Data Science vs. Data Analytics – What’s the Key Difference?

Introduction

Today, data is playing a prominent role in the growth of any industry. Many organizations are using this data to gain insights and make effective business decisions to stay ahead in the market competition. This increased demand for data drives the need for skilled professionals who specialize in analyzing and interpreting that data. It’s easy to get confused between data science and data analytics. Both are used to analyze information, but there are some key differences between them. In this article, we take a look at the key difference between data science vs data analytics.

Data Science and Data Analytics are Different

data science vs data analytics both incredibly important fields in the modern world. Both deal with the use of data to help make decisions and solve problems.

Data analytics and data science are two areas that are often confused with each other. The biggest difference between these two fields is their goals. 

Data analytics focuses more on analyzing an existing dataset, whereas data science focuses on creating new models to generate the best outcomes possible. 

What is Data Science?

Data Science is the study of data-driven decision-making. Data Scientists use data to make predictions and to find patterns in data. They create algorithms and models that help them make predictions. 

Data science is a broad term that includes many disciplines, including statistics, machine learning, and computer science. These fields are used to create new algorithms and models for processing large amounts of data.

What is Data Analytics?

Data analytics is the process of collecting, analyzing, and interpreting data to make better business decisions. 

It’s a unique way to get an objective picture of your business, and understand what’s working, what isn’t and what needs to change. Data analytics help find out if your marketing strategy is working or not. 

When you’re running marketing campaigns on social media or sending newsletters out to your customers, you want to know that you’re reaching the right people with the right message. And you want to know that your efforts are paying off.

1. Data Preparation

Data preparation involves cleaning up your data so that it is ready to be used in your analysis. 

2. Modeling & Forecasting

Modeling is the process of transforming your raw data into a format that can be used to generate predictions. 

3. Visualization & Reporting

Once you’ve completed the modeling process, it’s time to visualize your results using tools. It allows for the easy creation of reports with professional-looking graphs and charts. They can be shared with colleagues or clients.

Although data science and data analytics are similar in many ways, they differ in their focus. Data science is more focused on the scientific side of things, while data analytics is more focused on the business side of things. 

For example, it’s common for someone who works as a “data scientist” to also work as a “data analyst” or vice versa; however, it’s not typical for someone who works as an “analyst” to also work as an “expert.” 

Examples of Data Science & Data Analytics

1. Purchase Decisions

Let’s say you have a dataset of customer purchases over time and want to know what factors affect their purchasing decisions. You can use regression analysis or machine learning to decide which variables most strongly correlate with more purchases. This would be an example of data analytics.

2. Predicting Customer Behavior

Whereas, if you want to figure out how to predict how much customers will spend at a particular store next week. Then it would be an example of data science. Because it involves applying algorithms and machine learning techniques to generate predictions.

Skills of Data Scientists

Data Science is a very broad and diverse field. It’s difficult to define the field of Data Science because it consists of interdisciplinary areas that include statistics, computer science, data engineering, and machine learning. 

• The best way to understand what a Data Scientist does is to compare it with other fields like programming, information technology (IT), analytics, and business intelligence (BI).
• In comparison to IT specialists who use traditional programming languages like Java or C++ for developing apps for businesses or corporations, Data Scientists know both IT as well as statistics.
• Data scientists use different statistical techniques on large datasets using tools such as the Hadoop framework for processing big data in parallel clusters.

Skills of Data Analysts

• Data analysts use data to solve business problems. They use data to make decisions, present information and make predictions.
• They are also responsible for optimizing business processes by using data. Data analysts are responsible for analyzing data and providing insights to the business.
• They are often called upon to work with other teams to provide these insights, so they must be able to communicate effectively and build relationships.
• Data analysts must also be able to think critically about their work, using logic and reasoning skills to arrive at decision-making.

Here’s a Table that Compares Data Science vs. Data Analytics:

Roles & Responsibilities of Data Scientists & Data Analysts

• Data science is a broad field that covers a wide range of topics. It’s the study of data and how it can be used to reach certain goals, like improving business processes or creating better products or services.
• Data analysts are more focused on the analysis of data, but they’re not necessarily involved in creating the information from which they analyze.
• Data scientists are more likely to be involved in the creation of data as well as its analysis, but not all data scientists do both tasks equally well.
• There are many different types of professionals who fall under this umbrella term—the difference between them depends on how much emphasis they place on each part of their job. (creation vs analysis)
• It also depends on what tools they use for each task and how far along in their career path they’ve progressed (junior vs senior).

Conclusion

As we’ve seen, start a travel agency business online two very different disciplines. The skills required for each job are of course different. Hence, it’s important to understand what these roles & responsibilities entail before hiring them. 

Both data science and data analytics are essential entities for the growth of all types of organizations. Data analytics is more about the what, while data science is more about the why. Data analytics is more about the present. While data science is more about the future. And finally, data analytics focuses on numbers and facts whereas data science focuses on people and emotions.

FAQs for Data Science & Analytics