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Data Mining is the process of extracting usable data from a large set of any raw data. Data mining is a key part of knowledge discovery that helps to analyze an enormous set of data.
Data Mining can also be defined as the process of sorting through large data sets in order to discover and identify patterns and establish a relationship through data analysis.
Data Mining has applications in various fields of our life, like science and research. Businesses can learn more about their customers to develop more effective marketing strategies, increase sales, decrease costs, make better decisions by using software to understand deep insights of data.
Data Mining depends heavily on effective data collection, warehousing, and computer processing. This helps businesses be closer to their objectives and make better decisions that are future proof.
Data Mining deals with exploring and analyzing large pieces of information to extract meaningful patterns, trends, and insights to make better decisions. This can be used in a variety of ways, such as marketing, credit risk management, fraud detection, scientific discovery, determine users' behavior and sentiment.
To achieve this, the process involves effective data collection and warehousing as well as a computational process. Data mining employs sophisticated mathematical algorithms to segment the data and evaluate the probability of future events. Data mining is also recognized as Knowledge Discovery in Data (KDD).
The data mining process breaks down into the simple steps below:
1. First, organizations collect data and load it into their data warehouses.
2. Next, they store and manage the data, either on in-house servers or the cloud. Business analysts, management teams and information technology professionals access the data and determine how they want to organize it.
3. Then, application software sorts the data based on the user's results, and finally, the end-user presents the data in an easy-to-share format, such as a graph or table.
Data Mining deals with varieties of data and below are some types of data, data mining is applied on:
Relational databases: This is base on the relational model of data. It is a set of structured data tables from which data can be accessed or reassembled in many various ways without having to reorganize the database tables.
Data warehouses: This is a system used for reporting and data analysis, and is regarded as a core element of business intelligence. It is a process of collecting and managing data from various sources to provide meaningful business insights.
Advanced DB and information repositories: This usually requires a level of aggregation of data that the lower-level databases simply cannot provide, thus necessitating the creation of a higher-level structure.
Object-oriented and object-relational databases: An object-oriented database is a database that subscribes to a model with information represented by objects. The main feature of object-oriented databases is allowing the definition of objects, which are different from normal database objects.
Transactional Databases: Transactional database is a collection of data organized by timestamps, dates, etc to represent transactions in databases. With Transactional Databases, you have the full ability to roll back or undo its operation when a transaction is not completed or committed.
Spatial databases: Spatial databases are used for data involving spatially referenced data (i.e. data related to phenomena that have a position, and possibly, a shape, an orientation, and a size). Spatial databases can be implemented using various technologies, the most common now being the relational technology. They can have various structures and architectures according to their intended purpose. They store data in the form of coordinates, topology, lines, polygons, etc.
Heterogeneous and legacy databases: Legacy database is a group of heterogeneous databases that combines different kinds of data systems, such as relational or objects oriented databases, hierarchical databases, network databases, spreadsheets, multimedia databases, or file systems.
Multimedia Database: This is the combination of interrelated multimedia data that includes text, graphics (sketches, drawings), images, animations, video, audio, etc and have vast amounts of multisource multimedia data. These are used to store multimedia data such as images, animation, audio, video, and text-based. This data is stored in the form of multiple file types like .txt(text), .jpg(images), .swf(videos), .mp3(audio) etc.
Text databases: Text databases consist of a huge collection of documents. They collect this information from several sources such as news articles, books, digital libraries, e-mail messages, web pages, etc. Due to an increase in the amount of information, the text databases are growing rapidly. In many of the text databases, the data is semi-structured.
Flat Files: Flat files are defined as data files in text form or binary form with a structure that can be easily extracted by data mining algorithms. Flat files are represented by the data dictionary. Eg: CSV file.
Web Data: This is a collection of documents and resources like audio, video, text, etc which are identified by Uniform Resource Locators (URLs) through web browsers, linked by HTML pages, and accessible via the Internet network. These are basically data found on the web with their unique URLs.
1. Data Mining helps to extract information from a data set to give a meaningful insight for better decision making.
2. Data Mining helps in association/correlation between product sales.
3. Data Mining helps in checking competitors and monitoring market directions.
4. Data Mining helps in checking resources and spending.
5. Data Mining helps in checking and identifying criminality.
6. Data Mining helps to identify the kind of products your customers prefer per time.
7. Data Mining offers accurate market analysis.
8. Data Mining helps in fraud detection.
9. Data Mining helps with customer retention.
10. Data Mining helps in science exploration and research.
11. Data Mining helps in gaining higher returns on investment.
12. Data Mining provides job opportunities.
1. Data Mining implies an analysis of data patterns in large batches of data using one or more software.
2. Data Mining has applications in multiple fields such as fields like science and research.
3. With Data Mining, businesses can get more information about their customers and develop more effective ways to improve business functions.
4. Data Mining is used in effective data collection and warehousing as well as computer processing for the arrangement and evaluation of the data.
5. Data Mining uses sophisticated mathematical algorithms.
6. Data Mining is also known as knowledge discovery.
7. Data Mining is a process used by companies to turn raw data into useful information.
8. With Data Mining, businesses can learn more about their customers to develop more effective marketing strategies, increase sales and decrease costs leading to higher profitability.
9. Data mining depends on effective data collection, warehousing, and computer processing.
10. Data mining can be used in a variety of ways, such as database marketing, credit risk management, fraud detection, Spam email filtering even to discern the sentiment or opinion of users.
The Data Mining process is broken down into these five steps:
1. Organizations engage in the collection of data and load it into their data warehouses.
2. Organizations store and manage the data, either in-house-server or on the cloud.
3. Business analysts, management teams and information technology professionals access the data and determine how they want to organize it for use.
4. Applications software sorts the data based on the user's results.
5. Lastly, the end-user represents the data in an easy to share formats such as graphs or tables.
In the Full Course, you will learn everything you need to know about Data Mining with Certification to showcase your knowledge/skill gained.
Data Mining - Introduction/Overview
Data Mining - Tasks
Data Mining - Issues
Data Mining - Evaluation
Data Mining - Terminologies
Data Mining - Knowledge Discovery
Data Mining - Systems
Data Mining - Query Language
Data Mining - Classification & Prediction
Data Mining - Decision Tree Induction
Data Mining - Bayesian Classification
Data Mining - Rules-Based Classification
Data Mining - Classification Methods
Data Mining - Cluster Analysis
Data Mining - Mining Text Data
Data Mining - Mining WWW
Data Mining - Applications & Trends
Data Mining - Themes
Data Mining - Exams and Certification
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