Data Science & Analytics Topics
Statistical analysis, data analytics, big data technologies, and data visualization. Covers statistical methods, exploratory analysis, and data storytelling.
Business Impact Measurement and Metrics
Selecting, measuring, and interpreting the business metrics and outcomes that demonstrate value and guide decisions. Topics include high level performance indicators such as revenue decompositions, lifetime value, churn and retention, average revenue per user, unit economics and cost per transaction, as well as operational indicators like throughput, quality and system reliability. Candidates should be able to choose leading versus lagging indicators for a given question, map operational KPIs to business outcomes, build hypotheses about drivers, recommend measurement changes and define evaluation windows. Measurement and attribution techniques covered include establishing baselines, experimental and quasi experimental designs such as A B tests, control groups, difference in differences and regression adjustments, sample size reasoning, and approaches to isolate confounding factors. Also included are quick back of the envelope estimation techniques for order of magnitude impact, converting technical metrics into business consequences, building dashboards and health metrics to monitor programs, communicating numeric results with confidence bounds, and turning measurement into clear stakeholder facing narratives and recommendations.
Engineering and Business Outcomes
How engineering work and technical decisions translate into measurable business outcomes and how to demonstrate that linkage. Topics include mapping architecture choices, reliability, performance improvements and developer productivity initiatives to business metrics such as revenue, customer engagement, time to market, cost reduction and customer satisfaction. Candidates should be able to identify engineering metrics to track including latency, availability, error and incident rates, cycle time and deployment frequency, explain instrumentation strategies to capture signals, design measurement plans and experiments to establish causal impact, and attribute observed changes to specific engineering efforts. This topic also covers communicating technical tradeoffs and impact to nontechnical stakeholders, choosing appropriate granularity for measurement, and describing concrete initiatives with their measurement approach and quantified business impact.
Measurement Design and Analysis
Practical measurement design and analytic techniques for producing reliable metric signals and proving impact. Includes instrumentation and tracking plans, experiment selection and validation, attribution modeling and its limitations, sample size and statistical considerations, identifying confounding variables, and reasoning about correlation versus causation. Also covers tradeoffs in data collection and data quality checks, cohort and segmentation design, baselining and threshold setting, designing dashboards and monitoring cadence, and connecting engineering and telemetry data to business outcomes. Candidates should be able to write clear measurement plans and success criteria, describe experiment and validation approaches, and explain how to operationalize results through reporting and iteration.