Get complete basic knowledge on how to practically use methods for analyzing and modeling data for predictions and decision-making and learn how to use generative AI, such as ChatGPT or Copilot, in this process in a responsible way.
As a result of the large scale availability of data nowadays, the use of statistical methods has broadened considerably and the importance and meaning of data science has increased, not only in the laboratory and industry but also in marketing and business intelligence.
For such applications, this course offers essential insights into statistical concepts and skills needed to apply data analysis techniques responsibly. You will learn how to work statistically sound and interpret datasets and models correctly.
The course starts with a review of basic principles from the fields of statistics and probability theory. This provides a good starting point for the data analysis methods within the important fields of data mining (big data) and time series analysis, discussed thereafter. You will also gain experience in working effectively with the statistical software R.
Furthermore, possibilities and dangers of using generative AI, such as ChatGPT and Copilot, when applying the discussed techniques will be explicitly addressed and demonstrated.
The course consists of three topics:
Academics and higher professionals who want to make use of modern applied statistical techniques in their work and who want to familiarize themselves with the relevant skills, and want to get acquainted with the latest statistical freeware. The course is also suited for lecturers at universities or colleges of higher education who want to be informed on actual methods for data analysis and data science.
You have mathematics at at least secondary education level. Basic knowledge in the field of statistics is desirable.
Voorbeelden zijn in R.
Would you like to have more background information or read about experiences of data analysis course participants? Read our interviews:
– Interview with Hendrik-Jan de Kort (SPIE Nederland). He and his team followed an incompany data training to build on AI-knowledge.
– Interview with Tomaso della Vedova and Chantal Visser (Endress + Hauser), about their incompany course on data mining and predictive modeling.
– Interview with participant Mateen Asad (BearingPoint), who took a combination of the Practical data science with R and Time series analysis and forecasting course.
– Course leader dr. Koo Rijpkema (University of Technology Eindhoven) shares his vision on the world of data and courses and the importance of the discipline.
Do you want to follow the course in English? Please mention this in the remarks field when you register.
“For me, teaching means sharing knowledge and passion, inspiring and fascinating people through the application of statistics.”
Introduction and overview of the course and the software to be used.
Applied Probability and Statistics Revisited
Module 1:1
• Inleiding en overzicht van de cursus en de te gebruiken software.
• Exploratieve Data Analyse:
• Inleiding kansen, kansrekening en kansverdelingen.
• Oefeningen Exploratieve Data Analys en Kansrekening
Module 1.2
• Statistical Testing and Estimation in a nutshell.
• Selectie, validatie en gebruik van kansverdelingen in de praktijk.
• Oefeningen Kansverdelingen en Principes van toetsen/schatten
Data Mining in a Nutshell (1)
Module 2.1
• Voorspelmodellen op basis van regressie methoden.
• Selectie, validatie en gebruik van regressie modellen in de praktijk.
• Oefeningen regressie modellen.
Data Mining in a Nutshell (1)
Module 2.2
• Classificatiemodellen op basis van logistische regressie methoden.
• Selectie, validatie en gebruik van logistische regressie modellen in de praktijk.
• Oefeningen logistische regressie modellen.
Data Mining in a Nutshell (2)
Module 2.3
• Alternatieve methoden voor voorspellen en classificeren.
• Selectie, validatie en gebruik van voorspel en classifciatiemodellen in de praktijk.
Data Mining in a Nutshell (2)
Module 2.3
• Cluster analyse.
• Oefeningen voorspellen, classificeren en cluster-analyse.
Time Series Analysis in a Nutshell
Module 3.1
• Inleiding, karakterisering en exploratieve analyse van tijdreeksdata.
• Tijdreeksmodellen op basis van Exponential Smoothing.
• Selectie, validatie en gebruik van exponential smoothing modellen in de praktijk.
• Oefeningen exponential smoothing modellen.
Time Series Analysis in a Nutshell
Module 3.2
• Box-Jenkins modellen voor tijdreeksdata.
• Selectie, validatie en gebruik van Box-Jenkins modellen in de praktijk.
• Oefeningen Box-Jenkins modellen.
“For me, teaching means sharing knowledge and passion, inspiring and fascinating people through the application of statistics.”
Below you will find an overview of the available dates and locations. You can register immediately by clicking on the 'Register' button.
Heb je vooraf een offerte nodig voor jouw cursusaanvraag? Vraag deze hier eenvoudig aan.
Request a quotationAre 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.
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