Digitalization is increasing rapidly which generates very high data volumes. Also the permanent interaction between companies and systems with the Internet of data and services and the Internet of things accelerates the amount of available data.
The complexity of product and process designs raises permanently and as a final result we have enormous data pools. These data contain very much knowledge, worthfull for business purposes and opportunities and therefore we talk about the „Gold of 21st century“. In order to analyze big data it’s not any more possible to apply classic statistical tools..
Another toolset will be needed and this is Data Mining. Data Mining means „Digging in data“.
In former days people have been digging for gold but now data have the same intrinsic value. There are many different options to analyse big data, it’s a toolbox with high variety and not easy. Data Mining finds patterns in data and delivers knowledge about complex correlations or dependencies. It also distinguishes decision paths among a big number of variables. This all is unknown knowledge and it might help us for our overall task if we ask the right questions.
This Training will present to you several real examples for such learnings and their benefit. The course provides Data Mining in a comprehensible manner but gives also an introduction into the methods of analysis in a higher degree of detail. This is helpful because it will not be enough just to look to some results which are printed out by a software package.
- You know the meaning of Data Mining and which tools and methods belong to it
- You will have a detailed knowledge about several tools for analysis
- You know the CRISP model and can execute it in your company
- You are aware of the interfaces between Data Mining and other initiatives, especially Six Sigma, Design for Six Sigma and Process Mining
- You are ready to enable Data Mining in your department in the company and you know how to integrate with existing processes and data based systems in order to generate a benefit for the future.
- You understand the Data Mining process and all of the relevant requirements and frame conditions
Project leaders and Management members from all departments and business segments of production and service companies, who are involved in strategies and methods like Industry 4.0, Data Mining, Lean Six Sigma and Design for Six Sigma and Data Scientists, of course.
- Introduction into Data Mining and Big Data Analytics
- The meaning of Data Mining
- Data Mining process, CRISP-Model
- Framework, Diagnostics, Predictive und Prescriptive
- The 5 V’s of Big Data: Volume, Velocity, Variety, Veracity, Value
- Knowledge Discovery in Data Bases – KDD – Process
- Machine Learning
- Practical examples for application and real experiences from different companies
- Overview about DM analysis Tools
- Cluster analysis
- Discriminant analysis
- Principal component analysis
- Text Mining
- „Nearest neighbour“ analysis
- Association rules
- Time series analysis
- Decision trees
- Neural networking
- Bay‘s Theorems
- Support Vector machines
- Regression diagnostics
- Comparison between the classis statistical analytics and Data Mining
- Data Mining – Roadmap
- Presentation of several selected tools with the Software Rapid Miner
- Workshop for a Case Study
Dr. Dietmar Stemann
Managing Partner, Master Black Belt
Dr. Dietmar Stemann is the Managing Director of MTS ConsultingPartner and he has many years of experience in product and process improvement with Lean Six Sigma and Design for Six Sigma (DFSS). Its strength lies in the consistent introduction and implementation management for Six Sigma and Lean. He has in-depth experience in the international upper middle class and international corporations.Read more…
Managing Partner, Master Black Belt
Dipl. Eng. Gebhard Mayer has very intensive and longterm experiences in the Management of R&D, production, Quality and also Engineering. His spectrum of competencies contains many items from different industrial branches.
His main strenghts are a systematic way of working, paired with data ananalytics as well as root cause thinking and a visualization which is very important and helpful in complex projects.
M.Sc. in Mathematical Economics
Jacqueline Schmitt, M.Sc. in Mathematical Economics, specializes in the use of data-driven methods in the context of process improvement and quality assurance in the automotive, electrical and process industries. Her strengths lie in artificial intelligence and data mining.Read more…
Scope of services:
- Detailed training documentation (in paper and as .pdf.-files)
- Coaching of your project by an experienced Master Black Belt with data science expertise (limited)
- Interactive workshops
- Certificate “Data Mining”
- Free space for exchange and knowledge transfer