Data Science & Analytics Topics
Statistical analysis, data analytics, big data technologies, and data visualization. Covers statistical methods, exploratory analysis, and data storytelling.
Data Storytelling and Insight Communication
Skills for converting quantitative and qualitative analysis into a clear, persuasive narrative that guides stakeholders from findings to action. This includes leading with the headline insight, defining the business question, selecting the most relevant metrics and visual evidence, and structuring a concise story that explains what happened, why it happened, and what the recommended next steps are. Candidates should demonstrate tailoring of language and technical depth for diverse audiences from engineers to product managers to executives, summarizing trade offs and uncertainty in plain language, distinguishing correlation from causation, proposing follow up experiments or investigations, and producing concise executive summaries and status reports with an appropriate cadence. Interviewers evaluate the ability to persuade and align cross functional partners, answer questions about data validity and methodology, synthesize qualitative signals with quantitative results, and adapt presentation format and level of detail to the decision maker.
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.
Learning Analytics and Insights
Covers capturing, measuring, analyzing, and acting on data from learning programs and systems. Interviewers assess your approach to instrumentation and data collection across learning management systems, course modules, on the job assessments, and learner feedback; the metrics you track such as completion rates, time to competency, knowledge retention, learner engagement, and business impact; methods for identifying trends, skill gaps, and training effectiveness; experimental and quasi experimental approaches such as A B testing and cohort analysis to determine causality; dashboarding and reporting practices for different stakeholders; techniques for translating learning insights into program design changes, content optimization, learning technology investments, and return on learning calculations. Candidates should be able to describe data sources, data quality checks, analytic methods, examples of insights discovered, and how those insights influenced decisions and measured outcomes.
Learning Effectiveness and Evaluation
Covers frameworks and practices for evaluating the impact of learning programs and measuring learning effectiveness, from reaction and satisfaction through learning, behavior change, and business results. Includes discussion of common evaluation models such as the Kirkpatrick four levels, designing learning with measurable outcomes, assessing transfer of training to the job, and selecting appropriate metrics at each level (completion rates, assessment scores, skill measures, behavioral indicators, and business impact measures). Addresses how to measure and report return on investment and other business outcomes, tools and methods for data collection and analysis, attribution challenges when linking learning to business results, and how to use evaluation data to iterate and improve programs over time. Preparation should enable candidates to explain evaluation design choices, tradeoffs between ease of measurement and business relevance, examples of metrics and data sources, and approaches to demonstrating value at entry and senior levels.
Program Evaluation and Measurement
Assessing whether learning, people, and other organizational programs achieve their objectives and deliver measurable value. This includes defining success criteria and baseline metrics before implementation, selecting quantitative and qualitative measures during and after delivery, and understanding different measurement levels such as reaction, learning, behavior, and results as described in the Kirkpatrick model. Candidates should be able to design evaluation plans that include completion and engagement metrics, knowledge and skill assessments, behavior or application measures, retention and performance indicators, and business outcomes. The description should cover leading and lagging indicators, approaches to isolating program impact from confounding factors, simple experimental or quasi experimental designs when feasible, pragmatic trade offs between ideal and practical measurement, data collection methods and tools, calculating and communicating return on investment both financial and non financial, and tailoring reporting to stakeholders. Examples might include measuring onboarding effects on time to productivity, mentorship impact on retention, or communications effectiveness on benefits adoption. For junior roles, demonstrate familiarity with how to think about measurement choices and limitations; for senior roles, include designing robust evaluation frameworks and translating findings into business recommendations.
Research and Learning Analytics
Using structured research and learning data to inform decisions. Covers primary and secondary research methods, synthesizing market or user research, evaluating evidence quality, and using learning analytics to measure program effectiveness or skill gaps. Candidates should demonstrate how they gather appropriate research sources, interpret results, challenge assumptions, and apply findings to product, go to market, or learning and development decisions.
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.
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.