Data Science & Analytics Topics
Statistical analysis, data analytics, big data technologies, and data visualization. Covers statistical methods, exploratory analysis, and data storytelling.
Data Visualization and Dashboard Design
Designing visuals and dashboards that communicate clearly. Covers chart-type selection, encoding choices, dashboard layout and hierarchy, avoiding misleading visuals, and designing for the intended audience and decision. Emphasizes effectiveness over decoration.
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.
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.
Marketing and Growth Analytics
Measuring marketing, acquisition, and revenue performance. Covers multi-touch and marketing-mix attribution, incrementality testing, web and digital analytics, campaign and channel measurement, and privacy-aware mobile measurement. Focuses on connecting spend and behavior to business outcomes.
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.
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.
Optimization and Operations Research Methods
Prescriptive analytics: formulating decisions as optimization problems — linear and integer programming, constraint-based modeling, objective functions, and trade-offs between optimality and tractability. Applied to allocation, scheduling, routing, pricing, and supply/demand problems.