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Difference Between Data Mining and Data Science

We are living in a digital world now. Most of our global economy has become digital. A fundamental transformation is taking place and the focus is more on a wealth of applications. The merging of computing and communications has played a key role in this transformation. The emergence of web and social networks has led to massive amounts of data being generated every single second, which presents both opportunities and challenges for theory. The sheer volume of data calls for a change in our understanding of the data and how to extract usable information from the data. . While traditional areas of computer science remain important, crunching through the massive volumes of data require new age tools and technologies such as Data Science and Data Mining.

What is Data Science?

Data Science is an emerging field of computer science which focuses on data. There’s been a lot of hype in the media about “data science”, but there is a lack of definitions around the most basic terminology. What is Data Science anyway? How is Data Science related to Big Data? Data Science is an inter-disciplinary field that uses a blend of tools, algorithms, and machine principles to extract usable information from both structured and unstructured data. Data science is not just statistics or machine learning but rather a filed unto itself, which deals with data analysis and modeling to understand the complex world of data. A data scientist is the one responsible for this job; he collects data from a variety of sources, organize and analyze the data, and then communicate the findings in a way that effectively affects business decisions. The goal is to extract useful insights from data.

What is Data Mining?

Data mining is the process of discovering anomalies, patterns and correlations within large sets of raw data to extract useful information. Data mining is knowledge discovery from the vast amounts of data collected on a daily basis. It simply turns a large collection of raw data into knowledge. It is related to machine learning and can be described as the science of extracting useful information from large data sets or databases. Data mining can be applied to a variety of fields as a data analysis method for finding results. It can be viewed as a result of the natural evolution of information technology. The goal of data mining is to discover properties of existing data that were previously unknown and to find statistical rules or patterns from those data in order to solve complex computing problems. In simple terms, data mining is knowledge mining from data.

Difference between Data Mining and Data Science

Meaning

 – Data Science is an inter-disciplinary field of computer science that uses a blend of tools, algorithms, and machine principles to extract usable information from data both structured and unstructured. It is an emerging field of study that focuses on understanding the complex world of data. Data Mining, on the other hand, can be described as the science of extracting useful information from large data sets or databases. Data mining can be used as a synonym for another popularly used term, ‘knowledge discovery from data’, or KDD.

Goal 

– Data mining is a process that is used to turn raw data into usable information. The goal of data mining is to discover properties of existing data that were previously unknown and to find statistical rules or patterns from those data in order to solve complex computing problems. Data Science is not just statistics or machine learning but rather a filed unto itself. The goal of data science is to utilize certain specialized computational methods to discover meaningful and useful information within a dataset in order to make important decisions.

Field 

– Data Science is a multidisciplinary field that includes a number of related areas such as database systems, data engineering, data analysis, visualization, predictive modeling, experimentation, and business intelligence. Data science covers a wide range of techniques, applications, and disciplines. Data mining, on the other hand, is all about uncovering valuable information from the tremendous amounts of data and to transform such data into organized knowledge. Data mining is just a part of a broader KDD process whereas Data science is a combination of techniques and processes that may also include data mining.

Data Mining vs. Data Science: Comparison Chart

Summary of Data Mining vs. Data Science

In a nutshell, data mining is a process that is used to turn raw data into usable information while data science is a multidisciplinary field that involves capturing and storing of data, analyzing, and deriving valuable insights from the data. Data science utilizes certain specialized computational methods to discover meaningful and useful information within a dataset in order to derive valuable insights from the data to positively impact business operations. Data mining is just a process of crunching through existing databases to generate new information.

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References :


[0]Blum, Avrim et al. Foundations of Data Science. Cambridge, England: Cambridge University Press, 2020. Print

[1]O’Neil, Cathy and Rachel Schutt. Doing Data Science: Straight Talk from the Frontline. Sebastopol, California: O’Reilly Media, 2013. Print

[2]Song, Juyoung and Tae Min Song. Big Data Analysis Using Machine Learning for Social Scientists and Criminologists. Newcastle, England: Cambridge Scholars Publishing, 2019. Print

[3]Han, Jiawei et al. Data Mining: Concepts and Techniques. Amsterdam, Netherlands: Elsevier, 2011. Print

[4]Kotu, Vijay and Bala Deshpande. Data Science: Concepts and Practice. Burlington, Massachusetts: Morgan Kaufmann, 2018. Print

[5]Image credit: https://en.wikipedia.org/wiki/File:Data_science.png

[6]Image credit: https://c1.staticflickr.com/5/4169/34764532445_e3883bd446_b.jpg

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