Blog: Predictive Analytics Vs Data Science
In the era of data with 4V ( volume, variety, velocity, and veracity ) I found most of the students / professionals have still a big question regarding the main differences between Predictive Analytics and Data Science. I have come across many students and even my colleagues too who used to be confused about these two words most of the time. Being confused for these two words lead him/her to choose a different path for solving a problem. So I thought to bring the main differences between these two words.
Below are the main differences between Predictive Analytics and Data Science.
- Predictive Analytics is a method of statistical techniques derived from data mining, machine learning and predictive modeling. It uses current and historical events to predict future events or unknown outcomes in the future. While
In Data Science various types of data such as structured, semi-structured and unstructured data in any form or formats available in order are used to get some information out of it.
2. Data Modelling, Data Collection, Statistics and Deployment are the different stages in Predictive Analytics while Data Extraction, Data Processing, and Data Transformations to obtain some useful information out of it are the stages of Data Science.
3. In predictive analytics we try to do forecast for a given historical data set while in case of Data Science we try to get the insights of data given and we use for business purpose. For example forecasting the price of stock on next day / month / year on basis of historical data is Predictive Analytics while in the same historical data getting insight of variations of stock price and possible reasons for that is the part of Data Science.
4. Predictive analytics is the process of creating predictive models and replicates the behavior of the application or system or business model whereas the Data Science is the one that is used to study the behavior of the created model which is about to be predicted.