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.
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.
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.
Data Interpretation & Dashboard Literacy
Practice interpreting data visualizations, trend lines, and metric dashboards. Develop ability to identify what's noteworthy (seasonality, anomalies, correlations) vs. normal variation. Think about causation vs. correlation. Practice explaining what a metric trend means in business terms and what actions it might suggest.
Correlation vs. Causation and Confounding Variables
Recognize that correlation (statistical relationship between variables) doesn't imply causation (direct cause-and-effect relationship). Identify confounding variables that might explain an observed correlation. For example, summer ice cream sales and crime rates both increase but neither causes the other—warm weather is the confounder. Practice identifying lurking variables in business scenarios.
Metrics Analysis and Data Driven Problem Solving
Skills for using quantitative metrics to diagnose and solve business, product, or operational problems across functions. Candidates should be able to identify the key performance indicators relevant to their domain (for example: conversion rate, retention, revenue per user, pipeline velocity, response time, or customer satisfaction), detect anomalies and trends in metrics, formulate and prioritize hypotheses about root causes, design experiments and controlled tests (such as A/B tests) to validate hypotheses, perform cohort and time series analysis, evaluate statistical significance versus practical business impact, and implement and monitor data backed solutions. This also includes instrumentation and data collection best practices, dashboarding and visualization to surface insights, trade off analysis when balancing multiple competing metrics, and communicating findings and recommended changes to cross functional stakeholders.
Interest in Data and Analytics
Evaluates a candidate's genuine curiosity about working with data and their practical comfort with quantitative information, spreadsheets, dashboards, reporting, and analytics tools. Strong responses describe specific hands on experience with data analysis, measurement, reporting, or analytics projects, including concrete examples of metrics tracked, analyses performed, dashboards or reports built, and outcomes or decisions influenced by those insights. Candidates should be able to articulate learning activities and motivations such as courses, personal or open source projects, reading, or tool exploration, and to candidly identify development areas such as structured query language, statistical methods, experiment design, or visualization techniques. The topic also assesses the candidate's ability to explain why data matters for the role and how they use evidence to inform product, process, or business decisions.
Operational Metrics and Monitoring
Covers selection, measurement, visualization, and interpretation of operational metrics used to manage systems and processes. Candidates should show understanding of what to measure, how to instrument and collect data, how to design dashboards and reports, and how to interpret trends and signals to guide operational decisions. Includes familiarity with common key performance indicators, defining success criteria, recognizing when new metrics are needed, and using metrics to prioritize work or detect regressions. At more junior levels, demonstrate comfort with reading dashboards and proposing metric improvements; at senior levels, show metric strategy and measurement design.
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.