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Structured Data for AI
Structured data refers to information that is organized in a predefined format, typically rows and columns, such as relational databases, spreadsheets, or time-series rec...
- Analyze the role of structured data in AI systems to determi...
- Design schemas for AI-ready data that enforce consistency an...
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Pipeline Math
Pipeline math is the application of mathematical concepts—from basic arithmetic to advanced linear algebra—to the design and operation of data pipelines.
- Apply arithmetic and type conversion operations to ensure da...
- Design logical and comparison filters to isolate relevant su...
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Bandit Algorithms for A/B Testing
Bandit algorithms for A/B testing represent a family of adaptive experimentation methods that dynamically allocate traffic to the best-performing variant based on observe...
- Analyze the statistical limitations of classic A/B testing,...
- Design an epsilon-greedy strategy that dynamically adjusts e...
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Sample Size and Effect Size
Sample size and effect size are two fundamental statistical concepts that determine the validity and interpretability of any quantitative study.
- Evaluate the importance of sample size and effect size in en...
- Apply the core concepts of population, sample, and sampling...
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Difference-in-Differences Analysis
Difference-in-Differences (DiD) is a quasi-experimental method used to estimate the causal effect of a treatment or policy by comparing the change in outcomes over time b...
- Analyze causal inference frameworks to determine when differ...
- Implement canonical difference-in-differences regression mod...
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Regression Discontinuity Design
Regression Discontinuity Design (RD) is a quasi-experimental method that identifies causal effects by exploiting a cutoff or threshold that determines treatment assignmen...
- Analyze how the potential outcomes framework addresses selec...
- Design a sharp regression discontinuity design to estimate c...
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Synthetic Control Method
The Synthetic Control Method (SCM) is a statistical technique used to estimate the causal effect of an intervention or treatment on a single unit (such as a country, stat...
- Analyze the potential outcomes framework and counterfactual...
- Design a synthetic control model by selecting appropriate do...
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Propensity Score Matching
Propensity score matching is a statistical technique used to reduce selection bias in observational studies by mimicking randomization.
- Analyze the fundamental problem of causal inference in obser...
- Define the propensity score and describe its balancing prope...
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Experiment Power and Sample Size
Experiment power and sample size is a statistical discipline focused on determining how many observations are needed to detect a true effect of a given magnitude with acc...
- Design a hypothesis test framework that correctly identifies...
- Evaluate the statistical power of a proposed study and inter...
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Data Observability and Anomaly Alerting
Data observability is the practice of continuously monitoring, tracking, and understanding the health and quality of data across its entire lifecycle, from ingestion to c...
- Analyze the foundational principles of data observability to...
- Evaluate the key dimensions of data health, including freshn...
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Semantic Layer and Metric Store
A semantic layer is an abstraction layer that sits between physical data storage (like data warehouses or lakes) and the consumers of data—whether they are BI tools, cust...
- Design a semantic layer architecture that integrates with ex...
- Define metrics with clear naming conventions, semantics, and...
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Ethics in Data Visualization
Ethics in data visualization is the discipline of applying moral principles to the design, creation, and interpretation of graphical representations of data.
- Evaluate ethical implications of data visualization choices...
- Apply core ethical principles to design visualizations that...
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Missing Data and Multiple Imputation
Missing data and multiple imputation constitute a specialized branch of statistical methodology focused on the causes, consequences, and treatment of incomplete observati...
- Analyze the three missing data mechanisms MCAR, MAR, and MNA...
- Evaluate how missing data bias parameter estimates and reduc...
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Survival Analysis and Time-to-Event Models
Survival analysis is a branch of statistics focused on the expected duration of time until one or more events of interest occur, such as death, machine failure, or custom...
- Analyze survival data with censoring and define time-to-even...
- Interpret survival and hazard functions to describe the dist...
- +6 more outcomes
Bayesian Media Mix Modeling
Bayesian Media Mix Modeling (MMM) is a statistical methodology that estimates the contribution of various marketing channels—such as television, digital, print, and socia...
- Analyze the foundational concepts of media mix modeling and...
- Prepare marketing data by handling missing values, outliers,...
- +6 more outcomes