Course Outline
Introduction
Installing and Configuring Dataiku Data Science Studio (DSS) for Government Use
- System requirements for Dataiku DSS deployment in government environments
- Setting up Apache Hadoop and Apache Spark integrations to support government data processing needs
- Configuring Dataiku DSS with web proxies to ensure secure and compliant access
- Migrating from other platforms to Dataiku DSS for government, ensuring seamless integration and minimal disruption
Overview of Dataiku DSS Features and Architecture
- Core objects and graphs foundational to Dataiku DSS, tailored for government use cases
- Understanding the concept of a recipe in Dataiku DSS for efficient data processing
- Types of datasets supported by Dataiku DSS, including those relevant to public sector workflows
Creating a Dataiku DSS Project for Government Applications
Defining Datasets to Connect to Data Resources in Dataiku DSS
- Working with DSS connectors and file formats, ensuring compatibility with government data sources
- Comparing standard DSS formats versus Hadoop-specific formats for government datasets
- Uploading files for a Dataiku DSS project, adhering to government data handling standards
Overview of the Server Filesystem in Dataiku DSS for Government Use
Creating and Using Managed Folders for Secure Data Management
- Utilizing a Dataiku DSS recipe for merging folders to streamline data aggregation
- Differentiating between local and non-local managed folders for government projects
Constructing a Filesystem Dataset Using Managed Folder Contents for Government Applications
- Performing cleanups with a DSS code recipe to ensure data integrity and compliance
Working with Metrics Datasets and Internal Stats Datasets in Dataiku DSS for Government Use
Implementing the DSS Download Recipe for HTTP Dataset for Government Projects
Relocating SQL Datasets and HDFS Datasets Using Dataiku DSS for Government Applications
Ordering Datasets in Dataiku DSS for Government Workflows
- Comparing writer ordering versus read-time ordering to optimize data processing
Exploring and Preparing Data Visuals for a Dataiku DSS Project for Government Use
Overview of Dataiku Schemas, Storage Types, and Meanings for Government Applications
Performing Data Cleansing, Normalization, and Enrichment Scripts in Dataiku DSS for Government Projects
Working with Dataiku DSS Charts Interface and Types of Visual Aggregations for Government Use
Utilizing the Interactive Statistics Feature of DSS for Government Analysis
- Conducting univariate analysis versus bivariate analysis to derive insights
- Making use of the Principal Component Analysis (PCA) DSS tool for government data exploration
Overview of Machine Learning with Dataiku DSS for Government Applications
- Differentiating between supervised ML and unsupervised ML for government projects
- References for DSS ML algorithms and feature handling to support government analytics
- Implementing deep learning with Dataiku DSS for advanced government use cases
Overview of the Flow Derived from DSS Datasets and Recipes for Government Projects
Transforming Existing Datasets in DSS with Visual Recipes for Government Use
Utilizing DSS Recipes Based on User-Defined Code for Custom Government Applications
Optimizing Code Exploration and Experimentation with DSS Code Notebooks for Government Projects
Writing Advanced DSS Visualizations and Custom Frontend Features with Webapps for Government Use
Working with Dataiku DSS Code Reports Feature for Government Reporting
Sharing Data Project Elements and Familiarizing with the DSS Dashboard for Collaborative Government Workflows
Designing and Packaging a Dataiku DSS Project as a Reusable Application for Government Use
Overview of Advanced Methods in Dataiku DSS for Government Applications
- Implementing optimized datasets partitioning using DSS to enhance performance in government projects
- Executing specific DSS processing parts through computations in Kubernetes containers for scalable government solutions
Overview of Collaboration and Version Control in Dataiku DSS for Government Projects
Implementing Automation Scenarios, Metrics, and Checks for DSS Project Testing in Government Environments
Deploying and Updating a Project with the DSS Automation Node and Bundles for Government Use
Working with Real-Time APIs in Dataiku DSS for Government Applications
- Utilizing additional APIs and Rest APIs in DSS to support government data integration needs
Analyzing and Forecasting Dataiku DSS Time Series for Government Use
Securing a Project in Dataiku DSS for Government Compliance
- Managing project permissions and dashboard authorizations to ensure secure access
- Implementing advanced security options to meet government standards
Integrating Dataiku DSS with the Cloud for Government Projects
Troubleshooting Common Issues in Dataiku DSS for Government Use
Summary and Conclusion of Dataiku DSS for Government Applications
Requirements
- Proficiency with Python, SQL, and R programming languages for government applications
- Fundamental understanding of data processing using Apache Hadoop and Spark in a public sector context
- Knowledge of machine learning principles and data modeling techniques
- Experience with statistical analysis and data science methodologies for government use
- Skills in visualizing and effectively communicating data insights
Audience
- Engineers
- Data Scientists
- Data Analysts
