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.

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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.

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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.

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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.

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Design and Product Analytics

Using quantitative metrics to inform product and design decisions. Covers key user engagement metrics such as conversion rates, task completion, retention, and feature adoption, and how to instrument and interpret these signals using analytics platforms and product dashboards. Explains how quantitative data complements qualitative research, how to identify design problems from metrics, design experiments and metrics for validation, and how to translate findings into design priorities and success criteria.

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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.

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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.

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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.

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Advanced Data Analysis and Statistics

Focuses on higher level analytical and statistical techniques for interpreting data and testing hypotheses. Topics include time series analysis, cohort and segmentation analysis, correlation and causation distinctions, descriptive versus inferential statistics, experimental design and hypothesis testing, consideration of sample size and power, detection of confounding variables including Simpson s paradox, and practical interpretation of results and limitations. Emphasizes choosing appropriate methods for given questions and communicating statistical findings clearly.

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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.

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Metrics, Guardrails, and Evaluation Criteria

Design appropriate success metrics for experiments. Understand primary metrics, secondary metrics, and guardrail metrics. Know how to choose metrics that align with business goals while avoiding unintended consequences.

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Experiment Design and Practical Considerations

Defining metrics to measure (primary and secondary). Estimating sample size and duration needed. Choosing between between-subjects and within-subjects designs. Considering confounding variables and how to control for them. Planning for randomization strategy. Discussing trade-offs between statistical rigor and practical constraints.

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DoorDash Key Metrics & Dashboard Requirements

Defining and standardizing DoorDash KPIs, identifying data sources, calculating metric definitions, data governance, and designing dashboards and reporting pipelines to monitor product and business performance. Includes data visualization best practices, dashboard design, interactivity, drill-down capabilities, and alignment with business goals across operations, product, and marketplace analytics.

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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.

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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.

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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.

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Business Impact Measurement and Metrics

Selecting, measuring, and interpreting the metrics that show whether an initiative, product, or program actually delivered value, and using that evidence to guide decisions. Covers headline outcome metrics (revenue decomposition, customer lifetime value, churn and retention, average revenue per user, unit economics and cost per transaction) alongside operational indicators (throughput, quality, reliability) and how to connect the two. Candidates should be able to distinguish leading from lagging indicators, map operational metrics to business outcomes, form and test hypotheses about what is driving a metric, choose an evaluation window, and recommend changes to what gets measured. Also covers the fundamentals of establishing a valid baseline and comparison group (before/after checks, A/B tests, and other quasi-experimental comparisons when a controlled test is not possible), reasoning about whether an observed change is large enough and reliable enough to act on, and ruling out obvious confounding explanations. Includes quick back-of-the-envelope estimation for order-of-magnitude impact, translating technical or operational metrics into business consequences, building a simple health dashboard for a program or initiative, and communicating results (including uncertainty) as a clear, decision-ready narrative for stakeholders. Depth and specific techniques (for example difference-in-differences, regression discontinuity, or survival analysis) should scale to the role: some interviews probe rigorous experimental design, others probe sound judgment using simpler before/after comparisons.

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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.

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Data Analysis and Insight Generation

Ability to convert raw data into clear, evidence based business insights and prioritized recommendations. Candidates should demonstrate end to end analytical thinking including data cleaning and validation, exploratory analysis, summary statistics, distributions, aggregations, pivot tables, time series and trend analysis, segmentation and cohort analysis, anomaly detection, and interpretation of relationships between metrics. This topic covers hypothesis generation and validation, basic statistical testing, controlled experiments and split testing, sensitivity and robustness checks, and sense checking results against domain knowledge. It emphasizes connecting metrics to business outcomes, defining success criteria and measurement plans, synthesizing quantitative and qualitative evidence, and prioritizing recommendations based on impact feasibility risk and dependencies. Practical communication skills are assessed including charting dashboards crafting concise narratives and tailoring findings to non technical and technical stakeholders, along with documenting next steps experiments and how outcomes will be measured.

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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.

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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.

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Problem Definition and Hypothesis Formation

Break down ambiguous business questions into specific, answerable analytics problems and define what success looks like. Ask clarifying questions about business context, constraints, stakeholder expectations, and acceptance criteria. Use structured diagnosis and root cause analysis to isolate where a problem occurs by segmenting users, products, time periods, or geographies. Generate multiple testable hypotheses that explain observed outcomes, distinguish correlation from causation, and prioritize hypotheses by likelihood, potential impact, and ease of validation. Frame measurable metrics for each hypothesis and propose high level validation approaches or experiments to confirm or reject the hypotheses.

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Experimentation Metrics and Strategy

Designing experiments and selecting appropriate primary, secondary, and guardrail metrics to evaluate hypotheses while protecting long term user value. This includes choosing metrics that reflect both short term signal and long term outcomes, reasoning about metric interactions and potential unintended consequences, and applying statistical considerations such as minimum detectable effect, sample size and power analysis, test duration, and external validity across segments and platforms. Candidates should also discuss experiment risk mitigation, stopping rules, and how to operationalize experiment results into product decisions.

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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.

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Audience Segmentation and Cohorts

Covers methods for dividing users or consumers into meaningful segments and analyzing their behavior over time using cohort analysis. Candidates should be able to choose segmentation dimensions such as demographics, acquisition channel, product usage, geography, device, or behavioral attributes, and justify those choices for a given business question. They should know how to design cohort analyses to measure retention, churn, lifetime value, and conversion funnels, and how to avoid common pitfalls such as Simpson's Paradox and survivorship bias. This topic also includes deriving behavioral insights to inform personalization, content and product strategy, marketing targeting, and persona development, as well as identifying underserved or high value segments. Expect discussion of relevant metrics, data requirements and quality considerations, approaches to visualization and interpretation, and typical tools and techniques used in analytics and experimentation to validate segment driven hypotheses.

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Statistical Fundamentals and Exploratory Analysis

Core descriptive and exploratory statistical techniques used to summarize data, detect patterns, and generate testable hypotheses. Covers measures of central tendency and dispersion such as mean median and standard deviation, distributional assumptions, frequency and cross tabulation, visualization for exploration, cohorting and segmentation, identifying biases and data quality issues, and designing exploratory analyses to suggest causal hypotheses. Understand when to apply EDA to prepare data for formal tests and how to translate exploratory findings into confirmatory analyses. Candidates should demonstrate ability to summarize quantitative data, detect anomalies, and propose appropriate follow up hypothesis tests or experiments.

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Quantitative Analysis and Metrics Interpretation

Core skills for working with numeric business data: calculating and interpreting key metrics, comparing options numerically, identifying trends and anomalies, performing variance checks, and testing assumptions. Includes reading dashboards and query results, extracting meaningful insights from revenue and operational metrics, segmenting data, identifying outliers, and understanding what metrics indicate about business performance. Candidates should be comfortable stating and justifying assumptions, performing simple break even and cost benefit reasoning, and translating numbers into prioritized actions or follow up analyses. This topic covers cross functional metric types from sales and operations to product and marketing, and emphasizes structured thinking, correct metric definitions, basic descriptive statistics, and how to use data to support recommendations.

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Data and Trend Analysis with Pattern Recognition

Analyzing quantitative and qualitative data to identify patterns, trends, correlations, and meaningful insights. Skills assessed include descriptive statistics, time series and trend analysis, visualization and dashboarding, hypothesis generation and testing, identifying seasonality and structural changes, distinguishing signal from noise, and synthesizing findings into clear recommendations. For qualitative inputs candidates should demonstrate coding, theme extraction, categorization, and synthesis of transcripts or survey responses. Emphasis is on choosing appropriate methods, validating patterns, avoiding common pitfalls such as confounding and spurious correlation, and communicating insights effectively to stakeholders.

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Quantitative Research and Analysis

Covers end to end quantitative research methods used to measure and validate product and user behavior hypotheses. Topics include experimental and quasi experimental design, split testing and controlled experiments, metric definition and success criteria, sample size calculation and statistical power, selection of appropriate statistical tests and interpretation of statistical significance and effect sizes, confidence intervals, correlation versus causation, common statistical pitfalls and biases, analytics instrumentation and metric tracking, survey design and quantitative measurement, and data analysis workflows and tools used to analyze large scale user data. Candidates should be able to design experiments, justify metric choices, calculate sample size and duration, analyze results rigorously, and make data driven recommendations.

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Data Quality and Bias

Covers both the conceptual and technical aspects of data quality assessment, bias identification, and remediation. Candidates should be able to recognize common sources of bias including selection bias, confirmation bias, measurement bias, and sample limitations, and describe how these biases and methodological limitations affect conclusions. They should be able to document and communicate caveats and limitations clearly and responsibly. On the technical side, candidates should demonstrate techniques for detecting and handling missing values, duplicates, outliers, and inconsistent data types; explain trade offs between filtering, imputing, and transforming data; and discuss how data cleaning choices influence downstream analysis. Additional expected skills include validating cleaned data against expectations, performing sensitivity analyses to show how results change under different data handling decisions, tracking data provenance, and describing reproducible processes for data quality management.

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Problem Framing and Data Driven Recommendations

Covers the end to end process of turning ambiguous business questions into clear, actionable solutions using structured thinking and empirical evidence. Includes decomposing complex problems into root causes and manageable components, defining success criteria and key metrics, and selecting appropriate analytical approaches and frameworks. Encompasses extracting, cleaning, and synthesizing raw data into insights, using quantitative and qualitative evidence to generate and evaluate multiple solution options, and applying trade off and prioritization frameworks such as impact and effort. Requires producing evidence backed, prioritized recommendations with implementation considerations, sequencing and monitoring plans, and communicating findings clearly to stakeholders with varying levels of technical knowledge.

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Communicating Statistical Results to Business Stakeholders

Translating statistical findings into actionable business language. Explaining confidence, risk, and decision frameworks to non-technical audiences. Presenting trade-offs and uncertainties honestly. Building trust through clear communication.

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User Behavior and Analytics

Covers the ability to collect, analyze, and interpret user interaction data using analytics platforms and quantitative techniques. Candidates should demonstrate working knowledge of common analytics tools such as Google Analytics, Mixpanel, and Amplitude to navigate dashboards, create custom reports, define and instrument events, build funnels, and extract cohort and retention data. Core skills include segmentation by activity, demographics, or lifecycle stage, cohort analysis, funnel breakdowns by segment, retention curve interpretation, session and task level metrics, conversion rate analysis, and identifying behavioral patterns. Candidates should be able to pose testable hypotheses about user behavior, validate them using analytics data, surface data quality and instrumentation issues, support split testing, and translate findings into actionable product or design recommendations and prioritized next steps.

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Dashboard and Data Visualization Design

Principles and practices for designing, prototyping, and implementing visual artifacts and interactive dashboards that surface insights and support decision making. Topics include information architecture and layout, chart and visual encoding selection for comparisons trends distributions and relationships, annotation and labeling, effective use of color and white space, and trade offs between overview and detail. The topic covers interactive patterns such as filters drill downs tooltips and bookmarks and decision frameworks for when interactivity adds user value versus complexity. It also encompasses translating analytic questions into metrics grouping related measures, wireframing and prototyping, performance and data latency considerations for large data sets, accessibility and mobile responsiveness, data integrity and maintenance, and how statistical concepts such as statistical significance confidence intervals and effect sizes influence visualization choices.

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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.

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Data Science & Analytics Topics

Analysis to Recommendation and Decision Framing

Measurement Design and Analysis

Research and Learning Analytics

Data Interpretation & Dashboard Literacy

Design and Product Analytics

Correlation vs. Causation and Confounding Variables

Metrics Analysis and Data Driven Problem Solving

Interest in Data and Analytics

Advanced Data Analysis and Statistics

Program Evaluation and Measurement

Metrics, Guardrails, and Evaluation Criteria

Experiment Design and Practical Considerations

DoorDash Key Metrics & Dashboard Requirements

Data Driven Problem Solving and Recommendations

Analytical Problem Solving and Hypothesis Testing

Data Driven Recommendations and Impact

Business Impact Measurement and Metrics

Analytical Background

Data Analysis and Insight Generation

Metrics and KPI Fundamentals

Experiment Design, Analysis, and Causal Methods

Problem Definition and Hypothesis Formation

Experimentation Metrics and Strategy

Data Driven Decision Making

Audience Segmentation and Cohorts

Statistical Fundamentals and Exploratory Analysis

Quantitative Analysis and Metrics Interpretation

Data and Trend Analysis with Pattern Recognition

Quantitative Research and Analysis

Data Quality and Bias

Problem Framing and Data Driven Recommendations

Communicating Statistical Results to Business Stakeholders

User Behavior and Analytics

Dashboard and Data Visualization Design

Data Storytelling and Insight Communication