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.
Data Investigation and Root Cause Analysis
Techniques and a structured process for diagnosing metric changes and anomalies using quantitative evidence complemented by qualitative signals. Candidates should demonstrate how to validate that an observed change is a real signal and not noise or a reporting or instrumentation problem by checking data quality, event counts, sampling, and pipeline integrity. Describe slicing and decomposition strategies such as cohort segmentation, geography and platform segmentation, feature level analysis, time series decomposition to separate trend and seasonality, funnel and velocity analysis, retention analysis, and variance analysis. Explain how to form, prioritize, and test hypotheses; design diagnostic queries and tests using structured query language; and correlate metric changes with product releases, experiments, marketing activity, or external events. Include how to combine quantitative findings with qualitative research such as user interviews, session replay, logs, and support tickets to strengthen causal inference. Finally, cover communicating concise findings and actionable recommendations to stakeholders, creating reproducible queries and monitoring dashboards or alerts, and mentoring junior analysts on a systematic investigation approach.
Structured Query Language Fundamentals and Aggregation
This topic covers core Structured Query Language fundamentals for analytical querying and reporting. Candidates should be able to write correct, readable, and maintainable SELECT queries with filtering using WHERE, sorting with ORDER BY, grouping with GROUP BY, and group filtering with HAVING. They should apply aggregate functions such as COUNT, COUNT DISTINCT, SUM, AVG, MIN, and MAX and understand how NULL values affect results, how empty result sets behave, and when to use different counting approaches. The scope includes date and time filtering, basic cohort segmentation, and common time based comparisons used to compute metrics such as daily active users, average revenue per user, and period over period comparisons. Candidates are expected to use basic joins and join predicates including inner joins and left joins, write simple subqueries and conditional expressions, and perform common data transformation and cleansing patterns to prepare data for analysis. Finally, this topic assesses query readability and maintainability practices such as aliasing and formatting, plus awareness of elementary performance considerations including index usage and avoiding unnecessary full table scans for entry to mid level analytical tasks.