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강좌: AI Workflow: Business Priorities and Data Ingestion. 되돌아가려면 여기을 클릭하세요.
- Course Introduction
- About this course
- Target Audience
- Required skills
- An introduction to IBM Watson Studio and IBM Design Thinking
- Overview of IBM Watson Studio
- IBM Watson Studio - Create a project
- Am I ready?
- Am I ready to take this Specialization?
- Readiness Quiz Review
- Workflow Overview
- Advantages and disadvantages of process models
- Data Science Process Models
- The design thinking process
- Data science workflow combined with design thinking
- Process Models, Design Thinking, and Introduction: Summary/Review
- Data Collection Overview
- Data Collection Objectives
- Introduction to Business Opportunities
- Identifying the business opportunity: Through the eyes of our Working Example
- Introduction to Scientific Thinking for Business
- Scientific Thinking for Business
- Introduction to Gathering Data
- AI Workflow: Gathering data
- Gathering Data
- Data Collection: Summary/Review
- Introduction to Data Ingestion
- AI Workflow: Data ingestion
- Data Engineering
- Limitations of Extract, Transform, Load (ETL)
- Data ingestion in the modern enterprise
- Enterprise data stores for data ingestion
- Why we need a data ingestion process
- Data ingestion and automation
- AI Workflow: Sparse matrices for data pipeline development
- Sparse matrices are used early in data ingestion development
- Getting started Watson Studio
- Using Watson Studio to complete the case study
- Case Study Introduction
- Case Study
- Getting Started
- Data Sources
- PART 1: Gathering the data
- PART 2: Checks for quality assurance (Includes Assessment)
- PART 3: Automating the process (Includes Assessment)
- Data Ingestion: Summary/Review