Data Science & Analytics Topics
Statistical analysis, data analytics, big data technologies, and data visualization. Covers statistical methods, exploratory analysis, and data storytelling.
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.
Business Impact Measurement and Metrics
Selecting, measuring, and interpreting the business metrics and outcomes that demonstrate value and guide decisions. Topics include high level performance indicators such as revenue decompositions, lifetime value, churn and retention, average revenue per user, unit economics and cost per transaction, as well as operational indicators like throughput, quality and system reliability. Candidates should be able to choose leading versus lagging indicators for a given question, map operational KPIs to business outcomes, build hypotheses about drivers, recommend measurement changes and define evaluation windows. Measurement and attribution techniques covered include establishing baselines, experimental and quasi experimental designs such as A B tests, control groups, difference in differences and regression adjustments, sample size reasoning, and approaches to isolate confounding factors. Also included are quick back of the envelope estimation techniques for order of magnitude impact, converting technical metrics into business consequences, building dashboards and health metrics to monitor programs, communicating numeric results with confidence bounds, and turning measurement into clear stakeholder facing narratives and recommendations.
Data Informed HR Thinking
Practice scenarios that involve thinking through HR metrics or data: analyzing turnover in a team, understanding the impact of a policy change, or identifying trends. Learn to: (1) Ask what data is available, (2) Understand what the data shows vs. what it doesn't, (3) Consider context and potential causes, (4) Propose data-informed actions. Even at entry level, show that you think about cause and effect, not just isolated incidents. Demonstrate curiosity about metrics and their business meaning.
Metrics and KPI Fundamentals
Core principles and practical fluency for defining, measuring, and interpreting metrics and key performance indicators. 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 such as what counts as a daily active user and monthly active user, calculating common metrics such as engagement rate, churn rate and conversion rates, 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 with justification and action plans.
Human Resources Analytics and Technology
Experience working with human resources technology ecosystems and analytics tools for workforce and people analytics. Topics include using human resources management systems and applicant tracking systems that expose reporting capabilities extracting and transforming HR data in spreadsheets and databases building dashboards and reports in business intelligence tools such as Tableau Power BI and Looker and writing basic database queries for ad hoc analysis. Candidates should be able to discuss data models common HR metrics such as headcount turnover time to hire diversity and inclusion metrics compensation analysis and workforce planning; explain how data is structured stored and accessed in HR systems and describe privacy security and compliance considerations when working with employee data.
Human Resources and Recruitment Analytics
Human Resources and Recruitment Analytics covers applying data analysis, reporting, and measurement to talent acquisition, retention, and workforce planning. Candidates should be able to define and interpret common people metrics such as turnover and attrition rates, time to hire and time to fill, cost per hire, candidate funnel conversion rates, source effectiveness, diversity and inclusion measures, employee engagement and promotion rates. Core skills include extracting and preparing data from human resources information systems and other sources, exploratory and root cause analysis, designing and building operational dashboards and executive scorecards, creating analytical reports for hiring funnels and attrition deep dives, forecasting hiring needs, and measuring recruitment effectiveness and return on investment. It also includes designing recruitment reporting frameworks, running experiments or interventions to improve retention and hiring outcomes, constructing business cases for recruitment investments, and communicating findings and recommendations in clear business terms. Candidates should be mindful of privacy, compliance, and bias mitigation when working with people data and familiar with data visualization and dashboarding workflows and stakeholder-driven reporting practices.
Workforce Analytics and Insights
Focuses on analyzing people and workforce data to reveal patterns, diagnose problems, and recommend actions that improve hiring, retention, engagement, diversity, compensation fairness, and internal mobility. Interviewers will evaluate your ability to work with HR information systems, applicant tracking systems, payroll and compensation data, engagement surveys, performance data, and internal mobility records; define and calculate metrics such as turnover rates, retention drivers, time to hire, hiring funnel drop off points, diversity trends, compensation equity measures, and promotion velocity; apply appropriate statistical and machine learning techniques for correlation, segmentation, regression, predictive attrition modeling, and root cause analysis; design experiments or natural experiment analyses when possible; create clear visualizations and narratives that translate findings into business implications and prioritized interventions for leaders. Candidates should demonstrate rigorous data handling, question framing, analytical methodology, and examples where insights led to concrete workforce or organizational decisions.
Data Driven Recommendations and Impact
Covers the end to end practice of using quantitative and qualitative evidence to identify opportunities, form actionable recommendations, and measure business impact. Topics include problem framing, identifying and instrumenting relevant metrics and key performance indicators, measurement design and diagnostics, experiment design such as A B tests and pilots, and basic causal inference considerations including distinguishing correlation from causation and handling limited or noisy data. Candidates should be able to translate analysis into clear recommendations by quantifying expected impacts and costs, stating key assumptions, presenting trade offs between alternatives, defining success criteria and timelines, and proposing decision rules and go no go criteria. This also covers risk identification and mitigation plans, prioritization frameworks that weigh impact effort and strategic alignment, building dashboards and visualizations to surface signals across HR sales operations and product, communicating concise executive level recommendations with data backed rationale, and designing follow up monitoring to measure adoption and downstream outcomes and iterate on the solution.