Data Science and Data Analytics with KNIME

 Introduction to Data Analytics with KNIME

• Introduction and Context
  • 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)

LernzieleThe 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.ZielgruppenThe 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.