Data Mining

Data Mining

Challenges of large datasets

Industry 4.0, Internet of Things, and System of Systems are three buzzwords that are currently hotly debated. What these three terms entail is an ever-increasing interconnectedness of system elements and systems and thus also immense amounts of data are generated. The analysis of these data mountains poses new challenges across all sectors and divisions and requires special statistical methods. Companies which use this existing information can generate competitive advantages and ensure constant growth in the future as well.


Definition of Big Data Data Mining and Predictive Analytics

The term big data refers to data volumes that are too large to be evaluated using manual and classical methods of data processing. Big data is often the umbrella term for digital technologies that are technically blamed for the new era of digital communication and processing and socially responsible for social change.

By data mining analytics we understand the extraction of knowledge from large amounts of data that is unknown but potentially useful. The aim is to recognize cross-connections, patterns and trends with the systematic application of statistical methods.

Predictive analytics refers to calculated mathematical models based on collected data, which allow systems to make forecasts. In predictive analytics, mathematical models are trained on a dataset and then validated against an unknown dataset. The aim is that this algorithm achieves the best possible adaptation to the task to be performed, in order to be able to predict events. Predictive analytics is mainly used in machine learning. The best-known predictive techniques are neural networks and ensemble models. 
 

Data Mining Analytics as a discipline of  Business Intelligence
The term Business Intelligence, abbreviation BI, became popular from the beginning to the mid-1990s and refers to procedures and processes for the systematic analysis (collection, evaluation and presentation) of data in electronic form. The goal is to gain insights that enable better operational or strategic decisions in terms of corporate goals.
This is done with the help of analytical concepts and appropriate software. Now, a company needs systems for collecting and managing data such as Apache Hadoop. In addition, software for analyzing this data is necessary. The best known software tools for data mining analytics are JMP, Rapid Miner and R.

[Translate to English:] CRISP-DM Process Model

The CRISP-DM Process Model as a standardized data mining approach

An analytical concept for the CRISP-DM stands for Cross-Industry Standard Process for Data Mining and is a process model that corresponds to the usual procedure of a data mining expert or data scientist. The first phase is about business understanding, that is to understand and describe the project goal from a business perspective. Here it is important that the customer does describe the requirements not the Data Mining Analyst. The purpose of the Data Understanding Phase is to understand the initial data collected and to assess its quality. The Data Preparation Phase prepares the final dataset, which is used during the Modeling Phase to determine the mathematical model with the best fit. Before handing over the results to the customer in the Deployment Phase, they must be checked for suitability in the application during the Evaluation Phase.

Training Data Mining Analyst

Our Data Mining Analyst training gives you an overview of the most common Big Data Analysis, Data Mining, and Predictive Analytics tools and methods. You learn the procedure using the CRISP-DM and statistical methods such as cluster analysis, PCA, CART and neural networks. With the help of practical case studies, the individual subject areas will be introduced to you in an exciting and interactive way. Discover and learn the tools for Data Mining and Big Data Analysis with us and become the expert for the analysis of large data stocks in only 6 days.