The emergence of Data Science – How Data Science is Different from Data Analytics Approaches

Okay, folks now that you have learned all about the Data Science and how it functions. So, the next step is to establish the clear meaning of this science because it is quite interlinked with the other data analytics approaches that people sometimes get confused between them.

As you might have observed that data analytics includes some level of prediction and descriptive analysis in it as well. On the contrary, data science also includes some parts of data analytics such as Predictive Causal Analytics and Machine Learning. So, getting confused is a common problem here.

Data Science vs Business Intelligence

If you are familiar with the term business intelligence, then you can easily relate to both terms. It’s very common that both data science and business intelligence are confused with each other. So, if you are also confused between the role and functionality of both these terms, then let us clear some of your confusion with the fine difference.

Business Intelligence

To better understand the meaning and the nature of business intelligence and how it’s different from data science, we have breakdown the business intelligence features. So, that you can easily understand the very fine difference between both the terminologies.

  • BI always scrutinize the previous data records to find hindsight and insight into the particular subject. According to past trends, business intelligence evaluates the future trends of the business.
  • BI enables users to take data from both internal as well as external sources. Data collected from different sources are used to create queries dashboard to answer numerous vital questions like business problems, revenue analysis, etc.,
  • BI is competent enough to predict some future events on the basis of past trends.

Data Science

  • It is a bit forward-looking approach with vast complexities involved in it. It is an exploratory way to focus on analysis.
  • Data science focuses on analyzing the past or current data and predicting future outcomes with the aim of making informed decisions.
  • The answer which is focused on this approach always starts with the “what” and “how”. Like, what is the purpose of this event? How this event occurred?

Clear Difference

Well, now we have a breakdown of the literal meaning of both the terms. So, now you can easily locate the basic difference between both the terminologies. However, if you are still not getting the idea, then don’t worry as we have further mapped out the clear difference for you.

  • Business intelligence only manage the structured data such as SQL, Data Warehouse etc., Whereas data science handles both the structured and unstructured data such as logs, cloud data, SQL, NoSQL, text, etc.,
  • In Business Intelligence, only the statistical and visual approach of data is considered. On the other hand, in data science, different approach such as Statistics, Machine Learning, Graph Analysis, Neuro-linguistic Programming (NLP), etc., are considered.
  • The area of focus of both the terms is different, the operation of BI is based on the past and present data. However, the operational field of data science focuses on the present and future.
  • In BI, the following tools are used; Pentaho, Microsoft BI, QlikView, R. In this science, you have to use these tools; RapidMiner, BigML, Weka, R.

Okay, people that were all about data science and it’s the difference between business intelligence. Now, in the next part, we are going to dig deeper and learn all about the data science lifecycle with a lucrative case study. So, don’t forget to read the next installment of this series.

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