Visit This Web URL https://masterytrail.com/product/accredited-expert-level-ibm-watson-data-refinery-advanced-video-course Lesson 1: Introduction to IBM Watson Data Refinery
1.1. Overview of IBM Watson Data Refinery
1.2. Key Features and Benefits
1.3. Use Cases and Industry Applications
1.4. Setting Up Your Environment
1.5. Navigating the Watson Data Refinery Interface
1.6. Understanding Data Sources
1.7. Importing Data into Data Refinery
1.8. Data Refinery vs. Traditional ETL Tools
1.9. Hands-On: First Data Import
1.10. Troubleshooting Common Setup Issues
Lesson 2: Data Ingestion and Preparation
2.1. Connecting to Various Data Sources
2.2. Data Formats Supported by Data Refinery
2.3. Data Profiling and Quality Assessment
2.4. Handling Missing Data
2.5. Data Cleaning Techniques
2.6. Data Transformation Operations
2.7. Advanced Data Wrangling
2.8. Automating Data Preparation Workflows
2.9. Case Study: Data Preparation for a Retail Dataset
2.10. Best Practices for Data Ingestion
Lesson 3: Data Transformation and Enrichment
3.1. Basic Data Transformation Operations
3.2. Advanced Data Transformation Techniques
3.3. Data Enrichment with External Sources
3.4. Using Data Refinery Functions and Expressions
3.5. Custom Transformation Scripts
3.6. Handling Complex Data Structures
3.7. Data Aggregation and Summarization
3.8. Time Series Data Transformation
3.9. Hands-On: Transforming a Financial Dataset
3.10. Optimizing Data Transformation Performance
Lesson 4: Data Quality and Governance
4.1. Understanding Data Quality Metrics
4.2. Implementing Data Quality Rules
4.3. Data Lineage and Traceability
4.4. Data Governance Best Practices
4.5. Compliance and Regulatory Considerations
4.6. Data Security in Data Refinery
4.7. Monitoring Data Quality Over Time
4.8. Automating Data Quality Checks
4.9. Case Study: Data Governance in Healthcare
4.10. Tools for Data Quality Management
Lesson 5: Advanced Data Profiling
5.1. Deep Dive into Data Profiling Techniques
5.2. Statistical Analysis of Data
5.3. Identifying Data Anomalies
5.4. Data Distribution Analysis
5.5. Correlation and Dependency Analysis
5.6. Data Profiling for Large Datasets
5.7. Visualizing Data Profiles
5.8. Automating Data Profiling Reports
5.9. Hands-On: Profiling a Customer Dataset
5.10. Interpreting Data Profiling Results
Lesson 6: Machine Learning Integration
6.1. Introduction to Machine Learning in Data Refinery
6.2. Preparing Data for Machine Learning Models
6.3. Integrating with Watson Machine Learning
6.4. Building and Training ML Models
6.5. Evaluating Model Performance
6.6. Deploying ML Models in Data Refinery
6.7. Automating ML Workflows
6.8. Case Study: Predictive Maintenance Model
6.9. Advanced ML Techniques in Data Refinery
6.10. Troubleshooting ML Integration Issues
Lesson 7: Data Visualization and Reporting
7.1. Introduction to Data Visualization
7.2. Creating Basic Visualizations in Data Refinery
7.3. Advanced Visualization Techniques
7.4. Integrating with Watson Analytics
7.5. Building Interactive Dashboards
7.6. Customizing Visualizations
7.7. Sharing and Collaborating on Visualizations
7.8. Automating Report Generation
7.9. Hands-On: Visualizing Sales Data
7.10. Best Practices for Data Visualization
Lesson 8: Data Refinery Automation
8.1. Understanding Data Refinery Automation
8.2. Creating Automated Data Pipelines
8.3. Scheduling Data Refinery Jobs
8.4. Triggering Automation Based on Events
8.5. Integrating with External Automation Tools
8.6. Monitoring Automated Workflows
8.7. Handling Errors in Automated Pipelines
8.8. Case Study: Automating ETL Processes
8.9. Advanced Automation Techniques
8.10. Best Practices for Data Refinery Automation
Lesson 9: Performance Tuning and Optimization
9.1. Understanding Performance Metrics in Data Refinery
9.2. Optimizing Data Ingestion Performance
9.3. Tuning Data Transformation Operations
9.4. Handling Large Datasets Efficiently
9.5. Parallel Processing in Data Refinery
9.6. Resource Management and Allocation
9.7. Monitoring Performance Bottlenecks
9.8. Scaling Data Refinery Operations
9.9. Hands-On: Performance Tuning for a Large Dataset
9.10. Best Practices for Performance Optimization
Lesson 10: Integration with IBM Watson Studio
10.1. Overview of IBM Watson Studio
10.2. Integrating Data Refinery with Watson Studio
10.3. Data Preparation in Watson Studio
10.4. Building ML Models in Watson Studio
10.5. Collaborating on Data Projects
10.6. Deploying Models from Watson Studio
10.7. Automating Workflows Between Data Refinery and Watson Studio
10.8. Case Study: End-to-End Data Science Project
10.9. Advanced Integration Techniques
10.10. Troubleshooting Integration Issues
Lesson 11: Data Refinery and Cloud Integration
11.1. Overview of Cloud Integration with Data Refinery
11.2. Connecting to Cloud Data Sources
11.3. Storing Data in Cloud Storage Solutions
11.4. Integrating with IBM Cloud Services
11.5. Hybrid Cloud Architectures
11.6. Security Considerations for Cloud Integration
11.7. Scaling Data Refinery in the Cloud
11.8. Case Study: Cloud-Based Data Pipeline
11.9. Advanced Cloud Integration Techniques
11.10. Best Practices for Cloud Integration
Lesson 12: Advanced Data Transformation Techniques
12.1. Deep Dive into Advanced Transformation Operations
12.2. Custom Scripting for Data Transformation
12.3. Handling Nested and Complex Data Structures
12.4. Data Normalization and Denormalization
12.5. Advanced Aggregation Techniques
12.6. Time Series Data Transformation
12.7. Geospatial Data Transformation
12.8. Hands-On: Advanced Transformation of a Complex Dataset
12.9. Optimizing Advanced Transformation Operations
12.10. Troubleshooting Advanced Transformation Issues
Lesson 13: Data Refinery and Big Data Technologies
13.1. Overview of Big Data Integration with Data Refinery
13.2. Connecting to Big Data Sources
13.3. Integrating with Apache Spark
13.4. Handling Large-Scale Data Processing
13.5. Data Refinery and Hadoop Ecosystem
13.6. Performance Tuning for Big Data Workloads
13.7. Case Study: Big Data Analytics Project
13.8. Advanced Big Data Integration Techniques
13.9. Best Practices for Big Data Integration
13.10. Troubleshooting Big Data Integration Issues
Lesson 14: Data Refinery and AI Integration
14.1. Overview of AI Integration with Data Refinery
14.2. Preparing Data for AI Models
14.3. Integrating with Watson AI Services
14.4. Building and Training AI Models
14.5. Evaluating AI Model Performance
14.6. Deploying AI Models in Data Refinery
14.7. Automating AI Workflows
14.8. Case Study: AI-Driven Customer Segmentation
14.9. Advanced AI Integration Techniques
14.10. Best Practices for AI Integration
Lesson 15: Data Refinery and IoT Integration
15.1. Overview of IoT Integration with Data Refinery
15.2. Connecting to IoT Data Sources
15.3. Handling Real-Time Data Streams
15.4. Data Refinery and Edge Computing
15.5. Processing IoT Data in Data Refinery
15.6. Building IoT Analytics Pipelines
15.7. Case Study: IoT-Based Predictive Maintenance
15.8. Advanced IoT Integration Techniques
15.9. Best Practices for IoT Integration
15.10. Troubleshooting IoT Integration Issues
Lesson 16: Data Refinery and Blockchain Integration
16.1. Overview of Blockchain Integration with Data Refinery
16.2. Connecting to Blockchain Data Sources
16.3. Handling Blockchain Data in Data Refinery
16.4. Data Refinery and Smart Contracts
16.5. Building Blockchain Analytics Pipelines
16.6. Case Study: Blockchain-Based Supply Chain Analytics
16.7. Advanced Blockchain Integration Techniques
16.8. Best Practices for Blockchain Integration
16.9. Security Considerations for Blockchain Integration
16.10. Troubleshooting Blockchain Integration Issues
Lesson 17: Data Refinery and Natural Language Processing (NLP)
17.1. Overview of NLP Integration with Data Refinery
17.2. Preparing Text Data for NLP Models
17.3. Integrating with Watson NLP Services
17.4. Building and Training NLP Models
17.5. Evaluating NLP Model Performance
17.6. Deploying NLP Models in Data Refinery
17.7. Automating NLP Workflows
17.8. Case Study: Sentiment Analysis of Customer Reviews
17.9. Advanced NLP Integration Techniques
17.10. Best Practices for NLP Integration
Lesson 18: Data Refinery and Computer Vision
18.1. Overview of Computer Vision Integration with Data Refinery
18.2. Preparing Image Data for Computer Vision Models
18.3. Integrating with Watson Computer Vision Services
18.4. Building and Training Computer Vision Models
18.5. Evaluating Computer Vision Model Performance
18.6. Deploying Computer Vision Models in Data Refinery
18.7. Automating Computer Vision Workflows
18.8. Case Study: Object Detection in Security Footage
18.9. Advanced Computer Vision Integration Techniques
18.10. Best Practices for Computer Vision Integration
Lesson 19: Data Refinery and Cybersecurity
19.1. Overview of Cybersecurity Integration with Data Refinery
19.2. Preparing Security Data for Analysis
19.3. Integrating with Watson Cybersecurity Services
19.4. Building and Training Cybersecurity Models
19.5. Evaluating Cybersecurity Model Performance
19.6. Deploying Cybersecurity Models in Data Refinery
19.7. Automating Cybersecurity Workflows
19.8. Case Study: Threat Detection and Response
19.9. Advanced Cybersecurity Integration Techniques
19.10. Best Practices for Cybersecurity Integration
Lesson 20: Data Refinery and Healthcare Analytics
20.1. Overview of Healthcare Integration with Data Refinery
20.2. Preparing Healthcare Data for Analysis
20.3. Integrating with Watson Health Services
20.4. Building and Training Healthcare Models
20.5. Evaluating Healthcare Model Performance
20.6. Deploying Healthcare Models in Data Refinery
20.7. Automating Healthcare Workflows
20.8. Case Study: Patient Outcome Prediction
20.9. Advanced Healthcare Integration Techniques
20.10. Best Practices for Healthcare Integration
Lesson 21: Data Refinery and Financial Services
21.1. Overview of Financial Services Integration with Data Refinery
21.2. Preparing Financial Data for Analysis
21.3. Integrating with Watson Financial Services
21.4. Building and Training Financial Models
21.5. Evaluating Financial Model Performance
21.6. Deploying Financial Models in Data Refinery
21.7. Automating Financial Workflows
21.8. Case Study: Fraud Detection in Transactions
21.9. Advanced Financial Integration Techniques
21.10. Best Practices for Financial Integration
Lesson 22: Data Refinery and Retail Analytics
22.1. Overview of Retail Integration with Data Refinery
22.2. Preparing Retail Data for Analysis
22.3. Integrating with Watson Retail Services
22.4. Building and Training Retail Models
22.5. Evaluating Retail Model Performance
22.6. Deploying Retail Models in Data Refinery
22.7. Automating Retail Workflows
22.8. Case Study: Customer Segmentation for Marketing
22.9. Advanced Retail Integration Techniques
22.10. Best Practices for Retail Integration
Lesson 23: Data Refinery and Manufacturing Analytics
23.1. Overview of Manufacturing Integration with Data Refinery
23.2. Preparing Manufacturing Data for Analysis
23.3. Integrating with Watson Manufacturing Services
23.4. Building and Training Manufacturing Models
23.5. Evaluating Manufacturing Model Performance
23.6. Deploying Manufacturing Models in Data Refinery
23.7. Automating Manufacturing Workflows
23.8. Case Study: Predictive Maintenance for Machinery
23.9. Advanced Manufacturing Integration Techniques
23.10. Best Practices for Manufacturing Integration
Lesson 24: Data Refinery and Energy Analytics
24.1. Overview of Energy Integration with Data Refinery
24.2. Preparing Energy Data for Analysis
24.3. Integrating with Watson Energy Services
24.4. Building and Training Energy Models
24.5. Evaluating Energy Model Performance
24.6. Deploying Energy Models in Data Refinery
24.7. Automating Energy Workflows
24.8. Case Study: Energy Consumption Optimization
24.9. Advanced Energy Integration Techniques
24.10. Best Practices for Energy Integration
Lesson 25: Data Refinery and Transportation Analytics
25.1. Overview of Transportation Integration with Data Refinery
25.2. Preparing Transportation Data for Analysis
25.3. Integrating with Watson Transportation Services
25.4. Building and Training Transportation Models
25.5. Evaluating Transportation Model Performance
25.6. Deploying Transportation Models in Data Refinery
25.7. Automating Transportation Workflows
25.8. Case Study: Route Optimization for Logistics
25.9. Advanced Transportation Integration Techniques
25.10. Best Practices for Transportation Integration
Lesson 26: Data Refinery and Telecommunications Analytics
26.1. Overview of Telecommunications Integration with Data Refinery
26.2. Preparing Telecommunications Data for Analysis
26.3. Integrating with Watson Telecommunications Services
26.4. Building and Training Telecommunications Models
26.5. Evaluating Telecommunications Model Performance
26.6. Deploying Telecommunications Models in Data Refinery
26.7. Automating Telecommunications Workflows
26.8. Case Study: Network Performance Optimization
26.9. Advanced Telecommunications Integration Techniques
26.10. Best Practices for Telecommunications Integration
Lesson 27: Data Refinery and Government Analytics
27.1. Overview of Government Integration with Data Refinery
27.2. Preparing Government Data for Analysis
27.3. Integrating with Watson Government Services
27.4. Building and Training Government Models
27.5. Evaluating Government Model Performance
27.6. Deploying Government Models in Data Refinery
27.7. Automating Government Workflows
27.8. Case Study: Public Service Optimization
27.9. Advanced Government Integration Techniques
27.10. Best Practices for Government Integration
Lesson 28: Data Refinery and Education Analytics
28.1. Overview of Education Integration with Data Refinery
28.2. Preparing Education Data for Analysis
28.3. Integrating with Watson Education Services
28.4. Building and Training Education Models
28.5. Evaluating Education Model Performance
28.6. Deploying Education Models in Data Refinery
28.7. Automating Education Workflows
28.8. Case Study: Student Performance Prediction
28.9. Advanced Education Integration Techniques
28.10. Best Practices for Education Integration
Lesson 29: Data Refinery and Media Analytics
29.1. Overview of Media Integration with Data Refinery
29.2. Preparing Media Data for Analysis
29.3. Integrating with Watson Media Services
29.4. Building and Training Media Models
29.5. Evaluating Media Model Performance
29.6. Deploying Media Models in Data Refinery
29.7. Automating Media Workflows
29.8. Case Study: Content Recommendation Systems
29.9. Advanced Media Integration Techniques
29.10. Best Practices for Media Integration
Lesson 30: Data Refinery and Real Estate Analytics
30.1. Overview of Real Estate Integration with Data Refinery
30.2. Preparing Real Estate Data for Analysis
30.3. Integrating with Watson Real Estate Services
30.4. Building and Training Real Estate Models
30.5. Evaluating Real Estate Model Performance
30.6. Deploying Real Estate Models in Data Refinery
30.7. Automating Real Estate Workflows
30.8. Case Study: Property Value Prediction
30.9. Advanced Real Estate Integration Techniques
30.10. Best Practices for Real Estate Integration
Lesson 31: Data Refinery and Hospitality Analytics
31.1. Overview of Hospitality Integration with Data Refinery
31.2. Preparing Hospitality Data for Analysis
31.3. Integrating with Watson Hospitality Services
31.4. Building and Training Hospitality Models
31.5. Evaluating Hospitality Model Performance
31.6. Deploying Hospitality Models in Data Refinery
31.7. Automating Hospitality Workflows
31.8. Case Study: Guest Experience Optimization
31.9. Advanced Hospitality Integration Techniques
31.10. Best Practices for Hospitality Integration
Lesson 32: Data Refinery and Agriculture Analytics
32.1. Overview of Agriculture Integration with Data Refinery
32.2. Preparing Agriculture Data for Analysis
32.3. Integrating with Watson Agriculture Services
32.4. Building and Training Agriculture Models
32.5. Evaluating Agriculture Model Performance
32.6. Deploying Agriculture Models in Data Refinery
32.7. Automating Agriculture Workflows
32.8. Case Study: Crop Yield Prediction
32.9. Advanced Agriculture Integration Techniques
32.10. Best Practices for Agriculture Integration
Lesson 33: Data Refinery and Environmental Analytics
33.1. Overview of Environmental Integration with Data Refinery
33.2. Preparing Environmental Data for Analysis
33.3. Integrating with Watson Environmental Services
33.4. Building and Training Environmental Models
33.5. Evaluating Environmental Model Performance
33.6. Deploying Environmental Models in Data Refinery
33.7. Automating Environmental Workflows
33.8. Case Study: Climate Change Impact Analysis
33.9. Advanced Environmental Integration Techniques
33.10. Best Practices for Environmental Integration
Lesson 34: Data Refinery and Social Media Analytics
34.1. Overview of Social Media Integration with Data Refinery
34.2. Preparing Social Media Data for Analysis
34.3. Integrating with Watson Social Media Services
34.4. Building and Training Social Media Models
34.5. Evaluating Social Media Model Performance
34.6. Deploying Social Media Models in Data Refinery
34.7. Automating Social Media Workflows
34.8. Case Study: Sentiment Analysis of Social Media Posts
34.9. Advanced Social Media Integration Techniques
34.10. Best Practices for Social Media Integration
Lesson 35: Data Refinery and E-commerce Analytics
35.1. Overview of E-commerce Integration with Data Refinery
35.2. Preparing E-commerce Data for Analysis
35.3. Integrating with Watson E-commerce Services
35.4. Building and Training E-commerce Models
35.5. Evaluating E-commerce Model Performance
35.6. Deploying E-commerce Models in Data Refinery
35.7. Automating E-commerce Workflows
35.8. Case Study: Customer Purchase Prediction
35.9. Advanced E-commerce Integration Techniques
35.10. Best Practices for E-commerce Integration
Lesson 36: Data Refinery and Supply Chain Analytics
36.1. Overview of Supply Chain Integration with Data Refinery
36.2. Preparing Supply Chain Data for Analysis
36.3. Integrating with Watson Supply Chain Services
36.4. Building and Training Supply Chain Models
36.5. Evaluating Supply Chain Model Performance
36.6. Deploying Supply Chain Models in Data Refinery
36.7. Automating Supply Chain Workflows
36.8. Case Study: Inventory Optimization
36.9. Advanced Supply Chain Integration Techniques
36.10. Best Practices for Supply Chain Integration
Lesson 37: Data Refinery and Human Resources Analytics
37.1. Overview of Human Resources Integration with Data Refinery
37.2. Preparing Human Resources Data for Analysis
37.3. Integrating with Watson Human Resources Services
37.4. Building and Training Human Resources Models
37.5. Evaluating Human Resources Model Performance
37.6. Deploying Human Resources Models in Data Refinery
37.7. Automating Human Resources Workflows
37.8. Case Study: Employee Retention Prediction
37.9. Advanced Human Resources Integration Techniques
37.10. Best Practices for Human Resources Integration
Lesson 38: Data Refinery and Customer Experience Analytics
38.1. Overview of Customer Experience Integration with Data Refinery
38.2. Preparing Customer Experience Data for Analysis
38.3. Integrating with Watson Customer Experience Services
38.4. Building and Training Customer Experience Models
38.5. Evaluating Customer Experience Model Performance
38.6. Deploying Customer Experience Models in Data Refinery
38.7. Automating Customer Experience Workflows
38.8. Case Study: Customer Satisfaction Analysis
38.9. Advanced Customer Experience Integration Techniques
38.10. Best Practices for Customer Experience Integration
Lesson 39: Data Refinery and Marketing Analytics
39.1. Overview of Marketing Integration with Data Refinery
39.2. Preparing Marketing Data for Analysis
39.3. Integrating with Watson Marketing Services
39.4. Building and Training Marketing Models
39.5. Evaluating Marketing Model Performance
39.6. Deploying Marketing Models in Data Refinery
39.7. Automating Marketing Workflows
39.8. Case Study: Campaign Effectiveness Analysis
39.9. Advanced Marketing Integration Techniques
39.10. Best Practices for Marketing Integration
Lesson 40: Data Refinery and Operations Analytics
40.1. Overview of Operations Integration with Data Refinery
40.2. Preparing Operations Data for Analysis
40.3. Integrating with Watson Operations Services
40.4. Building and Training Operations Models
40.5. Evaluating Operations Model Performance
40.6. Deploying Operations Models in Data Refinery
40.7. Automating Operations Workflows
40.8. Case Study: Operational Efficiency Optimization
40.9. Advanced Operations Integration Techniques