Teaching Materials Access the slides for each chapter of Guide to Intelligent Data Science to build your own curriculum. A sample curriculum is outlined below.

Disclaimer: Except otherwise noted, the teaching materials (including workflow examples, code examples, and slides) are available under the Creative Commons Attribution-NonCommercial 4.0 International Public License (CC BY-NC 4.0).

Chapter	Exercises	Slides
01. Introduction	–	PPTX PDF
02. Practical Data Analysis: An Example	–	–
03. Project Understanding	–	PPTX PDF
04. Data Understanding	–	Data Visualization (PPTX) Data Visualization (PDF) Dimensionality Reduction (PPTX) Dimensionality Reduction (PDF)
05. Principles of Modeling	Theoretical Confusion Matrix Numeric Scorer ROC Curve	PPTX PDF
06. Data Preparation	–	PPTX PDF
07. Finding Patterns	Clustering Theoretical Clustering	Clustering (PPTX) Clustering (PDF) Association Rules (PPTX) Association Rules (PDF)
08. Finding Explanations	Decision Trees Theoretical Decision Trees Regression Trees Theoretical Regression Trees Bayes Classifiers Theoretical	Decision Trees (PPTX) Decision Trees (PDF) Bayes Classifiers (PPTX) Bayes Classifiers (PDF) Regression (PPTX) Regression (PDF) Rule Learning (PPTX) Rule Learning (PDF)
09. Finding Predictors	Ensemble Methods Theoretical Ensemble Methods SVM Theoretical SVM Neural Networks Theoretical Neural Networks Deep Learning (LSTM) Deep Learning (CNN)	Nearest-Neighbor Predictors (PPTX) Nearest-Neighbor Predictors (PDF) Artificial Neural Networks (PPTX) Artificial Neural Networks (PDF) Deep Learning (PPTX) Deep Learning (PDF) Support Vector Machines (PPTX) Support Vector Machines (PDF) Ensemble Methods (PPTX) Ensemble Methods (PDF)
10. Evaluation & Deployment	–	PPTX PDF
A. Statistics	–	–
B. KNIME	Getting Comfortable	PPTX PDF

Download all KNIME Exercises Download all Theoretical Exercises Download all PDF Slides Download all PPTX Slides

Sample Curriculum:
Machine Learning and AI for Data ScienceUniversity of Konstanz, Winter 2020/2021

Week	Type	Topic	Notes	Material
1	Lesson	Introduction to Data Science		Slides
1	Exercise	KNIME Overview	Install KNIME Analytics Platform	Slides Download KNIME
2	Lesson	Principles of Modeling		Slides
2	Exercise	Principles of Modeling		Exercises
3	Lesson	Clustering		Slides
3	Exercise	Clustering		Theoretical Practical
4	Lesson	Regression / Logistic Regression		Slides
4	Exercise	Regression / Logistic Regression		Exercises
5	Lesson	Version Space		–
5	Exercise			–
6	Lesson	Kernel Methods and SVM		Slides
6	Exercise	SVM		Theoretical Practical
7	Lesson	Decision Trees		Slides
7	Exercise	Decision Trees		Theoretical Practical
8	Lesson	Classic Neural Networks		Slides
8	Exercise	Neural Networks		Theoretical Practical
9	Lesson	Deep Learning	incl. applications to images	Slides
9	Exercise	Deep Learning		LSTM CNN
10	Lesson	Ensembles / Naïve Bayes		Ensemble Methods Bayes Classifiers
10	Exercise	Ensembles / Naïve Bayes		Ensembles Theoretical Ensembles Practical Bayes Classifiers Theoretical
11	Lesson	Deployment, REST, Guided Analytics		Slides
11	Exercise	Exam for KNIME Certification Program	Contact academia@knime.com for more information	–
12	Lesson	Guest Lecture	Phil Winters of ciagenda.com Contact academia@knime.com for information	–
12	Exercise	Q&A for written exam		–

Teaching Materials Access the slides for each chapter of Guide to Intelligent Data Science to build your own curriculum. A sample curriculum is outlined below.

Sample Curriculum:Machine Learning and AI for Data ScienceUniversity of Konstanz, Winter 2020/2021

Sample Curriculum:
Machine Learning and AI for Data ScienceUniversity of Konstanz, Winter 2020/2021