Data Science & Analytics Topics
Statistical analysis, data analytics, big data technologies, and data visualization. Covers statistical methods, exploratory analysis, and data storytelling.
Data Driven Recommendations and Impact
Covers the end to end practice of using quantitative and qualitative evidence to identify opportunities, form actionable recommendations, and measure business impact. Topics include problem framing, identifying and instrumenting relevant metrics and key performance indicators, measurement design and diagnostics, experiment design such as A B tests and pilots, and basic causal inference considerations including distinguishing correlation from causation and handling limited or noisy data. Candidates should be able to translate analysis into clear recommendations by quantifying expected impacts and costs, stating key assumptions, presenting trade offs between alternatives, defining success criteria and timelines, and proposing decision rules and go no go criteria. This also covers risk identification and mitigation plans, prioritization frameworks that weigh impact effort and strategic alignment, building dashboards and visualizations to surface signals across HR sales operations and product, communicating concise executive level recommendations with data backed rationale, and designing follow up monitoring to measure adoption and downstream outcomes and iterate on the solution.
Data Driven Decision Making
Using metrics and analytics to inform operational and strategic decisions. Topics include defining and interpreting operational measures such as throughput cycle time error rates resource utilization cost per unit quality measures and on time delivery, as well as growth and lifecycle metrics across acquisition activation retention and revenue. Emphasis is on building audience segmented dashboards and reports presenting insights to influence stakeholders diagnosing problems through variance analysis and performance analytics identifying bottlenecks measuring campaign effectiveness and guiding resource allocation and investment decisions. Also covers how metric expectations change with seniority and how to shape organizational metric strategy and scorecards to drive accountability.
Analysis to Recommendation and Decision Framing
Ability to move from analysis to a concise, justified recommendation and a pragmatic plan for decision and implementation. Candidates should lead with a clear recommendation or conditional decision, support it with evidence and trade offs, quantify expected business impact, estimate effort and time horizon, and state assumptions and limitations. The skill set includes proposing prioritized action plans and alternative options, anticipating objections, defining monitoring and rollback strategies, translating technical remediation or risk into business terms and measurable success metrics, and tailoring recommendations to stakeholder needs and constraints.
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 Analysis and Requirements Translation
Focuses on translating ambiguous business questions into concrete data analysis plans. Candidates should identify the data points required, define metrics and key performance indicators, state assumptions to validate, design the analysis steps and queries, and explain how analysis results map back to business decisions. This includes data quality considerations, required instrumentation, and how analytical findings influence product requirements or architectural choices.
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.
Experiment Design Analysis and Causal Methods
Design and analysis of experiments and causal inference methods for when randomization is not possible. Candidates should know strategies to ensure randomization and evaluate experiment quality compute sample size and minimum detectable effect select and interpret primary and guardrail metrics and design appropriate test duration. Analysis skills include hypothesis testing p values confidence intervals effect size estimation variance estimation and variance reduction segmentation and interaction analysis and robust reporting of uncertainty. This topic covers observational and quasi experimental approaches such as propensity score matching difference in differences and regression discontinuity how to reason about confounding and selection bias and when to prefer a quasi experimental approach over a randomized test. Candidates should be able to translate causal conclusions into actionable guidance recommend follow up analyses and triangulate evidence across methods.
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.
Probability and Statistical Inference
Covers fundamental probability theory and statistical inference from first principles to practical applications. Core probability concepts include sample spaces and events, independence, conditional probability, Bayes theorem, expected value, variance, and standard deviation. Reviews common probability distributions such as normal, binomial, Poisson, uniform, and exponential, their parameters, typical use cases, computation of probabilities, and approximation methods. Explains sampling distributions and the Central Limit Theorem and their implications for estimation and confidence intervals. Presents descriptive statistics and data summary measures including mean, median, variance, and standard deviation. Details the hypothesis testing workflow including null and alternative hypotheses, p values, statistical significance, type one and type two errors, power, effect size, and interpretation of results. Reviews commonly used tests and methods and guidance for selection and assumptions checking, including z tests, t tests, chi square tests, analysis of variance, and basic nonparametric alternatives. Emphasizes practical issues such as correlation versus causation, impact of sample size and data quality, assumptions validation, reasoning about rare events and tail risks, and communicating uncertainty. At more advanced levels expect experimental design and interpretation at scale including A B tests, sample size and power calculations, multiple testing and false discovery rate adjustment, and design choices for robust inference in real world systems.