The goal of the 3-day training course in Data Science and Data Analytics with KNIME is to equip participants with the knowledge and skills needed to effectively use KNIME for data preparation, visualization, modeling, and reporting. By the end of the course, participants should be able to design and implement data workflows using KNIME, apply machine learning techniques to solve analytical problems, and automate and deploy workflows using KNIME Server. The course aims to provide a comprehensive understanding of the concepts, techniques, and tools used in Data Science and Data Analytics using KNIME.
Overview of Data Science, Data Analytics, and related fields
Chances and Risks of Data Science
Tools for interactive reporting
Communikation and reporting
Tools for data analysis
Extract, Transform, Load (ETL) with KNIME
Introduction to KNIME
Data import from simple formats
Data verification
Merging data
Data cleaning
Data formats
Work documentation
Workflow organization
Data visualization
Data export
Data Analytics with KNIME
KNIME Machine Learning
Introduction to machine learning
Supervised and unsupervised learning
Building and evaluating classification models
Building and evaluating regression models
Tuning model parameters
Advanced Analytics with KNIME
Text mining and natural language processing
Time series analysis
Advanced visualization with KNIME
Workflow documentation
Results communication and reporting with KNIME
Advanced Data Analytics with KNIME
Data Science - Overview
Introduction to Data Science and its origins
The stages of analysis according to Gartner
Basic concepts of statistics
Descriptive statistics and data properties
Machine Learning Techniques
Regression, overfitting, Tree methods, Bagging, and Boosting
Classification methods and techniques
Unsupervised learning techniques
Advanced KNIME
Data streaming
Time and date formats
Looping in KNIME
Data import from databases locally and remotely
Data export to local and remote databases
Basic math and logical operations
KNIME Workflow Automation and Deployment
Automating KNIME workflows
Batch processing
Email notifications
Workflow documentation
Workflow maintenance
Workflow version control
Deploying workflows to servers
Deploying workflows as web services
Integrating KNIME with other data tools and systems
Automating data workflows with KNIME Server
Integrating KNIME with other analytics tools (e.g. R, Python)
Deploying KNIME workflows to cloud platforms (e.g. AWS, Azure)
Zielgruppe:
The 3-day training course in Data Science and Data Analytics with KNIME is suitable for a range of professionals, including data analysts, data scientists, business analysts, researchers, and anyone who works with data. The course is designed for both beginners and experienced professionals who want to improve their skills in using KNIME for data preparation, visualization, modeling, and reporting. The course is particularly relevant for individuals who are involved in data-driven decision making, such as those working in business intelligence, marketing analytics, healthcare, and science. Overall, the course is designed to provide participants with the knowledge and skills needed to apply data analytics and machine learning techniques to real-world problems using KNIME.
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