In the contemporary business world, terms like ‘artificial intelligence’, ‘machine learning’, and ‘data analytics’ are looming large. In this article, we’ll unpack these terms and explore how data science and machine learning are being used by organisations to make better decisions and help them secure a competitive edge.
More specifically, we’ll explain how data science, AI, and machine learning differ from one another. Conversely, we’ll look at the ways in which they can be used together to uncover hidden patterns in large datasets and make accurate predictions about future trends. This powerful combination can facilitate real-time analytics, automate complex tasks, and help to minimise errors – among many other benefits.
In this article, we’ll also explore the following:
- What is machine learning?
- What is data science?
- Data analytics and machine learning: How can they work together?
- What is the difference between AI and data science?
- Machine learning vs AI vs data science: Making sense of the jargon.
- Study data science and machine learning at Monash.
What is machine learning?
Machine learning is a branch of artificial intelligence that enables computers to learn and improve without being explicitly programmed. By using algorithms and statistical models, machine learning systems analyse data and make choices without much input from humans. Think of it as teaching a computer to identify cats not by coding specific traits like whiskers and fur, but by exposing the computer to thousands of cat pictures until it learns the features of a cat on its own.
Together, data science and machine learning can drive innovation and solve real-world problems. What makes machine learning especially powerful is its ability to handle complex situations where traditional rule-based programming is impractical. This technology powers many applications, from fraud detection systems, to Netflix recommendations and healthcare delivery. For example, machine learning can optimise supply chains, predict disease outbreaks, and detect financial fraud – all of which involve large volumes of data and dynamic patterns that are difficult to model with traditional methods.
What is data science?
Data science is an interdisciplinary field that uses scientific methods to get insights from data. It merges statistics, computer science, and domain expertise to solve challenging issues.
Data science is a broad field that involves all areas of data handling:
- Collecting and cleaning data
- Analysing data
- Creating visualisations and communicating findings
- Making business recommendations
Unlike traditional fields that focus on specific domains, data science uses a holistic, interdisciplinary approach to solve a wide range of problems across industries. Data scientists must be technically proficient and understand the specific domains they’re working in, such as public health, finance, security, and retail. They combine technical skills with business acumen to drive decision-making and innovation.
What is the role of machine learning in data science?
Data science and machine learning work together, with machine learning acting as a resource in a data scientist’s approach. Machine learning can automate complex tasks, like processing and analysing large amounts of data. It makes more accurate predictions than traditional statistical methods alone could achieve alone.
The role of machine learning in data science is to:
- Find patterns in large datasets
- Make predictions based on past data
- Automate repetitive analysis tasks
- Improve accuracy over time as it learns
Machine learning empowers data science to solve complex problems and deliver smarter, data-driven solutions across industries.
In the next section, we’ll discuss machine learning vs AI vs data science.
What is the relationship between AI and data science?
The distinction between AI vs data science can be confusing, as both use machine learning. Here, we’ll go over the differences and explain how all three work together.
While machine learning is a tool that data scientists use, artificial intelligence (AI) is a larger technology field that includes:
- Machine learning as one of its main components
- Natural language processing (understanding text)
- Computer vision (understanding images)
- Automated decision-making systems
- Speech recognition and generation
The difference between AI and data science is clearer when we see them as allies – they often work together, but they’re not the same. Data science is about digging into data to find patterns and insights, while AI focuses on creating systems that mimic human intelligence. Rather than replacing data scientists, AI is changing the way they work. With AI taking care of mass data processing, insights and pattern recognition, AI is shifting data scientists’ role toward strategic interpretation and stakeholder communication.
Benefits of using AI in data science:
By completing tasks that would take humans weeks, AI is freeing up scientists to focus on interpretation rather than preprocessing.
AI is helping data scientists by:
- Improving routine tasks and enabling more sophisticated analysis
- Enhancing pattern recognition at huge scales
- Automatically selecting relevant features from datasets
- Using natural language processing instead of complex code
- Automating insights and presenting them in natural language
What are the benefits of machine learning in data science?
Machine learning can handle complex datasets and uncover hidden patterns that traditional data science methods might miss. It improves predictive accuracy and enables better forecasts and insights.
Machine learning improves data science in the real world by:
- Automating decision-making to save time and reduce errors
- Scaling up to process massive amounts of real-time data
- Personalising user experiences based on data insights to drive consumer engagement
- Improving the accuracy of predictions to support healthcare
- Detecting anomalies and fraud
When comparing data science vs machine learning vs AI, machine learning’s unique contribution lies in its adaptability to improve itself. By learning from new data, machine learning models become more accurate and effective over time. This automation is particularly useful in industries where quick, data-driven decisions are crucial, such as finance and healthcare.
Here’s how data science, machine learning, and AI can work together to improve customer retention:
- Collect customer data (Data Science)
- Use machine learning to predict which customers might leave (Machine Learning)
- Use AI chatbots to automatically respond to those customers (AI)
The three work together but serve different purposes: data science is the profession and process, machine learning is a tool, and AI is the broader technology.
What’s the difference between data scientists and machine learning engineers?
The roles of data scientists and machine learning engineers overlap, but focus on different areas of working with data and models:
- Data scientists focus on analysing data to uncover insights and make predictions. They work with datasets to identify trends and solve business problems using techniques like machine learning. They also present their findings through clear visualisations and reports to help organisations make informed decisions.
- Machine learning engineers build and fine-tune machine learning models. They take models created by data scientists and make sure they can handle big datasets and work in real-time. They also collaborate with software engineers to integrate these models into business systems, keeping everything running smoothly.
Learn more with Monash
Monash Online offers data science courses that can prepare you for a fulfilling career in this industry. Data science is in Australia’s top five in-demand and highest-paying jobs, and our course is ranked #34 globally and #2 nationally in the field.
We’re harnessing the power of AI, bringing together multidisciplinary teams to explore emerging technologies and their societal impacts. Our applied data science program also teaches machine learning principles throughout, combining these highly employable, future-focused skills in one course. Plus, our accelerated six-week teaching periods mean faster career advancement, more efficient learning and quicker achievement of professional goals – without sacrificing academic rigour.
Explore Monash Online’s suite of data science courses:
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