Introduction
Businesses don’t just rely on their gut feelings. They rely on data. Without data, they can’t make the right decisions. Every single click, purchase and customer interaction creates valuable data. This data is then used to make the right decisions.
But there’s a catch in this statement. The catch is that making sense of data isn’t that easy! There’s where data science and business intelligence (BI) comes into play. In this blog, i’m going to break down the key differences between the two.
Moreover, if you’re eager to learn more, then I’ll also mention an awesome platform which gives free data science courses. This platform also offers real world data science case studies to clarify your doubts better.
But firstly, let’s discuss the difference between data science and BI.
Core Focus: Predictive vs. Descriptive Analytics
If you want to understand the difference between data science and BI, you should first understand their core purposes. I’ll talk about each of them one by one.
Data Science
The core area of data science is helping businesses make future decisions. It identifies patterns in large data sets. To do so, it uses machine learning, artificial intelligence, and statistical models. Using these techniques, data science predicts what might happen next.
For example, Flipkart or Amazon uses data science to suggest products that you might buy. How do they do so? They use your browsing and purchase history.
Business Intelligence
Now, let’s see the core focus of business intelligence (BI). It is all about analysing past data to understand trends and make better business decisions. It organises data into reports, dashboards and charts. These visual representations help in showing what has already happened.
Let’s consider example of a retail store. It can use BI to track its previous month’s sales and see which products sold the most. Similarly, a hospital can use BI tools to analyse previous records of their patients and notice common health issues in a specific age group of people.
Skillsets and Tools in Each Domain
In this section, I’m going to talk about different skillsets and tools used in data science and BI. While both of these domains involve working with data, their approach and required skills are different.
Data Science
Data science uses programming languages like Python and R. These languages help in building models that predict future trends. Data scientists often have to work with large amounts of data. They apply techniques like machine learning and artificial intelligence to find hidden patterns in the data.
This domain often involves using cool tools like TensorFlow, Scikit-learn and Jupyter Notebook. These tools help in analysing and visualising data.
Business Intelligence
Talking about business intelligence (BI), it focuses on organising and summarising past data. This data helps organisation make better decisions. BI professionals often use tools such as Microsoft PowerBI, Tableau and SQL. These tools helps in creating dashboards ad reports.
With the help of BI tools, professionals can turn raw data into easy-to-understand charts and graphs. These tools help in creating aesthetically pleasing visualisations which aid in a better understanding of data.
Both data science and business intelligence (BI) market are increasing day by day. There’s a huge demand for professionals with right skills and knowledge. If you are thinking about making your career in these domains then it is the right time for you to start.
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Conclusion
Both data science and business intelligence (BI) help in making businesses take data-driven decisions. Although they both involve working with data, they serve different purposes. Data science involves predicting future trends. On the other hand, BI involves using past data to showcase trends and insights.
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