In this digital age, data drives almost every decision from what series to binge-watch next to how companies plot their next move. As concepts including data science and machine learning begin to emerge, it is helpful to better understand what they mean and any distinctions between the two. 

Even though the two are often closely associated, understanding data science vs machine learning is important for employees and companies, as well as simply curious people. This blog will help clarify these terms by exploring what each discipline represents, where they intersect, and when to use one term versus the other.

What is Data Science?

Data science can be thought of as the entire process for making sense of data, and includes everything from obtaining and cleansing raw data to analyzing it to presenting the results in a report or dashboard. Whether you want to find trends in customer behavior or understand why a product you launched is not selling, data science helps you answer the “why” behind the numbers.

Data science is interdisciplinary and pulls from many fields, such as statistics, computer science, and sometimes even domain knowledge like marketing or healthcare. Data Scientists also use a variety of tools, including Python, SQL, and R, and utilize platforms like Tableau to explore patterns and tell stories with the data.

What is Machine Learning?

On the other hand, machine learning is a method in the data science universe and a big part of artificial intelligence. The concept is very simple: instead of writing rules for a computer to follow, you allow algorithms to study and learn on their own from historical data while making their predictions and decisions.

For instance, when your email filters out spam or Netflix is recommending your next favorite show, that is machine learning. Machine learning relies on algorithms, such as decision trees, neural networks, or linear regression, to discover patterns and learn, thus improving over time.

In the machine learning vs data science debate, machine learning strictly works on developing predictive models, while it encompasses everything that surrounds it.

Is Machine Learning Part of Data Science?

Indeed, yes. Machine learning is one of the primary methods that data scientists use in predicting future outcomes or building systems that can improve automatically. Think of it like this: data science is like the whole workshop, and the machine learning presentations are the power tool you are using. 

Machine learning is just the engine that takes over during the prediction projects, like predicting customer churn or stock prices. But before you can start using it as the engine, you still need clean, trusted data, context, and some way to present your findings, which are all parts of data science.

Data Science vs Machine Learning: Key Differences

Let’s break down the main differences between the two in a simple table:

In the context of comparing data science vs machine learning vs AI, it’s important to know that AI is the overarching umbrella, that ML is part of AI, and that data science only uses ML when it makes sense.

Data Science vs Machine Learning in a Nutshell

Simply put:

• Data science = a larger field that includes collecting, exploring, and generating insight.
• Machine learning = a field that is focused on the creation of models that get better through experience.

In brief:

• Data science = interpret
• Machine learning = predict

Machine learning is within the data science toolkit.

Applications of Data Science

Data science is influencing how organizations operate in all industries:

Healthcare and Life Sciences

• Identifying the disease earlier for physicians
• Tailoring treatment plans based on patient data and histories
• Observing outbreaks for public health reasons

Finance

• Identifying fraud in real time
• Determining credit risk more effectively
• Identifying trends in the markets for better investment decisions

Retail and E-Commerce

• Anticipating demand, so that there is no overstock or depletion
• Understanding what the customer wants before they ask
• Adjusting prices based on market trends

Manufacturing

• Identifying machinery issues before they fail
• Improving quality control, based on data
• Coordinating supply chain logistics

Applications of Machine Learning

While data science can help businesses understand what’s happening, machine learning can help them act more quickly and intelligently. Here are a few examples of applications in different sectors:

Healthcare and Life Sciences

• Reading and interpreting medical imagery
• Flagging patients who are at higher risk for complications
• Auto-generating clinical summaries

Finance

• Powering chatbots to handle customer questions
• Predicting how stocks will move based on sentiment in the news
• Optimizing investment portfolios

Retail & E-commerce

• Enabling image search (the “find similar” feature)
• Forecasting which products will strike next month
• Recommending specific products

Manufacturing

• Programming smart robots to learn in the moment
• Foretelling the failure of parts in real time
• Increasing inventory accuracy through automation.

Data Science vs Machine Learning: Which is Better?

It’s not about which is “better”—it’s about your needs. If you need to figure out why customer churn has increased, use data science. If you are trying to predict which customers will churn in the next month, machine learning is the right choice. 

The two work better together—a data science approach allows you to comprehend the data, and machine learning allows you to act upon it. So, when you are deciding between machine learning vs. data science, always start with your business problem, and let that be your guiding paradigm.

Curate Data Solutions: From Insights to Impact

At Curate Data, our passion is transforming raw data into something impactful in the real, everyday world. From helping a brand with consumer retention to better inventory planning, we use data science and machine learning to build valuable, real-world, and scalable solutions. 

Whether it’s predictive modeling, real-time dashboards, automated analytics, or AI-supported decision-making, we provide the right platform to allow you to make quicker and more informed decisions.

Conclusion

In a world filled with limitless data, understanding the difference between data science vs machine learning is not just trivial; it’s a competitive advantage. Data science relies on extracting meaning, while machine learning relies on acting on meaning. 

These fields, when combined, are a force of nature that is transforming multiple industries, including health care, finance, retail, and many others. Knowing where data science and machine learning fit allows organizations to challenge themselves to innovate, stay ahead of the pack, or at least make better decisions.

FAQs

1. Is machine learning part of data science?

Indeed, machine learning is an important piece of data science. Data science refers to the complete data lifecycle, from collecting and cleaning data, analyzing it, and finally presenting the results. Machine learning is used primarily to build models that predict or automate decisions.

2. Which is better for business insights: data science or machine learning?

It all comes down to your objective. Data science is beneficial for exploring patterns and insights. Machine learning is more useful when you want to automate decisions and/or predict the future. Best to do both simultaneously to get the highest value.

3. What tools are commonly used in data science and machine learning?

• Data science: Python, R, SQL, Tableau, Power BI 
• Machine learning: Scikit-learn, TensorFlow, Keras, PyTorch, XGBoost 

4. How do I decide whether to use data science or machine learning for my project?

If you want to find patterns or trends in the data or produce reports for your business, consider data science. However, if you need to do predictions, automations, or decisions that are happening in real-time, you likely want to do some machine learning.