Data Foundation — Build Strong Core Data Skills
A practical, role-agnostic foundation program teaching data literacy, SQL, data modeling, metadata, data quality, basic ETL, and governance — ideal for analysts, PMs, engineers, and leaders.
Overview
Data Foundation equips participants with the essential knowledge and practical skills to work confidently with data. The program balances conceptual understanding (what data is, why it matters) with tactical skills (SQL, data modeling, quality checks) so teams speak a common data language and reduce downstream rework.
Who it's for
- Business analysts & product managers
- Junior data engineers & analysts
- Data-literate executives & stakeholders
What you'll gain
- SQL fluency and data querying confidence
- Understanding of data models & pipelines
- Practical data quality and metadata practices
Delivery
Self-paced videos, interactive notebooks, graded labs, and cohort office hours for instructor-led cohorts.
Curriculum — Module Breakdown
Compact, practical modules that build from fundamentals to applied skills.
Module 1 — Data Literacy & Concepts
- What is data? Types, units, and common pitfalls
- Data lifecycle: capture → store → transform → analyze
- Key metrics, KPIs, and measurement basics
- Intro to privacy, security, and ethics
Module 2 — SQL & Querying Fundamentals
- SELECT, JOINs, GROUP BY, window functions
- Subqueries, CTEs, and performance-aware queries
- Hands-on labs with Postgres / BigQuery / Snowflake
Module 3 — Data Modeling & Schemas
- Normalization vs denormalization
- Star schema, snowflake, and dimensional modeling
- Entity-relationship diagrams and practical patterns
Module 4 — Metadata & Data Catalogs
- Importance of metadata and data catalogs
- Tagging, lineage, and searchable catalogs
- Simple catalog implementation & best practices
Module 5 — Data Quality & Testing
- Data quality dimensions: accuracy, completeness, timeliness
- Validation rules, reconciliations, and SLA checks
- Implementing checks with dbt / Great Expectations
Module 6 — Basic ETL & Pipelines
- ETL vs ELT patterns and when to use each
- Incremental loads & CDC basics
- Orchestration fundamentals with Airflow examples
Module 7 — Governance, Privacy & Roles
- Data ownership, stewardship, and operating model
- Privacy basics: anonymization, masking, consent
- Access controls and least privilege patterns
Module 8 — Hands-on Capstone
Pull together skills: build a small data product — ingest data, model it, validate quality, and present analytics with a simple dashboard.
Labs & Tools
Hands-on labs use accessible tools so learners can practice with real systems.
SQL Lab
Query sample datasets (Postgres / BigQuery) — joins, windows, aggregation, and optimization tips.
ETL Lab
Build an ETL/ELT pipeline with simple Airflow DAGs and load into a warehouse.
Quality & Catalog Lab
Implement basic data tests with Great Expectations and add metadata to a lightweight catalog (e.g., Amundsen-like).
Pricing & Delivery Options
Self-paced
Full video library, notebooks, and graded quizzes. Recommended 6–8 weeks.
Cohort (Instructor-led)
6-week cohort, weekly live sessions, office hours, and capstone review.
Team / Enterprise
Bulk licensing, private cohorts, and tailored curriculum mapping to your data landscape.
Instructors & Support
Lead Instructor — Data Foundation Team
Experienced data practitioner team with backgrounds in analytics, data engineering, and product management. Office hours, graded feedback, and community forum included.
Get Started
Ready to build a strong data foundation for your team or career? Enroll in the self-paced course, join a cohort, or request an enterprise proposal.