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.
Python and Pandas for Data Analysis
Programmatic data manipulation and analysis in Python and R. Covers pandas transformations, joins and reshaping, aggregation, working with PySpark for larger data, and using R for statistical analysis. Emphasizes clean, reproducible analytical code.
SQL for Data Analysis
Writing SQL to answer analytical and business questions. Covers filtering, joins, grouping and aggregation, subqueries, CTEs, and translating an ambiguous request into a correct query. Includes spreadsheet-to-SQL fluency for everyday analyst workflows.
BI Tools: Tableau, Power BI, and Looker
Building reporting and self-serve analytics in business-intelligence platforms. Covers data modeling within the tool, calculated fields and measures, interactive dashboards, and platform-specific concepts across Tableau, Power BI, and Looker. Focuses on delivering maintainable, trustworthy reporting.
Working with Large-Scale Datasets
Analyzing data that does not fit comfortably in memory or a single query. Covers distributed processing concepts, partitioning and sampling strategies, query and pipeline performance, and trade-offs when scaling an analysis. Focuses on getting correct answers efficiently at scale.
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.
Forecasting and Time-Series Analysis
Analyzing and projecting data that moves over time. Covers trend and seasonality decomposition, forecasting approaches, demand modeling, and anomaly detection on time series. Emphasizes reasoning about baselines, drivers, and forecast reliability.
Advanced SQL: Metric Monitoring, Anomaly Detection, and Data Correctness at Scale
SQL for monitoring a business or operational metric over time and catching when it is wrong or behaving abnormally, as distinct from the mechanics of the SQL constructs used to compute it (window function syntax, frame semantics, CTEs, and subqueries are covered by the companion topic Advanced SQL: Window Functions, CTEs, and Subqueries), from defining and computing cohort retention, funnel conversion, DAU/WAU/MAU, and lifetime value themselves (covered by the companion topics User Retention & Engagement, Conversion Funnel Optimization, and SQL for Data Analysis), and from sessionization via inactivity-gap detection (also covered by the companion window-functions topic). Covers rolling-window anomaly and change-point detection (z-score and threshold-based, including seasonality-aware baselines), testing whether a period-over-period change in a rate metric is statistically significant versus noise, decomposing a metric's change into which segments drove it, average revenue per user (ARPU) at both a simple and a rolling/at-scale altitude, keeping cumulative and rolling metrics correct against missing dates, late-arriving or out-of-order events, and incremental (not full-recompute) materialization at very large scale, approximate-distinct (HyperLogLog) computation, standalone timezone/daylight-saving-aware day bucketing, Kaplan-Meier-style survival curves computed in pure SQL, and multi-state subscription churn and retention (renewals, expansions, downgrades, and pauses at the plan level, not just customer presence/absence).
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.