An infinite amount of data are created every single day, and it is going to grow from there. Within a business environment, if we are able to use data with the right way and strategy, creating a product or service that suits customer needs will not become a big challenge for most businesses. However, processing data is not a simple tasks for companies. It takes the right capabilities and solutions to help companies overcome big data challenges.

In a data management, we probably hear about the terms of data analytics and data science all the time. While many people use the terms interchangeably, data analytics and data science are unique fields, with the major difference in their scope. They sound so similar because both of them are dealing with big data.

However, did you consider them as the same term? If yes is the answer, then you need to read  this article. In fact, data analytics and data science have different implications for business although they may take big data as the same “weapon”.

What are the differences between data analytics and data science? Let’s take a closer look on the explanation below!

·  Data Analytics

Before concluding the differences between data analytics and data science, let us first dive in to the definition of both of them.

Data analytics is used to get conclusions by analyzing data from multiple sources – with different types and sizes. The conclusion is helpful for businesses as it helps companies to make data-driven decisions and make the decisions more valuable for the future of a company’s business.

In most cases, data analytics use several tools and techniques to analyze big data as opposed to change human intervention and manual processes that lead to inaccurate results. Data analytics consist of these following steps:

  1. Determining the data grouping – data can be grouped to most appropriate such as age, location, gender, interests, lifestyle, and more.
  2. Collecting data from various sources – Data is collected from both online and offline sources such as company’s resources, surveys, social medias, and more.
  3. Organizing the data for analysis.
  4. Sorting incomplete and duplicated data sets before analysis. In this step, any errors in the data have been removed and data is ready to be analyzed.

·  Data Science

Data science combines mathematics, statistics, computations to streamline the process of data analysis. It works by applying algorithms to make systems that are supported by AI and machine learning, then it is used to analyze data.

So, the process of designing the way data is stored and ready to be analyzed is for data science. In most cases, data science consists of these following steps:

  1. Statistics – This process is related to the collection, analysis, interpretation, and the presentation of data through mathematical methods.
  2. Data visualization – This process works by changing data into visually appealing diagrams, charts, and graphs which makes it simple to be viewed and understood.
  3. Machine learning – The process of making machine learning model is an essential component in data science, since it impacts on the accuracy of customer’s behavior and interest prediction.

The Differences between Data Analytics and Data Science

To sum up, if we have understood the definition of each term, then we can conclude that the differences between data analytics and data science are in their scope of processing big data.  We can say that data analytics is one of the phases of the data science lifecycle. What happens before and after analyzing the data is all part of data science.

Both data analytics and data science deal with pools of data. However, the difference lies in what they do with the data. Data science design new processes in order to produce data modeling to make custom analysis and make the results as accurate as possible for each company.

Meanwhile, data analytics comes after data science to examine a large set of data and finding out the trend based on the data. Furthermore, the results of data analysis will help businesses chart up a better strategy.

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