Data mining & predictive modeling
During this course you will learn to use the techniques required to perform analyzes of large data sets to find (statistical) connections.
Fundamental concepts for understanding and successfully applying data mining methods
It is becoming increasingly easy and common to collect and store large amounts of data. This applies for example to consumer data, data on individual behavior, warranty and fault data and production processes where sensors log data on a large scale.
With the help of data mining it is possible to discover relationships and structures in such large amounts of data and to develop prediction models. Techniques from applied statistics, artificial intelligence and machine learning are used.
In this course you will learn fundamental concepts for understanding and applying data mining methods. During the course participants gain experience in the visual programming of data workflows, with which model-fit, -validation and -optimization can be easily executed in practice.
Successful application of data mining methods and business analytics
At the end of this module you will:
- You have an overview of commonly used methods for predictive modeling from the areas of statistics, artificial intelligence and machine learning.
- You can develop these models for standard situations independently, using software such as IBM Modeler, SAS-Enterprise Guide and Enterprise Miner or Orange to visually program data workflows.
- You have gained practical experience with validating, interpreting and comparing alternative models and their use for decision support.
Intended for
Academics and higher professionals who are dealing in their work with data mining issues and the analysis of large data files. The course is also suitable for lecturers from universities and colleges of higher education who want to become acquainted with current methods in the field of data mining.
Background in a specific discipline is not required. Knowledge of basic statistical techniques such as testing, estimation and regression modelling is desirable.
Curious about the background of course leader Koo Rijpkema and his vision on the importance of data in the world of technology? Read the interview with Koo Rijpkema! Or do you want to know more about the user experience during an incompany edition of this course? Then read the interview with Hendrik-Jan de Kort (SPIE)..
In English on request
Do you want to follow the course in English? Please mention this in the remarks field when you register.
Course leader
dr. Koo Rijpkema
“For me, teaching means sharing knowledge and passion, inspiring and fascinating people through the application of statistics.”
This course is rated with an average of
Program manager
Why PAOTM
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The latest post-academic knowledge and skills
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Focused on questions that arise in a technical environment
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Interactive and directly applicable in practice
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Top teachers from science, research and business
Frequently asked questions
Program
Bij een incompany cursus wordt het programma in samenspraak vastgesteld. Hieronder zie je een basisprogramma.
- Reception and registration
- Introduction to course, software, examples and case studies
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Principles in predictive analytics
Data pre-processing, data exploratie & data reductie
Overfitting en modeltuning - Resampling methods and regularization
- Completion of day 1
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Basics of forecasting methods
Overview of commonly used prediction methods, such as linear regression, neural networks, decision trees, random forests, support vector machines and nearest neighbor algorithms
- Selection, validation and use of prediction models in practice
- Completion of day 2
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Essential classification methods
Overview of commonly used classification methods, such as logistic regression, neural networks, decision trees, random forests, support vector machines and nearest neighbor algorithms
- Selection, validation and use of classification models in practice
- Completion of day 3
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Essentials of cluster and segmentation models
- Overzicht van veelgebruikte cluster- en segmentatiemethodes, zoals hiërarchische, k-Means en Louvain clustering
- Selectie, validatie en gebruik van cluster- en segmentatiemodellen in de praktijk - Epilogue: overview, trends and next steps
- Evaluation and closing
Course leader
dr. Koo Rijpkema
“For me, teaching means sharing knowledge and passion, inspiring and fascinating people through the application of statistics.”
This course is rated with an average of
Frequently asked questions
Dates and locations
Deze cursus wordt enkel incompany georganiseerd, er zijn dus geen data ingepland. Neem contact op met de programmamanager voor de mogelijkheden voor jouw organisatie.
Program manager
Investment
Een incompany cursus wordt in samenspraak vastgesteld. Neem contact op met de programmamanager voor een offerte.
Frequently asked questions
Program manager
In-company
Are several employees interested in the same course, do you want to enrich knowledge with the entire team or focus on your own practice? Then an in-company course could be interesting. We are happy to think along with you about the possibilities. PAOTM has extensive experience in organizing in-company courses in many technical fields for a wide range of companies. You can choose to have an existing course organized in-company for multiple employees. However, if you have a specific organizational or departmental issue, we can also design a unique course. For every customized request, we search our network at universities, knowledge institutes and the business community for the right teachers who can provide your team with the desired knowledge. We then put together a course based on your training needs, learning needs and organizational goals.
Curious about the possibilities? Contact one of our program managers or complete the form below. We are happy to make you a suitable offer.
FAQ's
In-company request
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Program manager
In-company courses of PAOTM are rated with an average of
Why In-company
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A course tailored to your specific needs
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Get started right away with your own cases
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Led by top teachers with the most up-to-date knowledge
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You choose where and when: always efficient
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The entire team trained simultaneously
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Customization possible in all our fields
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