Course Outline
Day 1
Foundations of Data Solutions & Strategy
Introduction to Contemporary Data Solutions
Distinction Between Data Solutions and Traditional Data Systems
Data as a Strategic Business Asset
Core Elements of a Data Solution Ecosystem
Identifying Business Challenges Suitable for Data Solutions
Overview of the Data Solution Lifecycle (Ideation through Scaling)
Case Studies: Successful Industry Examples
Day 2
Data Solution Design & Architecture
Principles of Data Solution Design
Understanding User Personas and Data Consumers
Data Architecture Models (Centralized vs. Data Mesh vs. Hybrid)
Designing Scalable Data Pipelines
Data Modeling for Analytics and Operational Use
APIs and Data Accessibility Layers
Cloud Infrastructure for Data Solutions (Overview of AWS / Azure / GCP)
Day 3
Data Engineering & Implementation
Data Ingestion Methods (Batch vs. Streaming)
ETL vs. ELT Frameworks
Constructing Robust Data Pipelines
Data Storage Solutions (Data Lakes, Warehouses, Lakehouse)
Data Transformation and Orchestration Tools
Introduction to Real-Time Data Processing
Practical Lab: Building a Basic Data Pipeline
Day 4
Analytics, AI Integration & Governance
Integrating Analytics into Data Solutions
Dashboards, KPIs, and Decision Intelligence
Introduction to AI/ML in Data Solutions
Recommendation Systems and Predictive Models
Data Quality Management and Monitoring
Data Governance, Privacy, and Compliance (Overview of GDPR concepts)
Ensuring Trust, Security & Reliability in Data Solutions
Day 5
Deployment, Scaling & Productization
Delivering Data Solutions to End Users
Deployment Strategies and CI/CD for Data Solutions
Monitoring, Performance Optimization & Scaling
Data Solution Lifecycle Management in Organizations
Monetization Strategies for Data Solutions
Future Trends: Generative AI & Autonomous Data Solutions
Capstone Project Presentation & Feedback Session
Requirements
- A foundational grasp of data concepts and business reporting is advisable.
- Prior experience with Excel or similar basic data analysis tools is advantageous.
- Familiarity with the role of data in business decision-making is beneficial.
- No advanced programming or technical expertise is necessary.
- A genuine interest in data, analytics, and digital product development is essential.
Testimonials (2)
The variety of the information shared and the clarity to explain terms in plain English.
Arisbe Mendoza - Fairtrade International
Course - GDPR Workshop
It's a hands-on session.