Detailed Dataiku Training Path:

Detailed Dataiku training path to master, moving from beginner to advanced levels:

Beginner Level

1. Introduction to Dataiku
   • Objective: Understand the basics of Dataiku and its environment.
   • Key Topics:
     • Overview of Dataiku Data Science Studio (DSS)
     • Navigating the interface
     • Basic concepts: projects, datasets, and recipes
     • Importing and exploring data
2. Data Preparation Basics
   • Objective: Learn how to clean and prepare data.
   • Key Topics:
     • Data cleaning and transformation
     • Handling missing data
     • Joining and merging datasets
     • Using visual recipes for data preparation
3. Basic Data Analysis
   • Objective: Perform initial data analysis and visualization.
   • Key Topics:
     • Descriptive statistics
     • Creating visualizations and dashboards
     • Exploring data with charts and graphs
     • Using Jupyter notebooks within Dataiku
4. Dataiku Core Designer Certification Preparation
   • Objective: Prepare for the Core Designer certification exam.
   • Key Topics:
     • Core concepts of data preparation and analysis
     • Best practices for project organization
     • Basic automation and workflows

Intermediate Level

5. Advanced Data Preparation
   • Objective: Deep dive into more complex data preparation techniques.
   • Key Topics:
     • Advanced recipes and transformations
     • Data blending and enrichment
     • Handling large datasets
     • Data validation and quality checks
6. Machine Learning Basics
   • Objective: Learn the fundamentals of building machine learning models in Dataiku.
   • Key Topics:
     • Introduction to supervised and unsupervised learning
     • Preparing data for machine learning
     • Building and evaluating models using the visual interface
     • Understanding model performance metrics
7. Automation and Scripting
   • Objective: Automate workflows and use scripting for custom tasks.
   • Key Topics:
     • Creating scenarios for automation
     • Using Python and R scripts within Dataiku
     • Customizing recipes with code
     • Scheduling tasks and monitoring workflows

Advanced Level

8. Advanced Machine Learning
   • Objective: Build and deploy advanced machine learning models.
   • Key Topics:
     • Feature engineering and selection
     • Hyperparameter tuning
     • Model interpretability and explainability
     • Deploying models to production
9. Dataiku Administration
   • Objective: Manage and administer Dataiku environments.
   • Key Topics:
     • User and project management
     • Setting up and configuring Dataiku instances
     • Monitoring and troubleshooting
     • Security and governance
10. Collaborative Data Science and MLOps
    • Objective: Enable collaboration and streamline operations.
    • Key Topics:
      • Collaborating with teams on projects
      • Version control and project documentation
      • Continuous integration and deployment (CI/CD)
      • Best practices for MLOps
11. Dataiku Advanced Designer Certification Preparation
    • Objective: Prepare for the Advanced Designer certification exam.
    • Key Topics:
      • Comprehensive understanding of Dataiku’s advanced features
      • Complex data preparation and analysis
      • Advanced machine learning and automation
      • Real-world case studies and projects

Resources and Practice

• Online Courses and Tutorials:
  • Dataiku Academy
  • Coursera and edX
  • Udemy
• Books and Documentation:
  • Dataiku DSS User Guide
  • Data Science and Machine Learning with Dataiku by Corey Weisinger
  • Dataiku official documentation and whitepapers
• Practice and Hands-on Labs:
  • Dataiku Community Edition for hands-on practice
  • Practice projects and case studies
  • Kaggle for datasets and competitions
• Communities and Forums:
  • Dataiku Community Forum
  • Stack Overflow
  • LinkedIn Groups and other professional networks

By following this guided path, you can progress from a beginner to an advanced Dataiku user, equipped with the skills needed to handle data preparation, analysis, and machine learning projects using Dataiku’s comprehensive tools and features