming skills, e.g. Python, R, Java, Perl, R, C++, C, etc.
- Excellent at working with unstructured data.
Note: Data scientists are not to be confused with data engineers. You can read about the differences between data scientists and data engineers (and how they overlap) here.
What makes data science unique?
While data analysts and data scientists share a common goal—helping to make business decisions—they go about it in different ways. An analyst seeks answers to questions. Meanwhile, a data scientist’s job is to ask very detailed, tactical questions to help inform an organization’s overall strategy. While a data analyst may work within a single division or department (and have detailed knowledge of that division) a data scientist needs to understand the processes, systems, and aims of the organization as a whole. This means they need a far broader range of tools at their disposal, from data analysis to machine learning, deep learning, computer science, statistics, hacking skills, and more.
To learn more, watch this webinar we conducted with CareerFoundry’s own data scientist, Tom Gadsby:
3. In-depth: What is data analytics?
While data scientists have full oversight of an organization, data analysts have a narrower, but more detailed focus. This means they tend to have a more intimate working knowledge of a particular aspect of an organization (such as marketing, finance, or product management, to name a few).
A data analyst’s main role is to process raw data and to provide actionable insights. This might include where to cut costs, recommending new product features, or how best to target an advertising budget.
Learn more: What is data analytics? A complete introduction.
Key skills for data analysts
Data scientists and data analysts share several important skills, such as statistics and probability, and excellent working knowledge of software tools and programming languages. These skills are crucial for general data manipulation.
However, especially important for a data analyst is the ability to communicate well. Because they often work with non-technical personnel, they must do more than simply find compelling patterns in data; they must communicate these patterns to justify key business decisions. Their main skills usually include:
- Knowledge of probability and statistics.
- Strong management and communication skills (for working with different teams).
- Advanced database management skills.
- Experience in filtering and data cleaning.
- Strong data visualization expertise.
- Fluent at least in Python, R, SAS, and SQL, and in MS Excel.
What makes data analytics unique?
4. In-depth: What is machine learning?
In the context of data science, machine learning is used to produce pattern-spotting algorithms that can automate aspects of the data analytics process. By feeding large amounts of data to a machine, it can learn to spot patterns that a human being can’t.
As well as being a practical support tool, machine learning is also an entire discipline of its own—a subset of artificial intelligence (AI). However, in the respect that it relies on machines to carry out analytics tasks that a human cannot, it can certainly be defined as a tool.
Here’s data scientist Tom back at it again, this time with a deep dive into all things machine learning:
Key skills for machine learning
As with data science and data analytics, machine learning engineers require a number of important mathematics and data manipulation skills. However, a machine learning engineer is more likely to have specialized knowledge in areas such as non-neural machine learning concepts (e.g. decision tree and random forest) as well as in natural language processing and computer vision (which deal with how computers interpret audio and visual input). Other skills usually include:
- Software engineering and system design expertise.
- Strong knowledge of machine learning algorithms and concepts, e.g. supervised learning, unsupervised learning, reinforcement learning.
- Non-neural learning concepts, e.g. decision tree, random forest, logistic regression.
- Expert knowledge of machine learning libraries, e.g TensorFlow, PyTorch, Theano.
What makes machine learning unique?
What makes machine learning stand out is its ability to solve labor-intensive problems much more quickly than a person can. As it’s mostly used for big data, it is usually more accurate, efficient, and reliable than a human being. Machine learning is also commonly used when an analyst has a goal in mind, but the data is too complex to define a clear pathway to that goal. In essence, machine learning allows us to work backward; spotting patterns in data that can then be analyzed to solve the problem that the analyst needs an answer to.
Machine learning can be used to automate decision-making across a wide range of different disciplines and industries. This includes healthcare, retail, e-commerce, and finance. Its potential is huge. This is why it’s such a ‘buzz’ topic right now.
However, for many business goals, a human data analyst is sufficient. Machine learning is great for managing big data sets, but if it’s not finely tuned, it can easily overcomplicate problems that don’t require intricate algorithms. If you’re new to the field, this is definitely something to keep in mind! While investigating machine learning, you may also come across the topic of deep learning. You can read about how machine learning and deep learning differ (and overlap) in this guide.
Data science, data analytics, and machine learning are three complex, interrelated topics. They all involve manipulating and interpreting data. While each overlaps, they can be broadly defined as follows:
- Data science is a scientific discipline that explores facets of all kinds of unstructured data and how those data relate to the world.
- Data analytics is a key process within the field of data science, used for creating meaningful insights based on sets of structured data.
- Machine learning is a practical tool that can be used to streamline the analysis of highly complex datasets.
Despite significant overlap (and differences) between the three, one thing’s certain: demand for data scientists, data analysts, and machine learning engineers is on the rise. There are tonnes of career paths available, depending on where your skills lie, and what your interests are. If you’re not sure which route to pursue, it’s worth investigating each path in depth and seeing which resonates the most. To help you get started, you can dip your toes in with this free, five-day introductory data analytics short course.
To learn more about forging a career in data, check out the following: