Data Science vs. Data Analytics: What’s the Difference

Data science and data analytics are both growing fields in a modern world where technology is advancing rapidly. Many of us might not have a working understanding of what these two disciplines are, how they are impacting the world we live in, or what the difference is between these two terms – today, we want to change that! Data science is a larger umbrella term, focused on acquiring knowledge from data using a variety of methodologies; while data analytics is the specific process of analyzing data, which is focused on turning that knowledge into actionable insights. In this blog post, we will discuss the differences between data science and data analytics in more detail, as well as describe how they are being used in the real world.

Table of Contents

What is Data Science?

Data science is a growing, multidisciplinary field that combines the use of many disciplines including statistics, mathematics, computer science and business to collect and analyze data, in order to solve problems or to optimize existing systems.

This process involves the collection of all different types of data, which then gets analyzed for patterns or useful insights with tools such as forecasting, machine learning, and predictive analytics, amongst many others.

How does this work?

The process of employing data science involves the following basic steps:

  1. Identify and understand the problem you want to solve
  2. Collect raw data
  3. Clean and prepare the data
  4. Develop new models based on that data
  5. Test those models
  6. Continue to monitor and update the models on an ongoing basis

Why is this so useful?

In a world where massive amounts of data are now able to be collected and analyzed, having the ability to find these trends and create better solutions for people is a win-win for companies and for consumers. In this way, data science is transforming the business landscape in some incredibly helpful ways!

Not only does it mean that your streaming services are better able to cater to your preferences (one example of data science at work), it also means that businesses can find ways to streamline time-consuming processes and optimize them for the better.

Data science is being employed in a variety of industries, ranging from finance, insurance and healthcare, all the way to energy and manufacturing. Whether that means finding the best routes for deliveries, tracking delayed flights and being able to notify passengers in real time, or companies having better internal processes to serve their own employees — data science is a tool for the future, that is here now.

What is Data Analytics?

Data analytics is the more specific process of examining raw data in order to identify patterns, and then making conclusions from that information. Data analytics is a branch within the tree of data science, one of many tools that can help solve some of the problems we encounter daily. There are a number of different types of data analytics that can be used in order to optimize processes and enhance efficiency for organizations. Some of these types of data analytics include:

  • Descriptive Analytics
  • Diagnostic Analytics
  • Predictive Analytics
  • Prescriptive Analytics

All of these methods can be employed to gain insights and answer a variety of questions for a business. Descriptive analytics might simply show what happened over a specific period of time; whereas diagnostic analytics could be employed to then answer the question of why something happened.

Beyond this, data analytics can help to predict what will happen in the future, or help us understand the most efficient way to employ a variety of processes.

This all may sound a bit vague, but how this shows up in the real world includes:

  • Predicting future sales or purchasing behaviors
  • Detecting and protecting against fraud
  • Analyzing the success of marketing campaigns
  • Increasing efficiency, reducing costs, and helping make better business decisions.

Understanding the Difference Between Data Science and Data Analytics

So, data analytics may be the first step in solving a data science related problem. Engaging data analytics to problem solve will always be engaging in data science. However, data science also includes so many other aspects that other methodologies may need to be utilized after analyzing data in order to fully develop an adequate solution.

While data science is focused on the bigger picture and employs a number of different techniques in order to find trends and solve problems, data analytics is more specific, and is used to answer specific questions by examining data patterns.

We hope this article has helped you understand the differences between data analytics and data science. In the real world, both of these tools are used to help people make better decisions. Data analytics is used to find insights in data so that people can take action. Data science takes it one step further by using machine learning algorithms to find trends and patterns in data so that predictions can be made about future events. Both of these tools are important for making sound business decisions, and we encourage you to continue learning about them so that you can use them effectively. If you need help understanding or collecting your data don’t hesitate to contact us — our experts would be happy to assist you.

Share this Post

Scroll to Top