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Data science and data analytics – What is the difference

Understand the differences between data science and data analytics to bring better value to the business.

Big data has become an integral part of the business world. However, as organisations become more reliant on data, it becomes important to distinguish the tools responsible for cleaning and analysing it. Data science and data analytics tend to be used interchangeably, but there is a difference between the two terms. Understanding the difference is crucial if we are to understand the value they bring to organisations. I am going to address the difference between the two terms, and why it matters.

What is data science?

It’s important to define data science before explaining how it is different from analytics. Data science is a multidisciplinary field consisting of predictive analytics, statistics, machine learning and computer science. The objective is to churn through raw, unstructured data to discover new avenues of study, and find connections between seemingly remote data patterns. The main focus is on finding answers to what we don’t know.

What is the difference between data science and data analytics?

Scope and scale

As you can imagine, the first point of difference between data science vs data analytics is the scope and scale. Data science is much broader, incorporating different elements like machine learning and even analytics tools. The objectives of data science are also much broader in comparison. Data analytics is focused on finding answers to a hypothesis, while data science is about connections and answers without any particular question or hypothesis in mind. Put simply, data science is an umbrella term, while analytics is more focused.

The purpose behind data exploration

The second point of difference between data analytics and data science is the purpose behind exploration. Data science tries to find the connections between data without a question or hypothesis in mind, for the objective is to find potential questions that can be answered in more detail. By contrast, data analytics analyses data with the intent of answering a specific hypothesis. Thus, data science is broader, while analytics is more focused on its exploration of data.

Relevant in different fields

Finally, both data analytics and data science play a major role in different fields. Data science is important in AI, corporate analytics and search engine engineering. Meanwhile, data analytics is vital in industries with immediate data needs like healthcare, travel and business.

Why should the difference matter?

Objectives and targets

Understanding the difference between data science and data analytics is important for organisations. Data analytics and data science use different techniques and will deliver different results, therefore, the techniques should depend on the status of the data set and company objectives. It’s also important to note that data science is used in many cutting edge technologies like AI and machine learning. If companies want to make further advances in AI and machine learning, then more focus is needed in data science. However, this does not mean that data analytics is unimportant. Industries who need to make immediate use of their data to get actionable insights should invest in a suitable data analytics platform.

Different skillsets

Mastery in data science and data analytics requires different skillsets. Data analysts need to have knowledge in mathematical statistics, understand data wrangling, PIG, HIVE and familiarity with R and Python. Data scientists require a strong knowledge of R, Scala, SAS and Python, SQL databases, machine learning and multiple analytical functions. Distinguishing between data analytics and data science means you will need to hire the right people with the appropriate skillsets.

This is especially important because the duties of data analysts and data scientists are very different. A data analyst sifts through data, draws reports and visualises these findings to make sense of specific queries. Meanwhile, data scientists spend a lot of time collecting and cleaning data by finding patterns models and connections, testing hypotheses and conducting experiments.

Key takeaways

While it’s easy to mix the two terms, data science and data analytics are very different terms. It’s important for organisations to understand this difference. Data analytics looks to answer specific queries, while data science is concerned with finding connections and patterns. The difference between data science and analytics requires differing skillsets and knowledge, which is important to bear in mind when hiring a professional, developing technologies, gaining actionable insights or achieving certain objectives. The main point of difference between analytics and science is that the latter is specific, while the former is broad.

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