Data Science & Analytics Topics
Statistical analysis, data analytics, big data technologies, and data visualization. Covers statistical methods, exploratory analysis, and data storytelling.
Exploratory Data Analysis and Data Quality
Understanding and preparing an unfamiliar dataset before analysis or modeling. Covers systematic profiling through summary statistics, distribution and outlier inspection, and relationships between variables to form initial hypotheses, alongside turning raw data into a trustworthy base: handling missing values, deduplication, outlier treatment, type and consistency checks, and validation. Includes critical thinking about sampling and measurement bias and what a dataset can and cannot support.
Product and User Behavior Analytics
Analyzing how users actually behave in a product, from event data that already exists. Covers cohort construction and cohort analysis, retention curves and how to interpret their shape, engagement, stickiness and activation metrics, behavioral segmentation used as an analytical lens, funnel and conversion interpretation, and the statistical treatment of small or noisy cohorts, including churn and uplift modeling. The scope is reading and interpreting behavioral data, not instrumenting its collection, defining the metrics themselves, diagnosing why a specific metric moved, or attributing conversions to acquisition channels.
Data Storytelling and Insight Communication
Turning analysis into a persuasive, decision-ready narrative for non-technical stakeholders. Covers structuring an insight, translating statistical results into plain language, framing recommendations and trade-offs, and tailoring the message to the audience. Emphasizes driving action, not just presenting numbers.
Causal Inference
Establishing cause-and-effect from observational and experimental data. Covers correlation versus causation, confounding, treatment-effect estimation, and quasi-experimental methods such as difference-in-differences, matching, and instrumental variables. Includes incrementality reasoning when true randomization is not possible.
Statistical Inference and Hypothesis Testing
Reasoning about uncertainty in data and drawing formal conclusions from samples. Covers probability rules and common distributions, the Central Limit Theorem, sampling, standard error, confidence intervals, and Bayesian reasoning, together with the significance-testing framework: null and alternative hypotheses, p-values, statistical power, Type I and Type II errors, effect sizes, and choosing the right test (t-test, chi-square, non-parametric). Emphasizes correctly interpreting statistical results and avoiding common misreadings of significance in business and product contexts rather than memorizing formulas.
Metrics and KPI Design
Defining, selecting, and monitoring the metrics that measure a business or product. Covers north-star and supporting metrics, guardrails, metric decomposition, segmentation, and operational monitoring and alerting. Emphasizes choosing metrics that are actionable and hard to game.
Data Investigation and Root Cause Analysis
Diagnosing why a metric moved. Covers structured drill-down, segmentation to isolate drivers, distinguishing real shifts from noise or data artifacts, and forming and testing explanatory hypotheses. Focuses on the investigative reasoning behind metric-change and anomaly questions.
Estimation and Quantitative Reasoning
Producing defensible numeric estimates with limited data. Covers market sizing, back-of-the-envelope estimation, structuring assumptions, and sanity-checking magnitudes. Emphasizes transparent reasoning and reasonable approximation over false precision.