Causal Inference, Forecasting & Decision Science
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10 lessons3 levels1 capstone project
Move beyond prediction into decisions: design experiments, reason about confounding and causality, estimate treatment effects, build forecasts, quantify uncertainty and use optimization/scenario analysis to recommend actions.
BASIC
- Probability Distributions — StatQuest (7 min)
Fast uncertainty foundation needed for causal and forecasting work. - AI Experimentation Webinar — Statsig (30 min selected)
Practical experimentation patterns, metrics and decision-making under noisy outcomes. - Bayes Theorem — StatQuest (15 min selected)
Build evidence-updating intuition for uncertainty and decision analysis.
INTERMEDIATE
- What Is MLOps? — IBM Technology
Apply Causal Inference, Forecasting & Decision Science through the adjacent capability: MLOps & Model Serving. - MLOps Zoomcamp — DataTalksClub
Apply Causal Inference, Forecasting & Decision Science through the adjacent capability: MLOps & Model Serving. - Architecture & Key Concepts — Snowflake
Apply Causal Inference, Forecasting & Decision Science through the adjacent capability: Data Platform.
ADVANCED
- Quality, Lineage, Privacy and Trust — Data Governance
Extend Causal Inference, Forecasting & Decision Science into senior/production practice through Data Quality, Governance & Lineage. - Airflow, Databricks, Kubernetes — Build a Data Platform from Scratch
Extend Causal Inference, Forecasting & Decision Science into senior/production practice through Data Pipelines, Orchestration & Streaming. - Key Building Block for Governance — Data Lineage
Extend Causal Inference, Forecasting & Decision Science into senior/production practice through Data Quality, Governance & Lineage.