Data Analytics & Analytics Engineering — Skills Plan
Planned skill map for Data Analytics & Analytics Engineering. The curriculum will be developed and released in stages.
Use SQL and dbt to create tested warehouse models, analyze funnels and cohorts, define trusted metrics, and communicate findings through reports.
Potential roles: Data Analyst · Analytics Engineer · Product Analyst · Reporting Analyst
Before you start
- Spreadsheets and descriptive statistics
- SQL basics and relational data
- Business questions and metric definitions
What you’ll build
- Build a tested warehouse analytics model
- Analyze a product funnel and communicate findings
- Deliver a governed KPI dashboard
Prerequisites
PLANNED- Spreadsheets and descriptive statistics
Build the Spreadsheets and descriptive statistics knowledge needed to follow the Data Analytics & Analytics Engineering foundations.
- SQL basics and relational data
Build the SQL basics and relational data knowledge needed to follow the Data Analytics & Analytics Engineering foundations.
- Business questions and metric definitions
Build the Business questions and metric definitions knowledge needed to follow the Data Analytics & Analytics Engineering foundations.
Foundations
PLANNED- Exploratory data analysis
Explain how Exploratory data analysis works and where it fits in the Data Analytics & Analytics Engineering learning path.
- Data quality and missing-value handling
Explain how Data quality and missing-value handling works and where it fits in the Data Analytics & Analytics Engineering learning path.
- Data visualization and chart selection
Explain how Data visualization and chart selection works and where it fits in the Data Analytics & Analytics Engineering learning path.
- Dimensional modeling and analytical grain
Explain how Dimensional modeling and analytical grain works and where it fits in the Data Analytics & Analytics Engineering learning path.
Core Skills
PLANNED- Advanced SQL and window functions
Use Advanced SQL and window functions to implement a working capability and verify its behavior in Data Analytics & Analytics Engineering.
- dbt models, tests, and documentation
Use dbt models, tests, and documentation to implement a working capability and verify its behavior in Data Analytics & Analytics Engineering.
- Version control and analytics workflows
Use Version control and analytics workflows to implement a working capability and verify its behavior in Data Analytics & Analytics Engineering.
- Cohort, funnel, and retention analysis
Use Cohort, funnel, and retention analysis to implement a working capability and verify its behavior in Data Analytics & Analytics Engineering.
- BI semantic layers and reusable metrics
Use BI semantic layers and reusable metrics to implement a working capability and verify its behavior in Data Analytics & Analytics Engineering.
Advanced / Production
PLANNED- Experiment analysis and causal caveats
Evaluate Experiment analysis and causal caveats trade-offs against the performance, reliability, and safety needs of Data Analytics & Analytics Engineering systems.
- Incremental models and late-arriving data
Evaluate Incremental models and late-arriving data trade-offs against the performance, reliability, and safety needs of Data Analytics & Analytics Engineering systems.
- Data contracts and freshness monitoring
Evaluate Data contracts and freshness monitoring trade-offs against the performance, reliability, and safety needs of Data Analytics & Analytics Engineering systems.
- Metric governance and stakeholder alignment
Evaluate Metric governance and stakeholder alignment trade-offs against the performance, reliability, and safety needs of Data Analytics & Analytics Engineering systems.
- Privacy-aware analytics and access control
Evaluate Privacy-aware analytics and access control trade-offs against the performance, reliability, and safety needs of Data Analytics & Analytics Engineering systems.
Real-World Projects
PLANNED- Build a tested warehouse analytics model
Deliver a working outcome with Build a tested warehouse analytics model, then test it against the project requirements.
- Analyze a product funnel and communicate findings
Deliver a working outcome with Analyze a product funnel and communicate findings, then test it against the project requirements.
- Deliver a governed KPI dashboard
Deliver a working outcome with Deliver a governed KPI dashboard, then test it against the project requirements.
Career / Interview Preparation
PLANNED- SQL and analytics case interviews
Explain your approach to SQL and analytics case interviews, defend key trade-offs, and discuss what you learned.
- Metric definition and experiment critique
Explain your approach to Metric definition and experiment critique, defend key trade-offs, and discuss what you learned.
- Communicating findings to non-technical partners
Explain your approach to Communicating findings to non-technical partners, defend key trade-offs, and discuss what you learned.
Discussion
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