Explore Tasks
Diagnose product adoption, funnel and retention behaviour
A Product Manager receives a diagnosis of where users drop out, which features drive adoption and retention by cohort and segment, the tracking limits behind it and the product questions worth testing next.
1job does it
1job helps with it
59skills it needs
Does itHelps with it
- Reading and tracing unfamiliar code
- Reviewing human- and AI-generated code changes for correctness and risk
- Sampling and reviewing a team's work against standards
- Diagnosing misleading data visualisations
- Verifying reports and dashboards before and after release
- Verifying AI-assisted outputs against sources
- Disclosing AI contribution to professional work
- Testing competing explanations with discriminating evidence
- Independently checking analytical conclusions and claims
- Escalating unresolved risks with evidence and clear ownership
- Giving specific developmental feedback on data and AI work
- Judging the materiality of defects and deficiencies for the intended use
- Designing and validating digital analytics event measurement
- Setting and reviewing action thresholds for scores and model predictions
- Interpreting averages, weighted measures and mix effects
- Reading business metrics
- Writing data quality rules
- Interpreting correlation without inferring cause
- Designing experiments
- Estimating and challenging causal effects
- Evaluating ML generalisation and fitness
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Analysing staged funnels: conversion, time in stage and slippage
- Mapping customer journeys and evidence gaps
- Constructing and challenging customer segments
- Diagnosing digital funnel conversion from event data
- Reconstructing a record's state at a point in time from history and audit trails
- Challenging probability-weighted sales forecasts
- Measuring campaign outcomes with valid denominators
- Building cohort and retention analyses with correct denominators
- Writing maintainable code for analytical and data work
- Fitting, checking and interpreting inferential regression models
- Estimating quantities and differences with intervals
- Establishing and declaring data grain
- Combining tables safely: cardinality, fan-out and conservation of totals
- Calculating and interpreting percentages, rates and ratios
- Judging whether a change exceeds normal variation
- Formulating answerable data questions
- Assessing data source credibility and fitness for a question
- Profiling data
- Standardising and cleaning data under agreed rules
- Preparing analysis-ready datasets reproducibly
- Choosing and justifying missing-data treatments
- Describing distributions with summaries and plots
- Comparing groups
- Exploring data to find patterns that survive validation
- Investigating anomalies
- Designing and interpreting hypothesis tests
- Reading charts and dashboards correctly, including their state
- Selecting charts for evidence and decisions
- Writing evidence-led findings and auditable recommendations with their limits
- Defining the analysis population: filters, nulls, dates and time zones
- Analysing event sequences, states and durations
- Diagnosing incorrect query and calculation results
- Decomposing one's own tasks for AI assistance
- Training reproducible machine learning models
- Eliciting the decision behind an analytical request
- Designing efficient and reproducible investigations
- Choosing an analytical method to fit the question and data
Paths that teach the skills it needs
Skill Path
Data Literacy for Business Decisions
Judge whether a business claim from a table, KPI, chart or dashboard really holds up.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Data Preparation: Cleaning, Repair and Reconciliation
Check whether a table is fit for the job, then clean, repair and reconcile it with the evidence to back it.
- Modules
- 5 open
- Practice data
- Raket Parts
Tool Path
SQL for Analysis
Answer real business questions with SQL, from your first query to finding why a number moved.
- Modules
- 5 open
- Runs in
- Your browser
Coming soonFraming Questions and Sourcing Evidence for Investigations