The courses are run by KNIME experts that we know and trust, on a regular basis.
Such courses are supported by KNIME through
- frequent guest lectures by KNIME employees on a variety of topics
- lecture materials and practical exercises
- curriculum support to integrate KNIME Software
Introduction to Machine Learning with KNIME (LinkedIn Learning)
Instructor: Keith McCormick
In this course, expert Keith McCormick shows how KNIME supports all the phases of the Cross Industry Standard Process for Data Mining (CRISP-DM) in one platform. Get up and running quickly—in 15 minutes or less—or stick around for the more in-depth training covering merging and aggregation, modeling, and data scoring. Plus, learn how to increase the power of KNIME with extensions and integrate R and Python.
Code Free Data Science (Coursera)
Instructor: Nathasha Balac
The Code Free Data Science class is designed for learners seeking to gain or expand their knowledge in the area of Data Science. Participants will receive the basic training in effective predictive analytic approaches accompanying the growing discipline of Data Science without any programming requirements. Machine Learning methods will be presented by utilizing the KNIME Analytics Platform to discover patterns and relationships in data. Predicting future trends and behaviors allows for proactive, data-driven decisions.
Data analyzing and machine learning Hands-on with KNIME (Udemy)
Instructor: Barbora Stetinova
The goal of this course is to gain knowledge how to use open source Knime Analytics Platform for data analysis and machine learning predictive models on real data sets. We will create machine learning models within the standard machine learning process way, which consists from:
- acquiring data by reading nodes into the KNIME software (the data frames are available in this course for download)
- pre-processing and transforming data to get well prepared data frame for the prediction
- visualizing data with KNIME visual nodes (we will create basic plots and charts to have clear picture about our data)
- creating machine learning predictive models and evaluating them
Data Science Algorithms and Tools (University of Reading)
Lecturer: Dr. Giuseppe Di Fatta
Automated data collection and mature database technology lead to tremendous amounts of data stored in databases, data warehouses and other information repositories. In this context, automated data analysis and data modelling tools and algorithms (Data Mining) are becoming essential components to any information system. Application areas of these techniques include scientific computing, intelligent business, direct marketing, customer relationship management, market segmentation, store shelf management, data warehouse management, fraud detection in e-commerce and in credit card transactions, etc.
The study of fundamental techniques and tools for data manipulation and transformation, and for data mining algorithms classification, regression, clustering, association rule mining. In particular, one of the leading platforms for Data Science and Machine Learning, KNIME, will be introduced and adopted for practical activities. We will also collaborate with KNIME for embedding Level-1 and Level-2 industrial certification.
Text Mining & Analytics for Marketing and Business Practice (University of Massachusetts Amherst)
Lecturer: Francisco Villarroel Ordenes
In this course, students will learn how to use this type of data for marketing research and how to develop analytics solutions in the areas of customer experience, sentiment analysis, and marketing communications. In particular, we will focus on developing innovative text analytic solutions for marketing problems, implement them through a text-mining model, and evaluate them by assessing their accuracy.
The specific objectives of this course are:
- Understanding how textual data can be used for market research and gaining consumer insight
- Introducing the text mining process, its main stages, and components
- Providing hands-on training on using text analytics tools and software
- Discuss state-of-art research in marketing analytics areas
Predictive Analytics for Data Driven Decision Making (Bocconi University)
Lecturer: Luca Molteni and Daniele Tonini
The course provides an overview of the integration and analysis process of structured and unstructured data (Big Data), focusing on the most important applications of predictive analytics in managerial issues. The contents of the course covers both technical aspects of data analytics and more interpretation related topics (how to translate the analytical outputs into meaningful business insights).
- Data management architectures: a brief overview.
- Data understanding and data preparation.
- Models and statistical techniques applied to Big Data.
- Models' performance evaluation.
- Applications and real cases using open-source software (KNIME and R) in the following areas: internet of things, social & web content analysis, customer relationship management, fraud detection and operations.
Predictive Analytics Technologies (Oklahoma State University)
Lecturer: Dursun Delen
The main objective of this course is for the student to develop an in-depth understanding of the role of business analytics and computer based information systems in direct support of managerial decision making (nowadays commonly referred to as business analytics, business intelligence, and data science). Specifically, at the end of this course
students should develop knowledge and hands-on skills about:
- business intelligence, business analytics (descriptive, predictive and prescriptive), data science, Big Data, and decision support systems
- real-world data and data preprocessing
- descriptive statistics, data warehousing, and visual analytics
- data, text and Web mining methodologies and enabling technologies
- Big Data tools and technologies
Data Mining (University of Sheffield)
Lecturer: Val Gillet
As the volume of and types of information collected and stored in databases grows, there is a growing need to gain new insights into the data by identifying important patterns and trends. Data Mining is the process by which this is done. This module will examine the two main goals served by data mining:
- insight (identifying patterns and trends on which to base actions), and
- prediction (modelling future activities or outcomes based on input data) and how algorithms are used to support these.
An overview will be provided of the algorithms that underpin the most commonly used machine learning methods for building models and identifying patterns in data.
Introduction to Business Analytics (Weber State University)
Lecturer: Lixuan Zhang and Seokwoo Song
Business analytics has become a critical capability for organizations of all types and all sizes. It is applied in many business functions including operations, marketing, finance and human resources. Students with skills in the field of analytics are highly sought after in the job market. This course covers basic analytic methods used by organizations. Students will learn how to explore, manipulate and present data. They will also learn how to use the data to develop insights and predictive capabilities by using predictive analytics techniques, as well as the basic principles and techniques of data mining.
Upon successful completion of this course, students should be able to:
- Learn the principles of business analytics and its underlying methods
- Learn knowledge of algorithms and computational paradigms to find patterns in large datasets
- Develop the skills necessary to use related software tools to perform data collection, cleansing, and analytics
- Use and apply predictive analytics techniques
- Develop the ability to effectively use data mining and predictive analytics to derive meaningful business insights from large datasets
Business Analytics I & II (ESB Business School - Reutlingen University)
Lecturer: Tobias Schütz and Clemens van Dinther
As management education is becoming more and more analytical, the mandatory courses Business Analytics I (5. Sem.) and Business Analytics II (8. Sem.) have become integral cornerstones of ESB Reutlingen’s Double Degree programs. Both courses feature lectures in MIS (Management Information Systems) and Data Analytics.
After the successful completion of the module, the students should have developed an understanding of:
- Concepts, methods, and tools for information processing/transformation and data analysis
- Information retrieval, information storage, information transformation, and information pricing
- Data Mining and use of Mining Software (KNIME)
- Network technique & critical path analysis
Statistics and Analytics for Engineers (Singapore Polytechnic)
Lecturers: Dr. Tang U Liang and Chia Tien Chern
This module aims to provide engineering students with an introduction to statistics and data analytics (DA). DA is a competency that cuts across the Skills Framework of many sectors related to engineering. With today’s technology, engineers can harness the power of statistics and DA to analyse data and generate insights to support decision-making. The topics in statistics include descriptive statistics, probability, random variables and probability distributions, sampling distributions, and estimation. The topics in data analytics include a brief overview of data mining, cluster analysis, decision tree classifier, and linear regression. Software tools (such as Minitab Express and KNIME) are used throughout for hands-on exercises.
Teaching Methods/Learning Tasks:
Flipped-classroom is adopted as the teaching method. Weekly, students will complete 1 hour of online lecture out of class, mostly via short videos. This is followed by 3 hours of tutorial in class, where students will solve tutorial problems and participate in activities with their peers, under tutor’s guidance and facilitation. As far as possible students will practice using a software tool for each procedure/technique learned. Students will also learn to interpret results and graphs through examples and exercises in the module.
Machine Learning: From theory to deployment (University of Applied Sciences and Arts Western Switzerland)
Lecturers: Dominique Genoud
At the University of Applied Sciences and Arts Western Switzerland, Bachelor students are using KNIME during their last year.
This class is for beginners in Machine Learning. It is divided into two parts: Theory and Practical Project.
In the first part, It goes through all the basics in order to build a strong knowledge. In addition, basics like CRISP-DM, available frameworks, and ML in Python are studied.
The second part is the application of the knowledge gained in the theory. The format is a group practical project and goes from the analysis to the deployment.