Coming soon
Data Science & Analytics
Find the answer in messy data, and prove it.
The complete course
- modules
- 71
- topics
- 818
- min lessons
- 15
What you'll be able to do.
Wrangle messy data with SQL and Python
Stage 1 · Foundations & Data Wrangling
Test hypotheses and quantify uncertainty
Stage 2 · Statistics & Inference
Build forecasts you can defend
Stage 3 · Modeling & Forecasting
Design A/B tests and read them right
Stage 4 · Causality & Experiments
Diagnose funnels, cohorts and churn
Stage 5 · Product & Business Analytics
Present findings that change decisions
Stage 6 · Decisions & Delivery
Seven stages.One steady climb.
From a messy table to a recommendation someone signs off on.
71modules
in 7 stages
818topics
≈ 12 per module
≈205hours
of 15-minute lessons
≈2years
≈13months
≈7months
to finish the whole course
- 1
Foundations & Data Wrangling
Messy data, made trustworthy
15 modules · 175 topics · ≈ 44 hr
- 2
Statistics & Inference
What the numbers can and can't say
13 modules · 151 topics · ≈ 38 hr
- 3
Modeling & Forecasting
Models that explain and predict
10 modules · 118 topics · ≈ 30 hr
- 4
Causality & Experiments
Cause, not just correlation
7 modules · 82 topics · ≈ 21 hr
- 5
Product & Business Analytics
The numbers a business runs on
11 modules · 128 topics · ≈ 32 hr
- 6
Decisions & Delivery
From analysis to a decision
10 modules · 112 topics · ≈ 28 hr
- 7
Practicum & Capstone
One decision, argued with data
5 modules · 52 topics · ≈ 13 hr
Months
0612180369024
MonthsFinish · ≈ 2 years · ≈ 13 months · ≈ 7 months
Hours and months are estimates: one 15-minute lesson per topic, every day.
Every module.Every topic.
Stage 1
Foundations & Data Wrangling
Messy data, made trustworthy
15 modules · 175 topics
1Foundations of Data Science and Analytics10 topics
- Data Science, Analytics, and Business Intelligence
- Descriptive, Diagnostic, Predictive, and Prescriptive Questions
- Statistical Inference and Causal Inference
- Data as Evidence About a Larger Process
- From Raw Observations to Decisions
- The Analytical Project Lifecycle
- Domain Knowledge and Analytical Judgment
- Roles of Analysts, Data Scientists, and Data Engineers
- Reproducibility, Transparency, and Responsible Interpretation
- Recognizing What Available Data Cannot Answer
2Analytical Problem Framing11 topics
- Translating a Business Question into an Analytical Question
- Defining the Decision an Analysis Will Inform
- Units of Observation and Units of Analysis
- Target Populations and Relevant Time Horizons
- Outcomes, Exposures, Treatments, and Covariates
- Estimands and Precisely Defined Quantities of Interest
- Hypotheses, Assumptions, and Competing Explanations
- Baselines, Comparators, and Success Criteria
- Data Availability and Feasibility Assessment
- Stakeholder Alignment and Scope Boundaries
- Choosing an Analytical Deliverable for the Decision
3Measurement and Data-Generating Processes12 topics
- How Data Are Created, Recorded, and Selected
- Constructs, Proxies, and Operational Definitions
- Nominal, Ordinal, Interval, and Ratio Scales
- Continuous, Discrete, Binary, and Count Variables
- Measurement Validity and Reliability
- Measurement Error and Instrument Limitations
- Observation Windows and Exposure Time
- Units, Denominators, and Population at Risk
- Selection Mechanisms and Unobserved Cases
- Administrative Data and Designed Research Data
- Changes in Measurement Across Time and Groups
- Distinguishing Real-World Change from Recording Change
4Quantitative Foundations for Data Analysis12 topics
- Ratios, Rates, Proportions, and Percentages
- Percentage Change and Percentage-Point Change
- Weighted Averages and Aggregation Rules
- Logarithms, Exponentials, and Growth Rates
- Functions and Transformations of Variables
- Vectors, Matrices, and Tabular Representations
- Dot Products and Linear Combinations
- Derivatives as Rates of Change
- Optimization Objectives and Loss Functions
- Numerical Precision and Rounding Effects
- Dimensional Consistency and Unit Conversion
- Order-of-Magnitude Checks and Plausibility Tests
5Spreadsheet Analytics and Analytical Prototyping12 topics
- Structured Tables and Consistent Column Types
- Relative, Absolute, and Structured References
- Conditional Aggregation and Lookup Functions
- Pivot Tables and Grouped Summaries
- Array Calculations and Reusable Formulas
- Dates, Number Formats, and Hidden Type Errors
- Formula Auditing and Error Propagation
- Data Validation and Protected Input Areas
- Scenario Tables and What-If Models
- Reconciling Spreadsheet Totals with Source Records
- Documenting Manual Steps and Workbook Assumptions
- Choosing Between Spreadsheet, SQL, and Code-Based Workflows
6Analytical Programming with Python and R13 topics
- Notebooks, Scripts, and Interactive Analysis
- Variables, Data Types, and Collections
- Arrays, Series, Data Frames, and Tables
- NumPy, pandas, and R Data Analysis Workflows
- Indexing, Filtering, and Boolean Logic
- Vectorized Operations and Elementwise Calculations
- Functions for Reusable Analytical Steps
- Iteration and Simulation Workflows
- Dates, Strings, and Categorical Data
- Missing Values and Type Conversion
- Reading Errors and Debugging Analytical Code
- Assertions and Checks on Intermediate Results
- Choosing and Documenting an Analysis Environment
7Tabular Data Transformation12 topics
- Tidy Data and Consistent Table Structure
- Selecting, Renaming, and Reordering Columns
- Filtering Records and Deriving Variables
- Grouping and Aggregating Observations
- Reshaping Between Wide and Long Formats
- Joining Tables and Matching Keys
- Join Cardinality and Row Multiplication
- Concatenation, Unions, and Schema Alignment
- Sorting, Ranking, and Within-Group Operations
- Text Parsing and Structured Field Extraction
- Time-Based and Nearest-Match Joins
- Validating Transformations with Reconciliation Checks
8SQL Foundations for Analytics11 topics
- Tables, Rows, Columns, and Relational Keys
- SELECT, WHERE, and ORDER BY
- Expressions, CASE Statements, and Derived Fields
- NULL Values and Three-Valued Logic
- COUNT, SUM, AVG, and Other Aggregations
- GROUP BY and HAVING
- Inner, Left, Full, and Cross Joins
- Subqueries and Common Table Expressions
- DISTINCT, Duplicates, and Counting Units
- Date, Time, and Interval Calculations
- Translating an Analytical Question into a Query
9Advanced Analytical SQL14 topics
- Window Functions and Partitioned Calculations
- ROW_NUMBER, RANK, and Dense Ranking
- LAG, LEAD, and Event Sequences
- Running Totals and Rolling Windows
- Window Frames and Boundary Semantics
- Conditional Aggregation and Pivoted Summaries
- Querying Nested and Semistructured Data
- Recursive Queries for Hierarchical Relationships
- Sessionization and Time-Gap Logic
- Cohort Tables and Retention Queries
- Funnel Steps and Ordered Event Matching
- As-Of Logic and Historical State Reconstruction
- Query Plans and Expensive Join Patterns
- Verifying Complex Queries Against Small Known Examples
10Data Acquisition and Integration12 topics
- Files, Databases, APIs, and Event Streams as Data Sources
- CSV, Spreadsheet, JSON, and Columnar Data Formats
- Encodings, Delimiters, and Schema Detection
- API Pagination, Rate Limits, and Incremental Retrieval
- Public Data Portals and Source Documentation
- Permission, Licensing, and Appropriate Data Reuse
- Identifiers and Cross-Source Record Matching
- Entity Resolution and Ambiguous Matches
- Timestamp Alignment and Timezone Conversion
- Source Reconciliation and Conflicting Values
- Preserving Raw Inputs and Acquisition Metadata
- Assessing Coverage Before Combining Datasets
11Analytical Data Modeling11 topics
- Transactional and Analytical Data Structures
- Table Grain and the Meaning of One Row
- Fact Tables and Dimension Tables
- Star Schemas and Analytical Relationships
- Event Facts, Snapshots, and Accumulating Records
- Slowly Changing Dimensions and Historical Attributes
- Additive, Semiadditive, and Nonadditive Measures
- Many-to-Many Relationships and Bridge Tables
- Semantic Layers and Shared Metric Definitions
- Derived Tables and Reusable Analytical Datasets
- Preventing Double Counting Across Data Models
12Data Cleaning and Standardization11 topics
- Profiling Types, Ranges, and Value Frequencies
- Parsing Numbers, Dates, and Structured Strings
- Standardizing Units, Categories, and Labels
- Duplicate Records and Duplicate Entities
- Impossible Values and Logical Contradictions
- Outliers, Rare Events, and Data Entry Errors
- Text Normalization and Identifier Cleanup
- Resolving Conflicting Records
- Handling Truncated, Rounded, and Censored Measurements
- Documenting Cleaning Decisions and Their Consequences
- Comparing Results Before and After Cleaning
13Missing Data and Measurement Error11 topics
- Missing Completely at Random, at Random, and Not at Random
- Missingness Patterns and Plausible Mechanisms
- Complete-Case Analysis and Its Assumptions
- Simple Imputation and Distorted Uncertainty
- Multiple Imputation Principles
- Missingness Indicators and Their Interpretation
- Attrition, Dropout, and Lost Follow-Up
- Measurement Error in Outcomes and Predictors
- Misclassification of Categorical Variables
- Sensitivity Analysis for Unobserved Values
- Distinguishing Absence of an Event from Absence of a Record
14Data Quality and Lineage11 topics
- Accuracy, Completeness, Consistency, and Timeliness
- Uniqueness, Validity, and Referential Integrity
- Data Contracts and Expected Schemas
- Field-Level and Dataset-Level Quality Checks
- Freshness, Volume, and Distribution Monitoring
- Reconciliation with Source Systems
- Lineage from Raw Data to Published Metrics
- Quality Ownership and Issue Escalation
- Schema Changes and Broken Assumptions
- Quantifying the Analytical Impact of Quality Problems
- Recording Known Limitations in Dataset Documentation
15Reproducible Analytical Workflows12 topics
- Organizing Data, Code, Outputs, and Documentation
- Separating Raw, Intermediate, and Final Datasets
- Version Control for Analytical Work
- Environment and Dependency Reproducibility
- Random Seeds and Sources of Nondeterminism
- Parameterized Analyses and Repeatable Reports
- Executable Notebooks and Hidden State
- Lightweight Tests for Analytical Assumptions
- Peer Review of Queries, Code, and Conclusions
- Verifying AI-Assisted Code and Statistical Suggestions
- Recording Decisions, Exclusions, and Deviations
- Reproducing an Analysis from Its Documented Inputs
Stage 2
Statistics & Inference
What the numbers can and can't say
13 modules · 151 topics
16Descriptive Statistics12 topics
- Counts, Frequencies, and Relative Frequencies
- Means, Medians, Modes, and Trimmed Means
- Variance, Standard Deviation, and Interquartile Range
- Quantiles, Percentiles, and Distribution Tails
- Skewness, Heavy Tails, and Multimodality
- Weighted Descriptive Statistics
- Rates with Unequal Exposure or Population Size
- Covariance and Correlation
- Cross-Tabulations and Conditional Summaries
- Aggregation Bias and Simpson's Paradox
- Robust Summaries for Skewed and Contaminated Data
- Choosing Summaries That Match the Analytical Question
17Exploratory Data Analysis11 topics
- Establishing Dataset Structure and Coverage
- Exploring Individual Variable Distributions
- Examining Relationships Between Variables
- Comparing Groups and Subpopulations
- Detecting Anomalies and Unexpected Patterns
- Investigating Temporal and Spatial Variation
- Exploring Missingness and Data Collection Artifacts
- Testing Initial Explanations Against Alternative Views
- Separating Exploration from Confirmation
- Recording Hypotheses Generated During Exploration
- Identifying the Next Data or Analysis Needed
18Data Visualization Principles12 topics
- Matching Chart Types to Analytical Tasks
- Visual Encodings and Perceptual Accuracy
- Distributions, Comparisons, Relationships, and Trends
- Scales, Axes, Baselines, and Transformations
- Small Multiples and Consistent Comparisons
- Visualizing Uncertainty and Sample Size
- Overplotting, Binning, and Density Displays
- Color, Contrast, and Accessible Design
- Annotations and Contextual Reference Lines
- Interactive Filtering and Linked Views
- Recognizing Misleading and Overdecorated Charts
- Choosing Tables When Exact Values Matter
19Probability and Conditional Reasoning11 topics
- Events, Sample Spaces, and Probability Rules
- Conditional Probability and Independence
- Joint and Marginal Probabilities
- Bayes' Theorem and Updating Beliefs
- Base Rates and Inverse Probability Errors
- Independence and Conditional Independence
- Counting Rules and Combinatorial Reasoning
- Expected Value and Long-Run Averages
- Law of Total Probability and Total Expectation
- Translating Verbal Uncertainty into Probability Models
- Simulating Probability Problems to Check Intuition
20Random Variables and Probability Models12 topics
- Discrete and Continuous Random Variables
- Probability Mass, Density, and Cumulative Distribution Functions
- Expectation, Variance, and Covariance
- Bernoulli and Binomial Models
- Poisson and Count Models
- Normal and Lognormal Models
- Exponential and Waiting-Time Models
- Heavy-Tailed and Mixture Distributions
- Joint Distributions and Dependence
- Transformations of Random Variables
- Laws of Large Numbers and the Central Limit Theorem
- Matching Distributional Assumptions to Observed Data
21Sampling and Study Design12 topics
- Populations, Sampling Frames, and Samples
- Probability and Nonprobability Sampling
- Simple Random and Systematic Sampling
- Stratified and Cluster Sampling
- Multistage Sampling Designs
- Convenience Samples and Coverage Bias
- Selection Bias and Survivorship Bias
- Cross-Sectional, Longitudinal, and Repeated Cross-Sectional Studies
- Independent Observations and Clustered Observations
- Sampling Error and Nonsampling Error
- Generalizability and Target Population Alignment
- Designing Data Collection Around the Intended Inference
22Survey Analytics and Weighting11 topics
- Questionnaire Design and Measurement Consistency
- Question Wording, Ordering, and Response Options
- Response Bias and Social Desirability
- Unit Nonresponse and Item Nonresponse
- Sampling Weights and Unequal Selection Probabilities
- Nonresponse Adjustments and Calibration
- Poststratification and Raking
- Design Effects and Effective Sample Size
- Weighted Estimation and Uncertainty
- Comparing Survey Responses with Behavioral Records
- Reporting Representation and Remaining Bias
23Estimation and Uncertainty11 topics
- Parameters, Estimators, and Estimates
- Bias, Variance, and Mean Squared Error
- Sampling Distributions and Standard Errors
- Point Estimates and Interval Estimates
- Confidence Intervals and Their Interpretation
- Estimation for Means, Proportions, Rates, and Differences
- Maximum Likelihood as an Estimation Framework
- Uncertainty in Ratios and Nonlinear Quantities
- Prediction Intervals and Confidence Intervals
- Finite-Sample and Asymptotic Reasoning
- Communicating Uncertainty Without Hiding the Main Result
24Resampling and Computational Inference11 topics
- Simulation as a Tool for Statistical Reasoning
- Bootstrap Resampling and Sampling Variability
- Bootstrap Confidence Intervals
- Permutation Tests and Exchangeability
- Randomization Tests and Assignment Mechanisms
- Resampling Paired and Clustered Observations
- Block Resampling for Dependent Temporal Data
- Monte Carlo Error and Simulation Precision
- Parametric and Nonparametric Resampling
- Situations Where Naive Resampling Fails
- Checking Analytical Results with Computational Experiments
25Hypothesis Testing14 topics
- Null and Alternative Hypotheses
- Test Statistics and Reference Distributions
- P-Values and Their Interpretation Limits
- Type I and Type II Errors
- One-Sided and Two-Sided Tests
- Tests for Means, Proportions, and Group Differences
- Paired and Independent-Sample Comparisons
- Analysis of Variance and Planned Contrasts
- Contingency Tables and Categorical Association Tests
- Small-Sample and Exact Tests
- Rank-Based and Distribution-Free Methods
- Statistical Significance and Practical Importance
- Equivalence and Noninferiority Questions
- Reporting Effect Sizes Alongside Test Results
26Statistical Power and Sample Size Planning10 topics
- Power as a Property of a Specified Study Design
- Minimum Detectable Effects and Decision-Relevant Effects
- Baseline Rates and Outcome Variability
- Sample Size for Means, Proportions, and Differences
- Allocation Ratios and Unequal Group Sizes
- Clustering and Repeated Measurements in Power Calculations
- Attrition and Incomplete Outcome Observation
- Duration Planning for Seasonal or Delayed Outcomes
- Simulation-Based Power Analysis
- Interpreting Inconclusive Results Without Post Hoc Power Claims
27Multiple Testing and Reliable Statistical Evidence12 topics
- Families of Hypotheses and Multiplicity
- Family-Wise Error and False Discovery Rate
- Adjustment Methods and Their Assumptions
- Exploratory and Confirmatory Analysis Plans
- Selective Reporting and Researcher Degrees of Freedom
- P-Hacking and Repeated Unplanned Analyses
- Winner's Curse and Effect Size Inflation
- Replication and Independent Confirmation
- Sensitivity to Analytical Choices
- Multiverse and Specification-Curve Reasoning
- Preregistration and Transparent Deviations
- Evaluating Evidence Beyond a Significance Threshold
28Bayesian Analysis12 topics
- Prior, Likelihood, and Posterior Distributions
- Bayesian Updating for Simple Data Models
- Weakly Informative and Domain-Informed Priors
- Prior Predictive Checks
- Posterior Summaries and Credible Intervals
- Posterior Predictive Distributions
- Bayesian Estimation of Rates and Group Differences
- Hierarchical Priors and Shrinkage
- Computational Posterior Approximation
- Convergence and Effective Sample Size Concepts
- Prior Sensitivity and Model Criticism
- Posterior Probabilities in Decision Contexts
Stage 3
Modeling & Forecasting
Models that explain and predict
10 modules · 118 topics
29Linear Regression and Explanatory Models12 topics
- Simple and Multiple Linear Regression
- Least Squares and Fitted Relationships
- Coefficient Interpretation and Measurement Units
- Categorical Predictors and Reference Groups
- Interactions and Conditional Associations
- Transformations of Outcomes and Predictors
- Residual Variation and Explained Variation
- Standard Errors and Coefficient Intervals
- Conditional Mean and Exogeneity Assumptions
- Association, Adjustment, and Causal Interpretation
- Prediction for New Observations
- Communicating Regression Results in Practical Terms
30Generalized Linear Models12 topics
- Outcome Distributions and Link Functions
- Logistic Regression for Binary Outcomes
- Odds, Odds Ratios, and Predicted Probabilities
- Marginal Effects and Standardized Predictions
- Multinomial and Ordinal Outcome Models
- Poisson Regression for Counts
- Offsets and Unequal Exposure
- Overdispersion and Negative Binomial Models
- Positive Skewed Outcomes and Gamma Models
- Excess Zeros and Two-Part Outcome Processes
- Likelihood-Based Estimation and Model Comparison
- Matching the Model to the Data-Generating Process
31Regression Diagnostics and Robust Analysis12 topics
- Residual Patterns and Model Misspecification
- Heteroskedasticity and Robust Standard Errors
- Multicollinearity and Unstable Coefficients
- Leverage, Influence, and Unusual Observations
- Nonlinear Relationships and Flexible Functional Forms
- Splines and Generalized Additive Model Concepts
- Robust Regression and Sensitivity to Outliers
- Quantile Regression and Distributional Effects
- Regularization as a Tool for Stability
- Model Selection and Post-Selection Uncertainty
- Extrapolation and Unsupported Predictions
- Comparing Conclusions Across Reasonable Model Specifications
32Hierarchical, Longitudinal, and Panel Data11 topics
- Repeated Observations and Within-Unit Dependence
- Nested and Crossed Data Structures
- Fixed Effects and Unit-Specific Baselines
- Random Intercepts and Random Slopes
- Partial Pooling Across Groups
- Within-Group and Between-Group Associations
- Cluster-Robust Inference
- Generalized Estimating Equation Concepts
- Time-Varying Covariates and Dynamic Outcomes
- Unequal Follow-Up and Unbalanced Panels
- Choosing Models for Conditional and Population-Average Questions
33Predictive Analytics and Model Validation13 topics
- Prediction Targets and Decision Context
- Baseline Models and Benchmark Performance
- Training, Validation, and Test Data Roles
- Cross-Validation and Appropriate Data Splits
- Group-Based and Time-Based Validation
- Target Leakage and Information Available at Prediction Time
- Regression Error Metrics and Asymmetric Costs
- Classification Metrics and Class Imbalance
- Probability Calibration and Decision Thresholds
- Performance Variation Across Subpopulations
- Predictive Uncertainty and Distribution Shift
- Explaining Predictions Without Inferring Causation
- Comparing Model Utility with a Simple Decision Baseline
34Multivariate Analysis and Data Segmentation11 topics
- Correlation Structure Across Multiple Variables
- Standardization and Scale-Sensitive Comparisons
- Principal Components as Low-Dimensional Summaries
- Explained Variance and Component Interpretation
- Factor Analysis and Latent Construct Assumptions
- Distances and Similarity Measures
- Clustering as an Exploratory Summary
- Choosing and Evaluating Segment Granularity
- Cluster Stability and Sensitivity to Preprocessing
- Profiling Segments with Independent Variables
- Distinguishing Discovered Groups from Natural Categories
35Time-Series Data and Temporal Structure11 topics
- Time Indexes, Frequencies, and Observation Intervals
- Trends, Seasonality, Cycles, and Irregular Variation
- Calendar Effects and Timezone Boundaries
- Autocorrelation and Partial Autocorrelation
- Stationarity and Structural Change
- Differencing and Seasonal Adjustment
- Decomposition of Temporal Patterns
- Lagged Variables and Time Alignment
- Irregular Sampling and Temporal Aggregation
- Missing Periods and Revised Observations
- Distinguishing Leading Indicators from Lagged Correlations
36Statistical Forecasting12 topics
- Forecast Horizons and Forecasting Objectives
- Naive, Seasonal Naive, and Drift Baselines
- Moving Averages and Exponential Smoothing
- Trend and Seasonal Exponential Smoothing Models
- Autoregressive and Moving-Average Components
- Differencing and ARIMA Models
- Seasonal ARIMA and Repeated Temporal Patterns
- Dynamic Regression with External Predictors
- Predictor Availability at the Forecast Origin
- Prediction Intervals and Forecast Distributions
- Combining Forecasts and Incorporating Structured Judgment
- Handling Structural Breaks and Regime Changes
37Forecast Evaluation and Demand Planning12 topics
- Rolling-Origin and Expanding-Window Evaluation
- Horizon-Specific Forecast Accuracy
- MAE, RMSE, and Scaled Error Measures
- Percentage Error Metrics and Zero-Value Problems
- Forecast Bias and Systematic Underprediction
- Interval Coverage and Probabilistic Forecast Evaluation
- Hierarchical and Grouped Forecast Reconciliation
- Intermittent Demand and Sparse Outcomes
- Demand Censoring and Stockout Distortion
- Scenario Forecasts and Conditional Assumptions
- Forecasts, Targets, and Capacity Constraints
- Translating Forecast Error into Decision Consequences
38Survival and Event-History Analysis12 topics
- Time-to-Event Outcomes and Observation Windows
- Right, Left, and Interval Censoring
- Truncation and Delayed Entry
- Survival, Hazard, and Cumulative Hazard Functions
- Kaplan-Meier Estimation
- Comparing Survival Curves
- Cox Proportional Hazards Models
- Proportional Hazards Assumptions and Diagnostics
- Accelerated Failure Time Models
- Competing Risks and Cumulative Incidence
- Restricted Mean Survival Time
- Event Recurrence and Time-Varying Exposures
Stage 4
Causality & Experiments
Cause, not just correlation
7 modules · 82 topics
39Foundations of Causal Inference12 topics
- Counterfactual Questions and Potential Outcomes
- Average and Conditional Treatment Effects
- Consistency and Well-Defined Interventions
- Exchangeability and Confounding
- Positivity and Treatment Overlap
- Causal Diagrams and Directed Acyclic Graphs
- Confounders, Mediators, and Colliders
- Backdoor Paths and Adjustment Sets
- Selection Bias and Conditioning on Post-Treatment Variables
- Identification Versus Estimation
- Interference and Spillover Effects
- Generalization and Transport to New Populations
40Causal Inference from Observational Data12 topics
- Emulating a Target Trial with Observational Records
- Defining Eligibility, Treatment, Follow-Up, and Outcomes
- Outcome Regression and Standardization
- Propensity Scores and Their Interpretation
- Matching and Covariate Balance
- Inverse Probability Weighting
- Extreme Weights and Limited Overlap
- Doubly Robust Estimation Concepts
- Time-Varying Treatments and Confounding
- Negative Controls and Falsification Checks
- Sensitivity to Unmeasured Confounding
- Reporting Assumptions That Data Alone Cannot Verify
41Natural Experiments and Identification Strategies11 topics
- Natural Experiments and Plausibly Exogenous Variation
- Instrumental Variables and the Assignment Mechanism
- Relevance, Independence, and Exclusion Restrictions
- Local Average Treatment Effects and Monotonicity
- Weak Instruments and Unstable Estimates
- Regression Discontinuity Around Assignment Thresholds
- Sharp and Fuzzy Discontinuity Designs
- Bandwidth Selection and Local Comparisons
- Manipulation, Sorting, and Continuity Checks
- Placebo Thresholds and Alternative Explanations
- Limits of Generalizing Local Causal Effects
42Panel Data Methods for Causal Analysis11 topics
- Before-After Comparisons and Their Limitations
- Difference-in-Differences Design
- Parallel Trends and the Untreated Counterfactual
- Event Studies and Dynamic Treatment Effects
- Anticipation and Treatment Timing
- Staggered Adoption and Heterogeneous Effects
- Serial Correlation and Appropriate Inference
- Synthetic Control Design and Donor Selection
- Pre-Treatment Fit and Placebo Comparisons
- Interrupted Time Series and Concurrent Changes
- Sensitivity to Comparison Groups and Time Windows
43Randomized Experimental Design12 topics
- Random Assignment and Causal Identification
- Experimental Units and Analysis Units
- Control Groups and Treatment Conditions
- Blocking, Stratification, and Matched Designs
- Allocation Concealment and Blinding Concepts
- Primary Outcomes and Predefined Analysis Plans
- Noncompliance and Intention-to-Treat Effects
- Attrition and Missing Outcomes in Experiments
- Balance Checks and Interpretation Limits
- Experiment Duration and Delayed Effects
- Ethical Constraints and Participant Impacts
- Connecting Experimental Results to the Target Decision
44Online Controlled Experiments12 topics
- A/B Testing in Digital Products
- Eligibility, Assignment, Exposure, and Analysis Populations
- Stable Assignment and Cross-Device Identity
- A/A Tests and Instrumentation Validation
- Sample Ratio Mismatch and Allocation Failures
- Primary Metrics, Guardrails, and Diagnostic Metrics
- Triggered Analyses and Exposure Bias
- Novelty, Learning, and Carryover Effects
- Bots, Internal Users, and Invalid Traffic
- Attribution Windows and Delayed Outcomes
- Experiment Interactions and Concurrent Changes
- Interpreting and Communicating a Product Experiment
45Advanced Experimentation and Treatment Heterogeneity12 topics
- Factorial Designs and Interaction Effects
- Multivariant Experiments and Comparison Families
- Cluster Randomization and Group-Level Outcomes
- Switchback Designs and Time-Based Assignment
- Network Effects and Interference-Aware Experiments
- Sequential Testing and Planned Monitoring
- Alpha Spending and Repeated-Look Error Control
- Bayesian Monitoring and Decision Rules
- CUPED and Pre-Experiment Covariate Adjustment
- Heterogeneous Treatment Effects and Subgroup Credibility
- Adaptive Allocation and Inference Tradeoffs
- Long-Term Holdouts and Persistent Effects
Stage 5
Product & Business Analytics
The numbers a business runs on
11 modules · 128 topics
46Metric Design and Metric Systems12 topics
- Business Objectives and Operational Metrics
- North Star Metrics and Supporting Measures
- Metric Trees and Driver Decomposition
- Leading, Lagging, and Guardrail Metrics
- Numerator, Denominator, and Eligibility Definitions
- Users, Accounts, Sessions, Events, and Transactions
- Ratios of Totals and Averages of Ratios
- Aggregation Windows and Timezone Policies
- Metric Sensitivity and Responsiveness
- Goodhart's Law and Metric Gaming
- Ownership, Versioning, and Metric Change Governance
- Distinguishing a Useful Proxy from the Desired Outcome
47Event Instrumentation and Behavioral Data12 topics
- Tracking Plans and Event Taxonomies
- Event Names, Properties, and Schema Contracts
- Client-Side and Server-Side Measurement
- Event Time, Processing Time, and Late Arrival
- Identity Resolution and Anonymous-to-Known Transitions
- Deduplication, Retries, and Idempotent Event Handling
- Session Boundaries and Activity Windows
- Consent, Blocking, and Unobserved Behavior
- Attribution Metadata and Source Context
- Instrumentation QA and End-to-End Reconciliation
- Tracking Changes Across Application Releases
- Identifying Measurement Gaps Before Interpreting Behavior
48Product and Engagement Analytics11 topics
- Acquisition, Activation, Engagement, and Value Delivery
- Defining Meaningful Active Use
- Feature Adoption and Depth of Use
- Frequency, Recency, and Engagement Intensity
- Active User Ratios and Their Limitations
- User Journeys and Behavioral Sequences
- Exposure Opportunity and Actual Feature Usage
- Account-Level and User-Level Product Analysis
- Segment Mix and Apparent Engagement Changes
- Diagnosing Product Friction with Behavioral Evidence
- Linking Product Metrics to User and Business Outcomes
49Funnel and Conversion Analysis11 topics
- Funnel Steps and Eligible Starting Populations
- Open and Closed Funnel Definitions
- User-Level, Session-Level, and Event-Level Conversion
- Ordered and Unordered Step Completion
- Conversion Windows and Delayed Completion
- Repeated Attempts and Deduplicated Outcomes
- Step-Level Drop-Off and Time Between Steps
- Segment Comparisons with Consistent Denominators
- Cross-Device and Cross-Channel Journey Gaps
- Separating Traffic Mix Changes from Conversion Changes
- Testing Explanations for Funnel Bottlenecks
50Cohort, Retention, and Churn Analysis12 topics
- Acquisition, Activation, and Behavioral Cohorts
- Calendar Time and Time Since Cohort Entry
- Fixed-Period, Rolling, and Return-Window Retention
- Cohort Maturity and Incomplete Observation
- Retention Curves and Retention Matrices
- Churn Definitions and Inactivity Thresholds
- Reactivation and Resurrected Users
- Customer Churn and Revenue Churn
- Survival-Based Interpretation of Retention
- Comparing Cohorts Under Changing Product and Channel Mix
- Leading Signals and Causal Claims About Churn
- Long-Term Retention with Limited Follow-Up
51Customer, Revenue, and Unit Economics Analytics13 topics
- Customer, Account, Subscription, and Transaction Units
- Revenue Definitions and Recognition Timing
- Bookings, Billings, Cash Receipts, and Reported Revenue
- Recurring and Nonrecurring Revenue
- Gross and Net Revenue Retention
- Revenue per User and Revenue per Paying Customer
- Discounts, Refunds, and Failed Payments
- Contribution Margin and Cost Allocation Assumptions
- Customer Acquisition Cost and Attribution Scope
- Customer Lifetime Value and Observation Horizon
- Payback Period and Cohort Economics
- Discounting, Uncertainty, and Scenario-Based Value Estimates
- Avoiding Misleading Averages Across Customer Segments
52Marketing Measurement and Incrementality12 topics
- Campaign Taxonomies and Acquisition Source Definitions
- Reach, Frequency, Response, and Conversion Metrics
- Attribution Windows and Touchpoint Visibility
- First-Touch, Last-Touch, and Multitouch Attribution
- Attribution Credit and Incremental Impact
- Selection Bias in Channel Comparisons
- Holdout Tests and Conversion Lift Studies
- Geographic Experiments and Market-Level Comparisons
- Marketing Mix Modeling and Identification Limits
- Carryover, Saturation, and Channel Interactions
- Organic and Paid Activity Overlap
- Evaluating Measurement Under Incomplete Tracking
53Operational and Process Analytics11 topics
- Process Events, Cases, and Activity Sequences
- Throughput, Cycle Time, and Waiting Time
- Capacity, Utilization, and Bottlenecks
- Queueing Concepts and Little's Law
- Service Levels and Tail Performance
- Demand Variability and Workload Planning
- Process Mining and Deviations from Expected Flows
- Rework, Error Rates, and First-Pass Outcomes
- Resource Productivity and Case-Mix Adjustment
- Operational Changes and Causal Evaluation
- Turning Process Findings into Testable Improvement Proposals
54Geospatial Analytics11 topics
- Coordinates, Geometries, and Spatial Data Types
- Coordinate Reference Systems and Projections
- Geocoding and Location Uncertainty
- Spatial Joins and Geographic Aggregation
- Distances, Areas, and Neighborhood Definitions
- Spatial Autocorrelation and Dependence
- Hotspots and Local Concentrations
- Rates, Population Denominators, and Exposure
- Modifiable Areal Units and Ecological Fallacies
- Spatial Sampling and Uneven Data Coverage
- Communicating Geographic Patterns Without Overclaiming Causes
55Text and Qualitative Data Analytics12 topics
- Text as Unstructured Analytical Evidence
- Document Units and Corpus Construction
- Text Cleaning and Language Identification
- Word, Phrase, and Document Frequency Summaries
- Search Terms, Dictionaries, and Coding Rules
- Human Coding and Annotation Guidelines
- Inter-Rater Agreement and Label Quality
- Sentiment Measures and Contextual Limitations
- Topic Summaries and Independent Validation
- Linking Qualitative Themes to Quantitative Records
- Representative Quotations and Selective Evidence Risk
- Evaluating Automated Text Outputs Against Human Review
56Graph and Network Analytics11 topics
- Nodes, Edges, and Relationship Definitions
- Directed, Undirected, and Weighted Networks
- Bipartite and Multilayer Data Structures
- Degree, Connectivity, and Component Structure
- Centrality Measures and Their Interpretation
- Communities and Group Structure
- Paths, Reachability, and Network Distance
- Temporal Networks and Relationship Change
- Sampling Bias and Missing Edges
- Distinguishing Network Association from Social Influence
- Visualizing Networks Without Obscuring Their Structure
Stage 6
Decisions & Delivery
From analysis to a decision
10 modules · 112 topics
57Anomaly, Change, and Metric Monitoring11 topics
- Baseline Behavior and Expected Variation
- Point Anomalies and Contextual Anomalies
- Control Charts and Stable Process Assumptions
- Seasonality-Aware Monitoring
- Change Points and Structural Shifts
- Threshold Selection and Alert Burden
- Base Rates and False Alarm Interpretation
- Multiple Metrics and Repeated Monitoring
- Decomposing an Unexpected Metric Movement
- Distinguishing Data Failures from Business Changes
- Confirming a Signal Before Recommending Action
58Simulation and Scenario Analysis11 topics
- Defining a System and Its Decision Variables
- Deterministic and Stochastic Scenarios
- Monte Carlo Simulation of Uncertain Outcomes
- Choosing Distributions for Uncertain Inputs
- Correlated Inputs and Joint Scenarios
- Discrete-Event Simulation Concepts
- Queueing and Capacity Simulation
- Sensitivity Analysis and Dominant Assumptions
- Stress Scenarios and Tail Outcomes
- Calibration and Validation Against Observed Behavior
- Reporting Simulation Uncertainty and Model Limitations
59Optimization and Prescriptive Analytics11 topics
- Decision Variables, Objectives, and Constraints
- Linear Programming Formulations
- Integer and Mixed-Integer Decision Models
- Resource Allocation and Scheduling Problems
- Assignment, Routing, and Network Flow Concepts
- Constraint Feasibility and Conflicting Requirements
- Sensitivity, Shadow Prices, and Marginal Value
- Multiple Objectives and Pareto Tradeoffs
- Stochastic and Robust Optimization Concepts
- Validating Optimized Decisions Under Real-World Conditions
- Recognizing When Model Assumptions Dominate the Recommendation
60Decision Analysis and Value of Information11 topics
- Decisions, States of the World, and Consequences
- Expected Value and Expected Utility
- Decision Trees and Probabilistic Outcomes
- Asymmetric Costs and Decision Thresholds
- Risk Preferences and Downside Constraints
- Regret and Robust Decision Alternatives
- Value of Perfect and Partial Information
- Whether Additional Data Are Worth Collecting
- Combining Causal Effects with Costs and Constraints
- Sensitivity of the Preferred Decision to Assumptions
- Communicating a Recommendation Under Uncertainty
61Business Intelligence and Dashboard Design12 topics
- Executive, Analytical, and Operational Dashboards
- Audience Needs and Decision Frequency
- Metric Hierarchies and Information Priorities
- Consistent Definitions Across Reports
- Filters, Drill-Downs, and Interaction Design
- Time Comparisons and Reference Periods
- Alerts, Exceptions, and Actionable Views
- Displaying Uncertainty and Data Freshness
- Avoiding Misleading Cross-Filter Calculations
- Dashboard Performance and Usability
- Testing Whether a Dashboard Supports Its Intended Decisions
- Retirement of Unused and Redundant Reports
62Analytical Communication and Data Storytelling12 topics
- Leading with the Question and Main Finding
- Separating Evidence, Interpretation, and Recommendation
- Structuring an Analytical Narrative
- Choosing the Right Level of Technical Detail
- Explaining Effect Sizes in Familiar Units
- Presenting Uncertainty and Alternative Explanations
- Communicating Null and Inconclusive Results
- Avoiding Causal Language for Associational Findings
- Executive Summaries and Decision Memos
- Technical Appendices and Reproducible Evidence
- Responding to Challenges and Revising Conclusions
- Visual and Verbal Consistency Across Deliverables
63Efficient Analytics at Scale11 topics
- Memory, Compute, Storage, and Data Movement Costs
- Columnar Data and Selective Reading
- Partitioning and Predicate Pushdown
- Query Optimization for Analytical Workloads
- Join Strategy, Data Skew, and Intermediate Result Size
- Chunked and Out-of-Core Processing
- Vectorization and Avoiding Unnecessary Recalculation
- Sampling and Approximate Aggregation
- Accuracy-Cost Tradeoffs in Approximate Results
- Distributed Processing Semantics Relevant to Analysts
- Validating That Faster Computation Preserves the Intended Result
64Data Governance and Responsible Access11 topics
- Data Ownership, Stewardship, and Accountability
- Catalogs, Dictionaries, and Business Definitions
- Data Provenance, Licensing, and Reuse Conditions
- Access According to Analytical Need
- Sensitive Attributes and Data Minimization
- Pseudonymization and Reidentification Risk
- Privacy-Preserving Aggregation and Disclosure Risk
- Retention, Deletion, and Reproducibility Tensions
- Sharing Data, Code, and Results Responsibly
- Documenting Approved Uses and Known Limitations
- Coordinating Analysis with Privacy and Security Requirements
65Fairness, Ethics, and Societal Impact11 topics
- Representation and Historical Bias in Data
- Measurement Bias and Unequal Error
- Proxy Variables and Sensitive Characteristics
- Subgroup Evaluation and Small-Sample Uncertainty
- Competing Fairness Definitions and Decision Context
- Feedback Loops Created by Analytical Decisions
- Who Benefits and Who Bears the Costs
- Consent, Expectations, and Appropriate Data Use
- Human Oversight and Contestability
- Ethical Review of High-Impact Analyses
- Communicating Limitations Without Overstating Objectivity
66Analytics Delivery, Monitoring, and Maintenance11 topics
- Delivering Reusable Datasets, Reports, and Analytical Models
- Ownership and Handoff Documentation
- Scheduled Refreshes and Data Dependency Management
- Versioned Logic and Historical Result Reproducibility
- Acceptance Checks Before Publishing Results
- Monitoring Freshness, Completeness, and Metric Behavior
- Investigating Failed Refreshes and Inconsistent Outputs
- Upstream Changes and Analytical Drift
- Revising Models and Metrics with Controlled Comparisons
- Communicating Corrections to Published Findings
- Retiring Analyses That No Longer Support Valid Decisions
Stage 7
Practicum & Capstone
One decision, argued with data
5 modules · 52 topics
67Data Preparation and SQL Practicum10 topics
- Turning an Ambiguous Request into a Dataset Specification
- Profiling and Cleaning a Messy Multisource Dataset
- Resolving Identifier and Join Cardinality Problems
- Building an Analytical Table at the Correct Grain
- Implementing Event Windows and Historical State Logic
- Creating Cohort, Funnel, and Retention Queries
- Reconciling Metrics Across Independent Calculations
- Investigating Missing Data and Measurement Gaps
- Documenting Data Lineage and Quality Limitations
- Delivering a Reproducible Preparation Workflow
68Statistical Inference and Modeling Practicum10 topics
- Describing a Distribution Without Hiding Important Variation
- Designing a Sample and Assessing Representation
- Estimating an Effect with Appropriate Uncertainty
- Comparing Parametric and Resampling-Based Results
- Interpreting Multiple Tests Without Selective Reporting
- Fitting and Critiquing a Regression Model
- Analyzing a Binary or Count Outcome
- Evaluating Repeated or Clustered Observations
- Comparing Predictive Performance on Appropriate Held-Out Data
- Writing a Statistical Findings Memo with Explicit Assumptions
69Experimentation and Causal Analysis Practicum10 topics
- Defining a Causal Question and Drawing Its Assumptions
- Planning an Experiment with Decision-Relevant Power
- Auditing Assignment, Exposure, and Outcome Measurement
- Diagnosing Sample Ratio Mismatch and Attrition
- Analyzing an A/B Test with Primary and Guardrail Metrics
- Evaluating the Credibility of a Subgroup Effect
- Assessing Covariate Balance in an Observational Study
- Critiquing a Difference-in-Differences or Discontinuity Design
- Conducting Sensitivity Analysis for a Causal Claim
- Preparing an Experiment Readout and Decision Recommendation
70Forecasting and Decision Analytics Practicum10 topics
- Preparing a Time Series with Calendar and Missing-Period Checks
- Establishing Naive and Seasonal Forecast Baselines
- Comparing Forecasts Through Rolling-Origin Evaluation
- Assessing Prediction Intervals and Tail Risk
- Reconciling Forecasts Across Related Business Units
- Diagnosing Retention and Revenue Changes by Cohort
- Simulating an Operational Decision Under Uncertainty
- Formulating a Constrained Resource Allocation Problem
- Evaluating Whether More Information Would Change the Decision
- Presenting a Recommendation with Sensitivity to Key Assumptions
71Integrated Data Science and Analytics Capstone12 topics
- Selecting a Decision and Defining an Answerable Question
- Specifying the Population, Unit, Time Horizon, and Estimand
- Auditing Data Sources and Measurement Processes
- Building and Validating an Analytical Dataset
- Exploring Patterns and Recording Competing Explanations
- Choosing Methods Appropriate to the Question and Data
- Quantifying Uncertainty and Testing Robustness
- Distinguishing Descriptive, Predictive, and Causal Conclusions
- Evaluating Practical Value, Fairness, and Decision Consequences
- Delivering a Clear Report or Decision-Support Dashboard
- Providing Reproducible Code, Documentation, and Review Evidence
- Defining Follow-Up Measurement and Maintenance Needs
Fifteen minutes.Every day.
- 1
A lesson fits a lunch break
One idea at a time, in short slides. A whole lesson takes about fifteen minutes.
- 2
Practice with instant feedback
Questions sit inside the lesson. Answer one and see right away whether you got it.
- 3
A streak that brings you back
A lesson a day keeps the streak alive. Small, steady sessions carry you through.
- 1
A lesson fits a lunch break
One idea at a time, in short slides. A whole lesson takes about fifteen minutes.
- 2
Practice with instant feedback
Questions sit inside the lesson. Answer one and see right away whether you got it.
- 3
A streak that brings you back
A lesson a day keeps the streak alive. Small, steady sessions carry you through.
For anyone who owns a number.
Analysts outgrowing spreadsheets
Product managers who own a metric
PhDs leaving the lab for industry
Where this course leads.
The job this course is built around, and how people get into it.
Data Analyst
Turns a company's data into answers people can act on, and grows into building models.
All future careersOn the job
- Write the query that answers this week's question
- Build a dashboard leadership actually reads
- Design an experiment and read its result correctly
How people get in
Many start as analysts with SQL, spreadsheets and a statistics basis; data scientist roles usually ask for a quantitative degree.
Be first in line.
Early access for individuals, pilots for teams. Tell us who's learning.
enterprise@astratrainer.com