The concept of ‘Data Science’ can be visualized as a wide umbrella, giving shade to the many skills and concepts falling under it. Some of the skills are as follows:
Data science is inclusive of the various skills
· Machine Learning
· Data Mining
1. Machine Learning
The study that involves learning of statistical models and algorithms which computer systems utilize to their benefit for improving their efficiency in performance. It gives importance to the growth of computer based programs which can then tap into the information and learn it by themselves. Machine learning can be defined as an application of AI (Artificial Intelligence) providing systems with the potential of learning by themselves, and also improving based on their past experiences.
The existing data is called into play by the computer to design models for analysis of certain circumstances. Thus, machine learning is associated with predictive analysis. This data is then further utilized to predict more results / products to solve problems.
Machine learning is counted among the many disciplines falling under the term ‘Data Science.’ Artificial intelligence (AI), Machine learning and data science are some of the concepts introducing data science. Machine science in turn also helps in determining unique patterns in overwhelming amounts of data. Collection, storage, and visualization of such large amounts is not possible by humans. Machines can, however, do this conveniently by sifting through large chunks of data in a minimal amount of time and provide quick results. Thus, saving us a lot of time and effort on the front.
2. Data Mining
The process of discovering unique patterns in large chunks of data sets is known as data mining. It takes into account different perspectives and categorized the information or data accordingly.
Sometimes, data mining can be done for figuring out data based on the same algorithms that were applied in Machine learning. Often times people mistake one for the other considering Data Mining and Machine Learning to be similar, however this isn’t the case. There is an interconnectivity between both the prospects, however they are very diverse.
Data Mining vs. Machine Learning
The applications of data science include cluster analysis. It is also used to derive data from the existing information. Whereas machine learning is used for the purpose of detection of spam / fraud, credit scoring, filtering spam, web search and computer design. Machine learning provides access to the computer for learning and understanding the rules stated.
Data mining can be used on self-made models, whereas the algorithm of Machine Learning can be used in areas of Artificial intelligence and neural networks.
The scope of data mining is less compared to Machine learning. The latter can be applied to a broad area, unlike the first.
Data mining involves human intervention inclined towards manual. Whereas Machine learning is mainly automated, with zero requirement of human input and effort. After it has been designed, it becomes self-implemented.
Machine Learning possesses ability to learn and improve by itself using past experiences as a basis. Data Mining is incapable of doing so.
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