Predictive Analytics vs Data Science

Updated June 2, 2023

Difference Between Predictive Analytics vs Data Science

Predictive Analytics is a process of statistical techniques derived from data mining, machine learning, and predictive modeling that obtain current and historical events to predict future events or unknown outcomes.

Data Science is the study of various data types, such as structured, semi-structured, and unstructured data in any form or format available to get information.

Predictive analytics is an area within Statistical Sciences where the current information will be extracted and processed to predict trends and outcomes. The subject’s core lies in analyzing existing context to predict an unknown event.

Data Science consists of different technologies used to study data, such as data mining, data storing, data purging, data archival, data transformation, etc., to make it efficient and ordered.

Predictive analytics can be applied to predict not only an unknown future event but also present and past events.

Data Science is useful in studying internet users’ behavior and habits by gathering information from the users’ internet traffic and search history. This is how the recommended ads will be displayed for a user on their web browsing pages without their input.

Head-to-Head Comparison Between Predictive Analytics vs Data Science (Infographics)

Below are the top 8 Difference Between Predictive Analytics vs Data Science:

Key Differences Between Predictive Analytics vs Data Science

Following is the difference between Predictive Analytics vs Data Science:

• Predictive Analytics is an area of Statistical Science where a study of mathematical elements is proven helpful in predicting different unknown events, be it past, present, or future. Data Science is an interdisciplinary area of multiple scientific methods and processes to extract knowledge from existing data.
• Predictive Analytics has different stages, such as Data Modelling, Data Collection, Statistics, and Deployment. In contrast, Data Science has stages of Data Extraction, Data Processing, and Data Transformations to obtain helpful information.
• Many techniques are used in Predictive Analytics, such as Data mining, Artificial Intelligence, Machine learning, Statistics, modeling, etc., to analyze existing data to predict unknown future events. Data Science is the processing of existing information to manage, organize, and store it in a required manner.
• Predictive Analytics uncovers the relationship between data types, such as structured, unstructured, and semi-structured. Structured data is from relational databases; unstructured is like file formats; semi-structured is like JSON data. Data Science consists of different tools to handle different data types, such as Data Integration and manipulation tools.
• The steps in Predictive Analytics include Data Collection, Analysing, Reporting, Monitoring, and Predictive Analysis, the main stage that determines future outcome events. In contrast, Data Science contains Data Collection, Data Analysis, extracting insights from the analyzed data, and utilizing the extracted data for business purposes.
• Predictive analytics has many applications in industries such as Banking and Financial Services, Fraud Detection, Risk Reduction, and improving operations. Data Science applications are digital advertisements, internet search, recommender systems, image and speech recognition, price comparison, route planning, and logistics, etc.,
• The Predictive Analytics applications cover industries such as Oil, Gas, Retail, manufacturing, health insurance, and banking sectors. Data Science primarily covers technological industries.
• Predictive Analytics comes as a subset of Data Science. Data integration and data modeling come from predictive modeling. Data Science has everything from IT management to data analytics.
• Predictive analytics is creating predictive models and replicating the behavior of the application, system, or business model. In contrast, Data Science is the one that is used to study the behavior of the created model which is about to be predicted.
• For example, A banking or financial institution has many customers. The customer behavior will be analyzed by collecting the data from existing information and predicting the future business and prospective customers where they are about to show their interest in banking products. This helps the banking business grow efficiently by using a predictive model.
• The ultimate goal of Predictive Analytics is to predict the unknown things from the known things by creating some predictive models to drive the business goals successfully. In contrast, Data Science aims to provide deterministic insights into the information we do not know.

Predictive Analytics vs. Data Science Comparison Table

Below is the comparison table between Predictive Analytics vs Data Science.

Conclusion

Predictive Analytics is the process of capturing or predicting future outcomes or unknown events from existing data, and Data Science is obtaining information from existing data. Predictive Analytics will be greatly useful for companies to predict future business events or unknown happenings from the existing datasets.

Data Science will be valid for processing and studying data from existing information to get useful and meaningful information. Predictive Analytics and Data Science are crucial in studying and driving a company’s future, aligning to successful pathways.

Predictive Analytics is the best way of representing the business models to the managers, business analysts, and corporate leaders simply and excellently on how the businesses are evolving in daily meetings.

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