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.

0 questions

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.

0 questions

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.

0 questions

Technical Analysis and Methodology

Focuses on the technical depth and concrete analytical methods you use to produce reliable quantitative results. Interviewers look for how you validate assumptions, stress test key inputs, choose modeling techniques, and apply appropriate tools and processes. This includes building and auditing models, performing sensitivity and scenario analysis, data cleaning and transformation, statistical or econometric methods where relevant, and using software such as advanced spreadsheet techniques, scripting languages, or database queries to manipulate data. Candidates should be able to articulate their preferred tools and methods at a level appropriate to the interview and explain trade offs between model complexity and interpretability.

0 questions

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.

0 questions

Python Data Manipulation with Pandas & PySpark

Techniques for cleaning, transforming, and analyzing data in Python using Pandas and PySpark. Covers working with DataFrames, data wrangling, missing-value handling, filtering, aggregations, joins, grouping, and typical patterns for data preparation and exploratory analysis, including both in-memory Pandas workflows and distributed PySpark processing.

51 questions

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.

0 questions

Large Dataset Management and Technical Analysis

Develop skills in working efficiently with large datasets: data cleaning and validation, efficient aggregation and manipulation, handling missing data, identifying and managing outliers. Master advanced Excel features or learn SQL for database queries. Practice data quality assessment. Learn efficient workflows that scale with dataset size. Understand data security and privacy considerations.

0 questions

Ride-Hailing Demand Modeling & Forecasting

Techniques for modeling and forecasting ride-hailing demand, including time-series forecasting, demand drivers, feature engineering, model selection (e.g., ARIMA, Prophet, ML-based predictors), evaluation metrics (MAPE, RMSE), and deployment considerations within analytics workflows for transportation data.

0 questions

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.

45 questions

Exploratory Data Analysis

Exploratory Data Analysis is the systematic process of investigating and validating a dataset to understand its structure, content, and quality before modelling or reporting. Core activities include examining schema and data types, computing descriptive statistics such as counts, means, medians, standard deviations and quartiles, and measuring class balance and unique value counts. It covers distribution analysis, outlier detection, correlation and relationship exploration, and trend or seasonality checks for time series. Data validation and quality checks include identifying missing values, anomalies, inconsistent encodings, duplicates, and other data integrity issues. Practical techniques span SQL profiling and aggregation queries using GROUP BY, COUNT and DISTINCT; interactive data exploration with pandas and similar libraries; and visualization with histograms, box plots, scatter plots, heatmaps and time series charts to reveal patterns and issues. The process also includes feature summary and basic metric computation, sampling strategies, forming and documenting hypotheses, and recommending cleaning or transformation steps. Good Exploratory Data Analysis produces a clear record of findings, assumptions to validate, and next steps for cleaning, feature engineering, or modelling.

0 questions

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.

0 questions

Customer and Marketing Performance Analytics

Covers the end to end use of quantitative analysis to track, interpret, and act on business performance across accounts and campaigns. Candidates should be fluent in account level metrics such as customer retention rate, net revenue retention, annual recurring revenue, net promoter score, customer health scores, and customer lifetime value, as well as marketing and acquisition metrics such as click through rate, conversion rate, customer acquisition cost, return on advertising spend, and attribution model outcomes. Expect discussion of data sources and instrumentation, cohort and funnel analysis, segmentation, anomaly detection, attribution approaches, and calculating return on investment for initiatives. Candidates should be able to describe how they used analytics tools and queries, dashboards, and experiments or A B tests to identify at risk accounts or underperforming campaigns, prioritize actions, optimize strategies, and measure the impact of initiatives. Strong answers explain concrete metrics chosen, analysis methods, tools used, how results informed decisions, and how success was measured over time.

0 questions

End To End Case Study: Measurement Frameworks

Practice designing measurement frameworks for new products, features, or business models. Include defining the success criteria, identifying key user segments, setting up tracking, and planning for ongoing analysis.

0 questions

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.

0 questions

Window Functions and Time Series Analytics

Advanced SQL window functions: ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD, and aggregate functions (SUM, COUNT, AVG) with OVER and partition clauses. Using window functions to solve practical problems: ranking users or events within segments, calculating running totals and cumulative metrics, identifying trends and transitions over time, detecting patterns in user behavior sequences. Applications: cohort retention analysis (calculating retention rates across cohorts), user lifetime value trends, engagement metrics over time windows, and sequential user actions.

36 questions

Data and Business Outcomes

This topic focuses on converting data analysis and insights into actionable business decisions and measurable outcomes. Candidates should demonstrate the ability to translate trends into business implications, choose appropriate key performance indicators, design and interpret experiments, perform cohort or funnel analysis, reason about causality and data quality, and build dashboards or reports that inform stakeholders. Emphasis should be on storytelling with data, framing recommendations in terms of business levers such as revenue, retention, acquisition cost, and operational efficiency, and explaining instrumentation and measurement approaches that make impact measurable.

0 questions

Airbnb-Specific Data Patterns

Domain-specific data modeling and analytics patterns used in Airbnb-scale product analytics. Covers data schema design, event and transaction patterns, feature engineering templates for predictive models, cohort and lifecycle analytics, geospatial and temporal data patterns, price and demand forecasting signals, AB testing data patterns, and data quality, governance, and lineage considerations relevant to Airbnb data.

40 questions

Data Quality and Lead Flow Analysis

Recognize common data quality issues: duplicate records, missing or null values, incorrect data types, inconsistent formatting, stale data. Understand how bad data affects downstream processes (e.g., bad email addresses lead to failed campaigns; duplicate leads confuse the sales team; missing data makes metrics unreliable). Practice analyzing lead flow data: How many leads came in by source? At what stage did they drop off? Which sources produce the highest quality leads? For entry-level, you should be able to write queries that identify data quality issues and articulate the business impact. You don't need to implement automated data cleansing pipelines, but you should demonstrate understanding of why data quality matters and how to investigate problems.

0 questions

Streaming Platform Data Analysis Scenarios

Data analysis scenarios for subscription video streaming platforms, covering streaming and engagement metrics (DAU/MAU, watch time, completion rate, session length), user retention and cohort analysis, content consumption patterns, recommendation system evaluation, A/B test design and analysis, and data visualization and storytelling in the streaming domain.

0 questions

Technical Foundations for Business Analysis

This topic evaluates the baseline technical knowledge that enables a business analyst to turn business questions into verifiable analysis and to collaborate effectively with engineers and data teams. Candidates should be familiar with structured query language fundamentals for extracting and aggregating data, common business intelligence and spreadsheet tooling, basic data modeling and entity relationship concepts, and systems thinking to trace data flows and diagnose issues. Strong responses illustrate how the candidate uses these skills to validate requirements, design testable acceptance criteria, and identify when to escalate complex technical problems.

0 questions

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.

0 questions

Statistical Analysis and Interpretation

Demonstrates sound statistical methods for analyzing quantitative data and translating findings into actionable business insight. Core skills include descriptive statistics and distributional analysis (percentiles, median, mean, standard deviation, measures of dispersion), outlier detection and handling, correlation assessment, and basic to multivariable regression to control for confounding variables. Also covers hypothesis testing with interpretation of p-values and confidence intervals, evaluation of sample size and statistical power, and awareness of pitfalls such as confounding, multiple comparisons, and violations of model assumptions. Candidates should be able to choose appropriate statistical tests for a given analysis, validate models, and present statistical results clearly to nontechnical stakeholders.

0 questions

Compensation Data Problem Solving

Show a structured approach to solving compensation problems from first principles through to actionable recommendations. Interviewers expect candidates to define the business question and success metrics, identify and prioritize required data sources and fields (payroll, human resources information system, job codes, performance ratings, market surveys), specify data transformations and joins, conduct data quality checks and cleaning, choose appropriate analytic techniques and metrics (percentiles, gap measures, variance decomposition, regression controls), validate assumptions and test robustness, and produce clear recommendations with implementation steps, monitoring plans, and documented limitations. Emphasize reproducibility, handling of sensitive data, and communication of uncertainty and trade offs.

0 questions

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.

0 questions

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.

0 questions

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.

45 questions

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.

0 questions

SQL for Data Analysis

Using SQL as a tool for data analysis and reporting. Focuses on writing queries to extract metrics, perform aggregations, join disparate data sources, use subqueries and window functions for trends and rankings, and prepare data for dashboards and reports. Includes best practices for reproducible analytical queries, handling time series and date arithmetic, basic query optimization considerations for analytic workloads, and when to use SQL versus built in reporting tools in analytics platforms.

45 questions

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.

47 questions

Data Problem Solving and Business Context

Practical data oriented problem solving that connects business questions to correct, robust analyses. Includes translating business questions into queries and metric definitions, designing SQL or query logic for edge cases, handling data quality issues such as nulls duplicates and inconsistent dates, validating assumptions, and producing metrics like retention and churn. Emphasizes building queries and pipelines that are resilient to real world data issues, thinking through measurement definitions, and linking data findings to business implications and possible next steps.

0 questions

Data Analysis and Requirements Translation

Focuses on translating ambiguous business questions into concrete, actionable data analysis plans. Candidates should identify what data is needed to answer the question, define the metrics or KPIs that would settle it, state and validate the assumptions behind those definitions, and lay out the concrete analysis steps or queries that would produce an answer. Strong answers connect analysis choices back to the business decision at stake: what would change stakeholder behavior or strategy, what data quality or data availability issues could undermine the conclusion, and what additional data collection, reporting, or systems changes would be needed to answer the question reliably going forward.

40 questions

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.

0 questions

Business Intelligence Background

A summary of business intelligence experience including the BI platforms and tools used, types of dashboards and reports built, data volumes and sources, analytical methods, stakeholder consumption patterns, and measurable business outcomes. Candidates should explain how BI efforts influenced decisions, examples of ETL or modeling work, and any leadership or ownership of BI initiatives.

47 questions

Metrics Selection and Dashboard Storytelling

Focuses on selecting metrics and designing dashboards and reports that directly support stakeholder decision making. Candidates should be able to identify distinct audiences and the specific decisions each audience must make, choose actionable metrics rather than vanity metrics, and balance leading indicators with lagging indicators as well as strategic metrics with operational metrics. This topic covers defining key performance indicators and targets and justifying each metric by the decision it enables, setting data freshness requirements and update cadence, and ensuring instrumentation and data quality to make metrics reliable. It includes dashboard architecture and visual narrative design such as layering from high level summaries to detailed drill down, tailoring views for executives, managers, and operational teams, selecting appropriate visualizations and annotations to guide interpretation, and enabling root cause analysis. Reporting practices are covered, including formatting, distribution channels, and alerting. Governance and metric definition topics include creating a single source of truth, assigning ownership, documenting definitions, and change control. Candidates must also recognize metric interactions and common pitfalls that can make metrics misleading such as aggregation bias, sampling issues, correlation versus causation, and perverse incentives, and propose mitigations. Interview questions typically ask candidates to design metric sets and dashboards for hypothetical scenarios, explain why metrics were chosen based on decisions they support, and describe cadence, distribution, drilling, and governance approaches.

0 questions

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.

0 questions

End To End Data Preprocessing & Exploration

Follow a systematic, tool-agnostic data pipeline before deeper analysis: load the data, check shape and dtypes, identify missing values and duplicates, explore distributions, check for outliers, understand class or category balance where relevant, and summarize key statistics. Document findings and build visualizations that surface relationships in the data. This exploration is the foundation for whatever comes next: feature engineering and model selection for predictive/ML work, or clean aggregations and trustworthy KPIs for dashboards and reporting.

0 questions

Trend Analysis and Anomaly Detection

Covers methods for detecting and interpreting deviations in metric behavior over time and determining whether changes reflect real product or user behavior versus noise. Topics include baseline establishment, seasonality and holiday effects, time series decomposition, smoothing and aggregation choices, statistical detection techniques such as control charts, z scores, EWMA and CUSUM, thresholding strategies, and modern algorithmic approaches like isolation forest or LSTM-based detectors. Also covers visualization and dashboarding practices for communicating trends, setting sensible alerting rules, triage workflows for investigating anomalies, and assessing business impact to prioritize fixes or rollbacks.

40 questions

Cohort Analysis and Retention Metrics

Cohort analysis and retention metrics cover methods for grouping users into cohorts by acquisition date, behavior, channel, geography, or other attributes and tracking their behavior over time. Candidates should be comfortable defining cohorts, computing retention curves and retention tables, and calculating key metrics such as day one retention, day seven retention, rolling retention, repeat engagement, churn rates, and cohort lifetime value. Understand how to interpret retention curve shapes and cohort trends to diagnose product market fit, onboarding problems, or channel quality, and how retention drives unit economics and revenue. Practical skills include writing queries in structured query language to segment users and produce cohort tables, plotting retention curves, comparing cohorts across acquisition channels, running cohort based experiments and A B tests, and using cohort insights to prioritize product changes and growth experiments.

0 questions

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.

40 questions

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.

0 questions

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.

0 questions

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.

0 questions

SQL for Growth Analytics

Domain specific SQL patterns used in growth analytics: cohort analysis, retention and churn calculations, funnel analysis for multi step user journeys, acquisition cohort queries, lifetime value computations, customer segmentation, and event aggregation for time series. Emphasis on USING GROUP BY, window functions, CTEs, date bucketing, cohort windows, and efficient joins against event tables to compute retention curves, conversion rates, and growth metrics at scale.

0 questions

Data Exploration and Quality Assessment

Investigate a dataset thoroughly before analysis or reporting by profiling its structure, contents, and reliability. Typical steps include examining row counts and data volume, inspecting column data types and sample values, validating date formats and ranges, and identifying missing values, duplicates, outliers, and impossible values. Understand schema and relationships between tables or files, check data freshness and latency, and characterize data completeness and coverage with simple metrics and queries. Document discovered issues, their likely causes and impacts on conclusions, and pragmatic workarounds or transformation strategies to mitigate risk. Use exploratory queries and summary statistics to quantify data quality, note limitations and assumptions, and allocate an appropriate portion of case study time to data assessment before proceeding to modeling or visualization.

0 questions

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.

0 questions

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.

0 questions

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.

0 questions

Data Investigation and Root Cause Analysis

Techniques and a structured process for diagnosing an unexpected change in a metric, dataset, or system signal 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, instrumentation, or pipeline problem, by checking data quality, event or record counts, sampling, schema stability, and pipeline or data-flow integrity. Describe slicing and decomposition strategies such as cohort or population 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 or equivalent tooling; and correlate the change with plausible triggers such as releases or deployments, configuration or schema changes, experiments, campaigns, upstream system incidents, or external events. Include how to combine quantitative findings with qualitative evidence such as interviews, logs, session or trace replay, support tickets, or incident timelines to strengthen causal inference. Finally, cover communicating concise findings and actionable recommendations to stakeholders, creating reproducible queries and monitoring dashboards, alerts, or runbooks, and mentoring others on a systematic investigation approach. This applies broadly to investigating anomalies in business metrics, product data, system or service health signals, financial figures, or model performance, not only one of these domains.

0 questions

Data Science & Analytics Topics

Analysis to Recommendation and Decision Framing

Measurement Design and Analysis

Engineering and Business Outcomes

Technical Analysis and Methodology

Metrics Analysis and Data Driven Problem Solving

Python Data Manipulation with Pandas & PySpark

Interest in Data and Analytics

Large Dataset Management and Technical Analysis

Ride-Hailing Demand Modeling & Forecasting

Structured Query Language Fundamentals and Aggregation

Exploratory Data Analysis

Advanced Data Analysis and Statistics

Customer and Marketing Performance Analytics

End To End Case Study: Measurement Frameworks

DoorDash Key Metrics & Dashboard Requirements

Window Functions and Time Series Analytics

Data and Business Outcomes

Airbnb-Specific Data Patterns

Data Quality and Lead Flow Analysis

Streaming Platform Data Analysis Scenarios

Technical Foundations for Business Analysis

Data Driven Problem Solving and Recommendations

Statistical Analysis and Interpretation

Compensation Data Problem Solving

Analytical Problem Solving and Hypothesis Testing

Business Impact Measurement and Metrics

Analytical Background

Data Analysis and Insight Generation

SQL for Data Analysis

Metrics and KPI Fundamentals

Data Problem Solving and Business Context

Data Analysis and Requirements Translation

Problem Definition and Hypothesis Formation

Business Intelligence Background

Metrics Selection and Dashboard Storytelling

Data Driven Decision Making

End To End Data Preprocessing & Exploration

Trend Analysis and Anomaly Detection

Cohort Analysis and Retention Metrics

SQL for Business Scenarios

Statistical Fundamentals and Exploratory Analysis

Quantitative Analysis and Metrics Interpretation

Data and Trend Analysis with Pattern Recognition

SQL for Growth Analytics

Data Exploration and Quality Assessment

Data Quality and Bias

Problem Framing and Data Driven Recommendations

User Behavior and Analytics

Data Investigation and Root Cause Analysis