What is the difference between Data Science and Data Analytics?

In the modern era of cloud-based technologies, Data Science🛰️is predictive while Data Analytics🚀 are descriptive. Data Science seeks to optimize judgments and anticipate future situations, whereas Data Analytics relies on previous events to guide decisions.

🛰️Data Science involves a great deal of cleaning and massaging the data, followed by analysis and building predictive models and forecasting. The data scientist gets to experiment and try different techniques to analyze their datasets with various approaches. After that, they’ll often report their own findings and draw their own conclusions.

On the other side, Data Analytics🚀is mainly concerned with business intelligence and descriptive data. So data analysis involves more than just running numbers and spreadsheets and creating plots and figures for reports. They rarely conduct original research because they rely on others to do it.

Data science is the practice of manipulating data in order to discover relationships that would otherwise go undetected. These relationships can be utilized to develop algorithms for prediction and categorization. For example, you might develop an algorithm for facial recognition, forecasting inflation, or something as sophisticated as ChatGPT.

Data analytics is about…