Data mining. How to turn data into gold and why use Java for this?

In publications on JavaRush, we try to regularly review professions, niches and specializations in the IT field. First of all, those that actively use the Java programming language and platforms and solutions written on it. Today we’ll talk about Data mining (“data mining”, “data mining”, “in-depth data analysis” or simply “data mining” in the Russian interpretation). "In god we trust. Everything else needs data to be believed.” William Edwards Deming, American scientist and statistician.

What is Data mining?

Data mining is a collective name that is used to describe a number of methods for studying and analyzing large volumes of data to identify patterns and rules in them. Data mining is considered a distinct discipline within the field of data science. If we talk about the widespread use of knowledge and developments in this area, companies most often use Data mining to extract useful information from data. By using software solutions to find patterns in large volumes of data, companies can study consumer behavior and habits to develop more effective marketing solutions, increase sales and reduce costs. In addition, data mining techniques are used to build machine learning (ML) models, which are used in modern artificial intelligence applications such as search engine algorithms and recommendation systems, for example. “You can have data but not information, but there is no information without data.” Daniel Keys Moran, programming expert and writer.

How is Data mining different from Big Data?

It will also be useful to immediately clarify how data mining as a concept differs from Big Data (by the way, we have a separate article on the use of Java in the field of Big Data ). To put it simply, the term Big data refers to all aspects of large volumes of data of various kinds, including both structured and unstructured data, their collection, storage, classification, etc. Whereas Data mining refers solely to diving deep into data to extract key insights, patterns and similarities, and other information from data of any size (both large and small). Thus, both concepts relate to data and generally overlap, but Data mining is about using the collected information for specific purposes. “Without deep data analysis, companies see and hear nothing; online they are as helpless and confused as a deer running onto the freeway.” Geoffrey Moore, writer and management theorist.

Areas of application Data mining

In-depth data analysis, as you understand, is used very widely. Let's take a quick look at those industries and areas of activity where it is used most often.

• Marketing and targeting target consumer groups in retail.

  More often than others, data mining is used by retailers to better understand the needs of their customers. Data analysis allows them to more accurately divide consumers into groups and tailor promotions to them.

  For example, grocery supermarkets often offer customers a loyalty card, which opens up discounts that are not available to others. With the help of such cards, retailers collect data on what purchases are made by certain groups of consumers. The application of in-depth analysis to this data allows you to study their habits and preferences, adapting the assortment and promotions to take this information into account.

• Management of credit risks and credit histories in banks.

  Banks are developing and implementing data mining models to predict a borrower's ability to take out and repay loans. Using various types of demographic and personal data of the borrower, these models automatically determine the interest rate depending on the risk level of each client individually.

• Detecting and combating financial fraud.

  Financial organizations use Data mining to detect and prevent fraudulent transactions. This form of analysis applies to all transactions, and often consumers are not even aware of it. For example, tracking a bank customer's regular expenses can automatically identify suspicious payments and instantly delay their execution until the user confirms the purchase. Thus, Data mining is used to protect consumers from various types of scammers.

• Sentiment analysis in sociology.

  Sentiment analysis from social media data is also a common application of data mining, using a technique called text mining. It can be used to gain insight into how a certain group of people feel about a certain topic. This is done using automatic analysis of data from social networks or other public sources.

• Bioinformation in healthcare.

  In medicine, Data mining models are used to predict the likelihood of a patient developing various ailments based on risk factors. To do this, demographic, familial and genetic data are collected and analyzed. In developing countries with large populations, such models have recently begun to be implemented to diagnose patients and prioritize medical care before doctors arrive and face-to-face examination.

“If you study the data carefully enough, you can find messages from God in it.” Scott Adams, writer, humorist

Data mining and Java

As you must have already understood from the context, in the field of data mining, as elsewhere in Big data , Java is one of the main programming languages. Therefore, we will make a short overview of the main tools for data mining in Java.

• RapidMiner

  RapidMiner is an open data mining platform written in Java. One of the best predictive analytics solutions available, with the ability to create integrated environments for deep learning, text mining, and machine learning. Many organizations use it for in-depth data analysis. RapidMiner can be used both on local servers and in the cloud.

• Apache Mahout

  Apache Mahout is an open source Java machine learning library from Apache. Mahout is precisely a scalable machine learning tool with the ability to process data on one or more machines. Implementations of this machine learning are written in Java, some parts are built on Apache Hadoop.

• MicroStrategy

  MicroStrategy is a business intelligence and data analytics software platform that supports all data mining models. Thanks to a wide range of proprietary gateways and drivers, the platform can connect to any corporate resource and analyze its data. MicroStrategy excels at transforming complex data into simplified visualizations that can be used for a variety of purposes.

• Java Data Mining Package

  Java Data Mining Package is an open source Java library for data mining and machine learning. It facilitates access to data sources and machine learning algorithms and provides visualization modules. JDMP includes a number of algorithms and tools, as well as interfaces to other machine learning and data mining packages (such as LibLinear, Elasticsearch, LibSVM, Mallet, Lucene, Octave and others).

• WEKA Machine Learning Suite

  The Waikato Environment for Knowledge Analysis (WEKA) Machine Learning Suite is an open list of algorithms that are used to develop machine learning methods. All WEKA algorithms are tailored for machine learning and data mining. The WEKA Machine Learning Suite is now widely used in the business environment, providing companies with simplified data analysis and predictive analytics.

“Today’s world is full of data, and thanks to this, we can see consumers much more clearly.” Max Levchin, co-founder of PayPal

How data is mined

The generally accepted data mining process consists of six steps.

• Defining business goals.

  First, you need to formulate the overall business goals of the project and understand how data mining will help achieve them. At this stage, a plan should be developed that includes timelines, actions and role assignments.

• Understanding the data.

  At the second stage, the necessary data is collected from various sources. Visualization tools are often used to examine the properties of data to ensure it helps achieve business goals. At this and the next stage, Java tools are most often used and, accordingly, the qualifications of a Java programmer are required.

Data preparation.

The data is then cleaned and augmented to ensure the array is ready for mining. Depending on the volume of data being analyzed and the number of data sources, processing can take a huge amount of time. Therefore, modern database management systems (DBMS) are used for processing, which speeds up the process of in-depth analysis.

• Data modeling.

  At this stage, special tools and mathematical models are applied to the data, which make it possible to find patterns in them.

• Grade.

  The results are then evaluated and compared to business goals to determine whether the data can achieve them.

• Deployment.

  Well, at the final stage, the data obtained as a result of the steps described above is integrated into business operations. Various business intelligence platforms are often used as a tool for implementing the obtained information.

“Data mining is a skill that is needed almost everywhere. Learn it and you will be universally in demand.” John Elder, founder of the analytics company Elder Research

Salaries of Data mining specialists

As you must have already understood from all of the above, data mining is very, very in demand in the market, and therefore the demand for specialists in this field remains consistently high. Therefore, finally, let’s look at how much Data mining specialists earn. In the US, average data mining salaries range from about $44,000 per year for data analysts to about $141,000 per year for machine learning specialists, according to recruiting site Indeed . The PayScale resource reports that the average salary of a data mining specialist in the United States is $60 thousand per year. In Russia, according to this data , Data mining experts earn from 50 thousand rubles to 180 thousand rubles per month. For Ukraine and Belarus, we were unable to find current information on salaries in this area, but after studying a number of open vacancies, we can conclude that the figures are not very different from Russia and range, on average, from $1 thousand to 2-3 thousand per year. month.