Data Science & Analytics Topics
Statistical analysis, data analytics, big data technologies, and data visualization. Covers statistical methods, exploratory analysis, and data storytelling.
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.
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.
Program Evaluation and Measurement
Assessing whether a program, initiative, or intervention achieves its intended objectives and delivers measurable value, across domains such as training and development, product or feature rollouts, operational process changes, and organizational or culture initiatives. This includes defining success criteria and baseline metrics before implementation, selecting quantitative and qualitative measures during and after delivery, and evaluating impact across multiple levels: immediate reaction, learning or adoption, behavior or usage change, and downstream business results (the logic behind frameworks like the Kirkpatrick model, applied broadly to any program with a change-in-behavior goal, not only training). Candidates should be able to design evaluation plans that include completion and engagement metrics, knowledge or skill assessments, behavior or application measures, retention or usage indicators, and business outcomes. The topic covers 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 different stakeholders. Examples might include measuring onboarding's effect on time to productivity, a new internal tool's effect on team throughput, a communications campaign's effect on feature adoption, or a process change's effect on error rates. For junior level roles, demonstrate familiarity with measurement choices and their limitations; for senior level roles, include designing robust evaluation frameworks and translating findings into business recommendations.
Data Driven Problem Solving and Recommendations
Use data to define business problems, form and test hypotheses, identify root causes, and produce clear, prioritized, evidence based recommendations. Candidates should be able to translate a business question into measurable metrics, choose appropriate analyses, segment and compare cohorts, validate assumptions, quantify expected impact and implementation effort, and surface limitations or data quality issues. Good answers explain the analysis steps, any assumptions made, how results would be validated, and the communication approach to ensure stakeholders can act on the recommendation.
Analytical Problem Solving and Hypothesis Testing
Assesses the candidate's ability to convert an ambiguous business problem or signal into structured hypotheses, design analyses or experiments, and draw defensible conclusions. Expect discussion of problem framing, prioritizing hypotheses, selecting data sources, defining cohorts and metrics, designing queries or tests, validating assumptions, controlling for confounders, and communicating actionable recommendations. Core skills include critical thinking, data exploration, statistical reasoning, and translating insights into measurable action plans.
Analytical Background
The candidate's approach to analytical, evidence-based problem solving: how they take an ambiguous question, break it into testable pieces, gather and examine relevant information or data, choose appropriate methods to reach a conclusion, and turn that conclusion into a concrete recommendation or decision. This can show up as quantitative work (statistics, data analysis, experimentation, dashboards) or as qualitative and domain-specific analysis (reviewing logs or incidents, case or contract research, market or process analysis, root-cause investigation). Draw on academic projects, internships, or professional work. Focus on the end-to-end path: how the question or hypothesis was framed, what evidence was examined and with what tools or methods, what trade-offs were considered, and how the resulting insight changed a real decision or outcome.
Metrics and KPI Fundamentals
Core principles and practical fluency for defining, measuring, and interpreting metrics and key performance indicators, applicable across any professional domain. Candidates should be able to select meaningful metrics aligned to business objectives rather than vanity metrics, explain the difference between a metric and a target, and distinguish leading indicators from lagging indicators. Coverage includes decomposing complex outcomes into actionable component metrics, writing precise metric definitions (for example what counts as an active user, a completed case, a qualified lead, or a resolved ticket, depending on the domain), calculating common rate-based metrics such as engagement rate, churn rate, conversion rate, cycle time, or utilization rate, establishing baselines and sensible targets, and interpreting signal versus noise including awareness of statistical variability. Also includes using segmentation and cohort analysis to diagnose metric movements, and recommending two to three meaningful metrics for a hypothetical problem in the candidate's own domain with justification and action plans.
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.
SQL for Business Scenarios
Ability to read and decompose business questions and translate them into correct, efficient Structured Query Language queries that answer those questions. This includes identifying the required data sources and joins, choosing between inner joins, outer joins, anti joins and existence checks, writing subqueries and common table expressions for clarity, and applying filtering with where clauses, aggregation with group by and having, and window functions for ranking, running totals, and time series calculations. Candidates should demonstrate how to implement common business analyses such as conversion funnels, retention and cohort analysis, churn and lifetime value calculations, and operational metrics by mapping metric definitions to SQL expressions and handling edge cases like null values, duplicates, and late arriving data. The description also covers writing medium complexity queries that combine multiple tables, calculating derived metrics, validating results with sample data, and considering query performance through basic optimization techniques, indexing awareness, and selective projection.