Introduction To Data Mining Book

[Coleman John]

Datamining is the process of extracting useful information from large sets of data. It involves using various techniques from statistics, machine learning, and database systems to identify patterns, relationships, and trends in the data. This information can then be used to make data-driven decisions, solve business problems, and uncover hidden insights. Applications of data mining include customer profiling and segmentation, market basket analysis, anomaly detection, and predictive modeling. Data mining tools and technologies are widely used in various industries, including finance, healthcare, retail, and telecommunications.

Basically, Data mining has been integrated with many other techniques from other domains such as statistics, machine learning, pattern recognition, database and data warehouse systems, information retrieval, visualization, etc. to gather more information about the data and to helps predict hidden patterns, future trends, and behaviors and allows businesses to make decisions.

Data Mining can be applied to any type of data e.g. Data Warehouses, Transactional Databases, Relational Databases, Multimedia Databases, Spatial Databases, Time-series Databases, World Wide Web.

Market Basket Analysis: It is a technique that gives the careful study of purchases done by a customer in a supermarket. The concept is basically applied to identify the items that are bought together by a customer. Say, if a person buys bread, what are the chances that he/she will also purchase butter? This analysis helps in promoting offers and deals by the companies. The same is done with the help of data mining.

Fraud Detection: Nowadays, in this land of cell phones, we can use data mining to analyze cell phone activities for comparing suspicious phone activity. This can help us to detect calls made on cloned phones. Similarly, with credit cards, comparing purchases with historical purchases can detect activity with stolen cards.

Different data mining processing models will have different steps, though the general process is usually pretty similar. For example, the Knowledge Discovery Databases model has nine steps, the CRISP-DM model has six steps, and the SEMMA process model has five steps.

Even large companies or government agencies have challenges with data mining. Consider the FDA's white paper on data mining that outlines the challenges of bad information, duplicate data, underreporting, or overreporting.

There are two main types of data mining: predictive data mining and descriptive data mining. Predictive data mining extracts data that may be helpful in determining an outcome. Description data mining informs users of a given outcome.

Data mining relies on big data and advanced computing processes including machine learning and other forms of artificial intelligence (AI). The goal is to find patterns that can lead to inferences or predictions from large and unstructured data sets.

Data mining applications have been designed to take on just about any endeavor that relies on big data. Companies in the financial sector look for patterns in the markets. Governments try to identify potential security threats. Corporations, especially online and social media companies, use data mining to create profitable advertising and marketing campaigns that target specific sets of users.

The more data we produce, the more difficult it becomes to make sense of all that data and derive meaningful insights from it. Think of standing among trillions of trees; where do you start analyzing the forest?

Data mining provides a solution to this issue, one that shapes the ways businesses make decisions, reduce costs, and grow revenue. As a result, a variety of data science roles leverage mining as part of their daily responsibilities.

Data mining is most commonly defined as the process of using computers and automation to search large sets of data for patterns and trends, turning those findings into business insights and predictions. Data mining goes beyond the search process, as it uses data to evaluate future probabilities and develop actionable analyses.

Data mining and machine learning are unique processes that are often considered synonymous. However, while they are both useful for detecting patterns in large data sets, they operate very differently.

Data mining is most useful in identifying data patterns and deriving useful business insights from those patterns. To accomplish these tasks, data miners use a variety of techniques to generate different results. Here are five common data mining techniques.

With this technique, data points are assigned to groups, or classes, based on a specific question or problem to address. For instance, if a consumer packaged goods company wants to optimize its coupon discount strategy for a specific product, it might review inventory levels, sales data, coupon redemption rates, and consumer behavioral data in order to make the best decision possible.

Clustering looks for similarities within a data set, separating data points that share common traits into subsets. This is similar to the classification type of analysis in that it groups data points, but, in clustering analysis, the data is not assigned to previously defined groups. Clustering is useful for defining traits within a data set, such as the segmentation of customers based on purchase behavior, need state, life stage, or likely preferences in marketing communication.

Through regression analysis, specific inventory levels of milk and bread (in units/cases) can be recommended for specific levels of snow forecasted (inches), at specific points in time (days before the storm). In this way, the use of regression analysis maximizes sales, minimizes out-of-stock instances, and helps avoid overstocking which results in product spoilage after the storm.

Businesses use data mining to give themselves a competitive advantage by harnessing the data they collect on their customers, products, sales, and advertising and marketing campaigns. Data mining helps them sharpen operations, improve relationships with current customers, and acquire new customers.

Sales forecasting is a form of predictive analysis to which businesses are devoting more of their budgets. Data mining can help businesses project sales and set targets by examining historical data such as sales records, financial indicators (e.g., consumer price index, S&P 500, inflation markers), consumer spending habits, sales attributed to a specific time of year, and trends which may impact standard assumptions about the business. According to a recent MicroStrategy survey, 52 percent of global businesses consider predictive data their most important form of analytics.

Businesses build large databases of consumer data that they use to shape and focus their marketing efforts. These businesses need ways to manage and harness this data to develop targeted, personalized marketing communications. Data mining helps businesses understand consumer behaviors, track contact information and leads, and engage more customers in their marketing databases.

Data mining can provide businesses with up-to-date information regarding product inventory, delivery schedules, and production requirements. Data mining also can help remove some of the uncertainty that comes with simple supply-and-demand issues within the supply chain. The speed with which data mining can discern patterns and devise projections helps companies better manage their product stock and operate more efficiently.

Employment opportunities are growing for those skilled in data mining. Jobs in computer and information technology are projected to increase by 11 percent through 2029, according to the U.S. Bureau of Labor Statistics. Careers that focus on big data, database administration, and information security all employ data mining methods.

Computer and information scientists design new technology (computer languages, operating systems, software, etc.) in a rapidly expanding space and are always searching for new ideas. They work in fields like finance, technology, healthcare, and scientific exploration. Job opportunities are abundant (15 percent projected growth by 2029, per the BLS), and the median annual salary is $126,830.

Research analysts conduct marketing studies to help companies target new customers, increase sales, and determine the sales potential of new products. The growth of ecommerce is fueling growth in this field; CareerOneStop projects an 18 percent increase in job opportunities by 2029. The median U.S. salary is $65,810, with salaries in the New York/New Jersey region reaching $81,270.

Digital security experts have become indispensable to almost any organization needing to protect sensitive data and prevent cyberattacks. In fact, with 31 percent projected employment growth, even more jobs in this field will likely become available in the future. The field is also reasonably accessible for those entering from other industry concentrations. For example, database administrators can be strong candidates for roles in database security. Information security carries a median salary of $103,590.

A data science bootcamp can provide an introduction to data mining and a path to a new career. Bootcamps specialize in delivering concentrated learning opportunities in coding, data science, and cybersecurity, among other disciplines. In a 24-week data science program, students learn fundamental statistics, multiple programming languages, and big data analytics.

For professionals looking to expand their roles and transition to a technology career, a data science bootcamp can be a great entry point. According to a HackerRank 2020 survey, more than 70 percent of hiring managers said bootcamp graduates were as qualified as (or more than) other hires.

Programs like Rutgers Data Science Bootcamp offer a curriculum entailing a variety of crucial industry skills. These skills are learned through practical instruction simulating real-world experience. To begin your journey as a data miner, consider applying to Rutgers Data Science Bootcamp.

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