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 Duration 35 hours

Course Outline

Day 1

Core Principles of Data Solutions & Strategy
Introduction to Contemporary Data Products
Data Solutions vs. Legacy Data Systems
Viewing Data as a Strategic Corporate Asset
Essential Elements of a Data Solution Ecosystem
Selecting Business Challenges Amenable to Data Solutions
Overview of the Data Solution Lifecycle (From Concept to Scale)
Case Studies: High-Performing Data Solutions in Various Industries

Day 2

Designing Data Solutions & Architecture
Core Principles of Data Solution Design
Analyzing User Personas and Data Consumers
Data Architectural Models (Centralized vs. Data Mesh vs. Hybrid)
Constructing Scalable Data Pipelines
Data Modeling for Analytical and Operational Applications
APIs and Data Access Layers
Cloud Infrastructure for Data Solutions (Overview of AWS / Azure / GCP)

Day 3

Data Engineering & Execution
Data Ingestion Techniques (Batch vs. Streaming)
ETL vs. ELT Frameworks
Constructing Reliable Data Pipelines
Data Storage Options (Data Lakes, Warehouses, Lakehouse)
Data Transformation and Orchestration Tools
Intro to Real-Time Data Processing
Practical Lab: Creating a Basic Data Pipeline

Day 4

Analytics, AI Integration & Governance
Integrating Analytics into Data Solutions
Dashboards, KPIs, and Decision Intelligence
Introduction to AI/ML within Data Solutions
Recommendation Engines and Predictive Modeling
Data Quality Management and Monitoring
Data Governance, Privacy, and Compliance (GDPR overview)
Establishing Trust, Security & Reliability in Data Solutions

Day 5

Deployment, Scaling & Productization
Transforming Data Solutions into End-User Products
Deployment Strategies and CI/CD for Data Solutions
Monitoring, Performance Optimization & Scalability
Managing the Data Solution Lifecycle within Organizations
Monetization Strategies for Data Solutions
Future Trends: Generative AI & Autonomous Data Solutions
Capstone Project Presentation & Feedback Session

Requirements

  • A foundational grasp of data principles and corporate reporting is advised.
  • Proficiency with Excel or other introductory data analysis tools is advantageous.
  • An awareness of how data underpins business decision-making is valuable.
  • No advanced programming or specialized technical background is necessary.
  • A genuine interest in data, analytics, and digital product creation is required.

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