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]]>1- An overview of data mining applications
If you’ve made up your mind to learn data mining, here are some applications of it, knowing which would help you choose your career path:
- Market Basket Analysis: This modeling technique is based upon a theory that buying a specific set of articles makes you more inclined to buy another group of articles. A retailer can use this technique to recognize a buyer’s purchase behavior, which can then be used to tweak the offerings or change the store’s layout to encourage buying. The use of differential analysis comparison of results between customers in different demographic groups or even between stores at different locations can help retailers make the necessary changes to boost sales.
- Fraud Detection: Unlike time-consuming and complex traditional methods of fraud detection, data mining can help find meaningful patterns in data. This information can then be used to collect sample records, classify them as fraudulent or non-fraudulent, and build a model using this data where the algorithm is designed to spot whether the record is fraudulent or not.
- Future Healthcare: Using data mining approaches such as machine learning, multi-dimensional databases, statistics, data visualization, and soft computing, the number of patients in each category can be predicted. This can help develop processes that ensure each patient gets appropriate care at the right time and the right place. Apart from helping to improve healthcare and reduce costs, data mining can also assist healthcare insurers in identifying fraud and abuse.
- Education: EDM or Educational Data Mining helps predict the future learning behavior of students, and even lets you study the effects of educational support as well as how advanced scientific knowledge can help in learning. Thus, data mining can help educational and training institutes focus on what they should teach and the ways to effectively teach apart from predicting the results of the students.
Apart from the above, many other industries like banking, transportation, manufacturing, etc. can also gain from data mining and data science.
2- How to learn data mining?
If you have your eyes set on the field of data science and want to master data mining, you can either get enrolled in a full-time course or find some bootcamps to join where you’ll learn all that you need to, albeit much faster than a traditional course. In case you plan to use bootcamps, remember that you’ll need a good statistical and machine learning foundation to understand what’s being taught and apply this knowledge to get useful information by cutting the noise of Big Data.
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To learn more about data mining, click here and read our another article.
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]]>The post 7 Awesome Difference Between Data Science vs Data Mining appeared first on Magnimind Academy.
]]>- Probably the biggest difference between data science and data mining lies in their terms. Data science is a broad field that includes the processes of capturing of data, analyzing, and deriving insights from it. On the other hand, data mining is mainly about finding useful information in a dataset and utilizing that information to uncover hidden patterns.
- Another major difference between data science and data mining is that the former is a multidisciplinary field that consists of statistics, social sciences, data visualizations, natural language processing, data mining etc while the latter is a subset of the former.
- The role of a data science professional can be considered as a combination of an AI researcher, a deep learning engineer, a machine learning engineer, or a data analyst, to some extent. The person might be able to perform the role of a data engineer as well. On the contrary, a data mining professional doesn’t necessarily have to be able to perform all these roles.
- Another notable difference between data science and data mining lies in the type of data used by these professionals. Usually, data science deals with every type of data whether structured, semi-structured, or unstructured. On the other hand, data mining mostly deals with structured data.
- If you consider the nature of work in the fields, you’d be able to find another difference. In data science, you’re not only finding patterns and analyzing them which are key components of the data mining Instead, with the help of data science tools and technologies, you’re expected to be able to forecast future events by leveraging the present and historical data.
- The term data science has been around for a long time – since the 1960s whereas data mining became known in the 1990s, amongst the database communities.
- While the field of data science concentrates on the science of data, the field of data mining is mainly concerned with the process.
Final Takeaway
When it comes to handling the steadily increasing amount of data, both data science and data mining play crucial roles in helping businesses in identifying opportunities and making effective decisions. So, while the objective of both these fields remain similar – to derive insights that can help a business to grow – the key differences lie in the tools and technologies used, nature of work, and in the steps to perform respective responsibilities to attain that objective.
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To learn more about data science, click here and read our another article.
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