Data Mining vs Data Science - A Detailed Comparison - ACEIT Jaipur

 Businesses and organizations begin realizing the enormous value that is concealed in the large amounts of data that are captured on a daily basis. This led them to start introducing and employing different techniques to realize that potential and value. The ultimate goal of the students of best engineering colleges in Jaipur is to derive actionable insights from data. However, this introduced the need for a significant number of technical terms. 

What Is Data Science?

Data science is an interdisciplinary field or domain that involves the use of scientific methods, algorithms, processes, and systems to extract knowledge and insights from a large amount of structured and unstructured data. In turn, this is used for building predictive, prescriptive, and prescriptive analytical models. 

Data science is related to big data, deep learning, and data mining. It is an intersection of data and computing and is all about digging, capturing (building the model), analysing (validating the model), and utilizing the data (deploying the best model). Data science blends business with computer science and statistics. 

Different Process Of Data Science 

The six steps involved in the data science process are:

1. Framing the Problem - Before solving a problem, it is important for the students of top engineering colleges in Jaipur to know what the problem is; data questions first have to be translated to actionable business questions.
2. Collection of the Raw Data Required for the Problem - The required data has to be gathered to derive insights and probable solutions by scanning internal databases or purchasing databases from external sources.
3. Processing the Data for Analysis - The data has to be processed before going further and analyzing it for more accurate insights.
4. Exploring the Data - This crucial step involves developing ideas to help identify hidden patterns and insights. 
5. Performing In-depth Analysis - In this stage, mathematical, statistical, and technological knowledge and data science tools have to be implemented to crunch the data successfully and discover and derive every insight possible as well as other crucial factors. The quantitative and qualitative data can be combined and moved into action.
6. Communicating the Results of the Analysis - In this step, insights and findings are conveyed to the sales head to make them understand the importance of the findings and how they can help in business growth. 

Applications Of Data Science

Some major applications of data science include Fraud and risk detection, Targeted advertising, Speech recognition, Healthcare, Website recommendations, Advanced image recognition, Internet search, Airline route planning, etc. 

What Is Data Mining?

Data mining is used by the retail companies and the financial community for the purpose of analyzing data and identifying trends to increase customer base, and predict fluctuations in stock prices, interest rates, and customer demand.

Data mining is the process of identifying patterns in large datasets. It involves methods at the intersection of database systems, statistics, and machine learning. The overall goal for the students of engineering colleges Jaipur of this interdisciplinary subfield of computer science and statistics is to extract information from large datasets or libraries of data by using sophisticated mathematical algorithms and transform them into a comprehensible structure for further use.

Data mining helps derive insights through careful extraction, reviewing, and processing of raw data to discover patterns and correlations that can be valuable for businesses. Data mining processes include different types of services like Web mining, Text mining, Audio mining, Video mining, social network data mining, Pictorial data mining, etc. 

Data mining also referred to as knowledge discovery in data. It is performed with the help of simple or advanced software. The following steps are involved in data mining:

1. Business Understandings - It involves introducing and understanding the objective and work of the business as well as understanding the significant factors that will help achieve the target of the business.
2. Data Understandings - It performs data collection and data accumulation. The data is listed based on the source data, its location, how it was achieved, and if any issues had cropped up. The data is then visualized and checked for its completeness.
3. Data Preparation - It involves the selection of useful data, cleaning it, constructing attributes from it, and data integration from multiple databases.
4. Modeling - It involves selecting data mining techniques, generating a test design to evaluate the selected model, building a model from the datasets, and evaluating the model with experts of BTech colleges Jaipur to know the result.
5. Evaluation - It determines the degree to which the resulting model meets the business requirements by testing it based on real applications.

Applications Of Data Mining

Some major applications of data mining include Market analysis, Financial analysis, Higher education, Fraud detection, etc. 

Conclusion 

Whether it is data science or data mining, when it comes to handling the exponentially growing data volume, both play a crucial role in helping businesses identify opportunities and make sound decisions by the students of private engineering colleges in Jaipur. 

So, while the goal of both data science and data mining is, in a way, similar, i.e., to derive insights for helping businesses perform better and grow, the key differences lie in the tools and technologies that are implemented, the nature of work, and in the stages involved in performing the respective responsibilities to achieve that goal.