Corporate RapidMiner Training Course

1. Basics of RapidMiner

• Introduction to data science and analytics
• Overview of RapidMiner capabilities
• RapidMiner in the analytics lifecycle

2. RapidMiner installation

• System requirements
• Step-by-step installation guide
• Verifying the installation

3. What is RapidMiner Studio?

• RapidMiner Studio interface overview
• Key features and functionalities
• Examples of using RapidMiner Studio for data analysis

1. Connect to PostgreSQL and fetch table data into RapidMiner example set

• Setting up PostgreSQL for RapidMiner
• Connecting RapidMiner to PostgreSQL
• Fetching and importing data into RapidMiner

2. PostgreSQL with RapidMiner

• Advanced SQL queries in RapidMiner
• Handling PostgreSQL data types in RapidMiner
• Optimizing data fetch performance

1. User interface

• Navigating the RapidMiner Studio interface
• Customizing the workspace
• Utilizing the help and resources section

2. Operators and processes

• Understanding operators in RapidMiner
• Creating and configuring processes

3. Creating and managing RapidMiner repositories

• Repository types and management
• Importing and exporting projects
• Version control in RapidMiner

4. Loading data

• Data sources and formats supported by RapidMiner
• Importing data into RapidMiner
• Preparing data for analysis

5. Storing data, processes, and result sets

• Saving and organizing data within RapidMiner
• Managing process results and outputs
• Exporting data and reports from RapidMiner

6. Visualizing data & basic charting

• Data visualization tools in RapidMiner
• Creating charts and graphs
• Customizing visualizations for analysis

1. k-Nearest Neighbor

• Theory and application of k-NN
• Configuring k-NN in RapidMiner

2. Naïve Bayes

• Understanding Naïve Bayes classification
• Implementing Naïve Bayes in RapidMiner
• Practical applications and limitations

3. Decision trees & rules

• Basics of decision trees and rule-based models
• Building and pruning trees in RapidMiner
• Interpreting model results

4. Support Vector Machines (SVM)

• Introduction to SVMs
• Configuring SVM parameters in RapidMiner

5. Logistic and linear regression

• Regression analysis in RapidMiner
• Model fitting and evaluation
• Application scenarios for regression models

6. Neural networks

• Fundamentals of neural networks
• Designing neural network models in RapidMiner
• Tuning and optimizing neural networks

1. Multiple sources

• Integrating data from diverse sources
• Ensuring data consistency and quality
• Techniques for data consolidation

2. Joins & set theory

• Applying joins and set operations in data preparation
• Advanced joining techniques for complex data structures
• Set theory applications in data analysis

3. Understanding new attributes

• Generating and interpreting new data attributes
• Attribute selection and reduction strategies
• Enhancing model performance with attribute engineering

4. Advanced Data ETL

• Complex ETL processes in RapidMiner
• Automating data cleaning and transformation
• ETL optimization techniques

5. Aggregation & multi-level aggregation

• Data aggregation methods and their applications
• Configuring multi-level aggregation for in-depth analysis
• Aggregation for performance improvement and insight generation

6. Handling attribute roles

• Defining and managing attribute roles in RapidMiner
• Role-based data preparation and modeling
• Custom roles for specialized analysis

7. Pivot & De-pivot

• Pivoting data for structured analysis
• De-pivoting data for normalization

8. Calculated values

• Creating calculated attributes in RapidMiner
• Formula creation and application
• Enhancing data sets with calculated values

9. Regular expressions

• Utilizing regular expressions for data processing
• Pattern matching and data extraction techniques
• Regular expressions for data cleaning and transformation

10. Changing value types

• Converting data types for analysis compatibility
• Handling categorical and numerical data conversions

11. Feature generation and feature engineering

• Techniques for feature generation
• Feature engineering for model improvement

1. Integration with operations

• Embedding RapidMiner analytics into operational processes
• Real-time data analytics and decision-making

2. Data and text mining

• Combining structured and unstructured data analysis
• Text mining capabilities in RapidMiner
• Extracting insights from text data

3. Clustering

• Overview of clustering techniques
• Configuring clustering algorithms in RapidMiner

4. Predictive analytics

• Fundamentals of predictive analytics
• Building predictive models in RapidMiner
• Predictive analytics in business decision-making