A tailored course, built for your situation
Roles you couldn't apply for before, now open
Build the data fluency to move into business analytics roles across insurance, fintech, and risk intelligence
$199 one-time
24-hour access provisioning
30-day money-back guarantee
Hand-built implementation playbook
12 modules. 12 chapters per module. 144 chapters total.
12 modules, each with 12 chapters (144 chapters total), text-based, plus downloadable templates and a hand-built implementation playbook delivered alongside course access.
The situation this course is for
Who this is for
Early-career professional transitioning into data/business analytics with insurance domain experience
Who this is not for
Senior data scientists, engineers, or executives looking for strategic oversight content
What you walk away with
- Produce portfolio-ready analytics reports grounded in insurance workflows
- Structure raw claims and policy data into clean, queryable models
- Confidently apply to business analyst roles in fintech and insurtech
- Translate domain knowledge into data storytelling that resonates with stakeholders
- Build a personal implementation playbook for ongoing project replication
The 12 modules (with all 144 chapters)
Module 1. Mapping insurance workflows to data touchpoints
Identify where data is generated across underwriting, claims, and policy servicing, and align them to common analytics use cases.
12 chapters in this module
- Underwriting data sources
- Claims lifecycle stages
- Policy change tracking
- Premium capture points
- Customer interaction logs
- Agent input systems
- Third-party data feeds
- Regulatory reporting nodes
- Fraud detection triggers
- Reinsurance touchpoints
- Internal audit trails
- System of record mapping
Module 2. Translating domain knowledge into analysis briefs
Convert operational experience into clear problem statements and analysis goals that mirror real business analyst work.
12 chapters in this module
- From observation to question
- Defining success metrics
- Stakeholder intent decoding
- Scope boundary setting
- Assumption documentation
- Data availability check
- Timeframe alignment
- Impact estimation
- Output format selection
- Validation method design
- Feedback loop planning
- Brief version control
Module 3. Cleaning claims datasets for analysis
Apply consistent logic to messy claims data to produce structured, reliable inputs for reporting and modelling.
12 chapters in this module
- Status code normalization
- Date field reconciliation
- Deductible extraction
- Cause-of-loss categorization
- Payment type tagging
- Reserve amount tracking
- Adjuster assignment logs
- Litigation flagging
- Recovery status coding
- Claim reopen detection
- Fraud indicator mapping
- Settlement timeline structuring
Module 4. Building policyholder cohort models
Segment policy data to support retention analysis, risk profiling, and product targeting.
12 chapters in this module
- Tenure banding
- Premium tier grouping
- Geographic clustering
- Product bundle tagging
- Payment method segmentation
- Lapse history tracking
- Channel origin coding
- Claim frequency bands
- Demographic inference
- Risk score bracketing
- Cross-sell potential flags
- Engagement level scoring
Module 5. Creating underwriting performance dashboards
Design visual summaries that highlight approval rates, risk acceptance trends, and profitability by segment.
12 chapters in this module
- Application volume trends
- Approval rate tracking
- Decline reason breakdown
- Risk class distribution
- Average premium by tier
- Bind rate analysis
- Quote-to-issue lag
- Channel performance
- UW workload balance
- Reinsurance cession rates
- Policy amendment frequency
- Cancellation timing patterns
Module 6. Structuring loss ratio reports
Calculate and present loss ratios with appropriate segmentation and trend analysis for business decision-making.
12 chapters in this module
- Incurred loss definition
- Earned premium calculation
- Time period alignment
- Segment dimension selection
- Trend line construction
- Benchmark comparison
- Volatility adjustment
- Catastrophe exclusion
- Development lag consideration
- Per-policy averaging
- High-loss claim isolation
- Predictive ratio modelling
Module 7. Documenting data logic for peer review
Write clear, audit-ready documentation that explains transformations, assumptions, and limitations.
12 chapters in this module
- Transformation purpose
- Input source naming
- Output schema definition
- Filter rationale
- Join logic explanation
- Aggregation method
- Null handling rule
- Outlier treatment
- Version change log
- Validation result summary
- Stakeholder feedback
- Peer sign-off process
Module 8. Packaging insights for non-technical audiences
Turn technical findings into concise, action-oriented summaries for business teams.
12 chapters in this module
- Executive summary writing
- Key finding isolation
- Business impact framing
- Recommendation phrasing
- Visual simplification
- Jargon elimination
- Context anchoring
- Risk level signposting
- Next step suggestion
- Limitation disclosure
- Confidence level rating
- Feedback request drafting
Module 9. Developing a personal analytics portfolio
Curate and present projects that demonstrate applied skill and domain insight.
12 chapters in this module
- Project selection criteria
- Anonymization technique
- Narrative flow design
- Problem statement drafting
- Method summary writing
- Result visualization
- Impact reflection
- Lessons learned section
- Tool stack documentation
- Version control setup
- Portfolio platform choice
- Sharing permission settings
Module 10. Tailoring applications to analyst roles
Align your background and project work to job descriptions in fintech, insurtech, and corporate analytics teams.
12 chapters in this module
- JD keyword mapping
- Transferable skill listing
- Project relevance scoring
- Resume bullet crafting
- Cover letter framing
- LinkedIn profile update
- Portfolio linking
- Domain advantage highlighting
- Gap addressing statement
- Certification mention
- Reference readiness
- Follow-up timing
Module 11. Navigating internal transfer processes
Position yourself effectively for analytics opportunities within insurance organizations.
12 chapters in this module
- Internal job board monitoring
- Stakeholder relationship mapping
- Visibility enhancement tactic
- Skill gap self-assessment
- Manager conversation prep
- Project volunteer strategy
- Cross-team collaboration
- Mentor identification
- Development plan drafting
- Performance review alignment
- Transfer application timing
- Success metric definition
Module 12. Sustaining growth after role transition
Build habits and systems to continue advancing once in an analytics position.
12 chapters in this module
- Weekly learning block
- Project reflection ritual
- Feedback seeking schedule
- Tool mastery roadmap
- Network expansion tactic
- Conference participation
- Internal presentation prep
- Mentorship offering
- Knowledge sharing format
- Goal setting cycle
- Portfolio update rhythm
- Career path mapping
How this maps to your situation
- Transitioning from operations to analytics
- Building credibility without formal data role
- Applying internally or externally for analyst jobs
- Succeeding after landing first analytics role
Before vs. after
Before
Capable professional with domain insight but limited structured analytics output
After
Confident candidate with portfolio, documentation, and role-specific application materials
What's included with your purchase
- 12 modules with 12 chapters each (144 chapters)
- Downloadable templates and worked examples for every module
- Hand-built implementation playbook delivered alongside course access
- 30-day money-back guarantee
Delivery and format
- Course and learning environment access provisioned within 24 hours of purchase
- Hand-built implementation playbook delivered alongside course access
Format: Text-based modules and chapters in the Art of Service learning environment, plus downloadable templates and worked examples for every chapter, plus the hand-built implementation playbook delivered alongside course access.
Time investment: Approximately 3-4 hours per module, designed for completion over 12 weeks with real-world application between sections.
If nothing changes
Continuing to rely solely on experience without documented analytical work may limit access to formal analytics roles, even with strong domain knowledge.
How this compares to the alternatives
Unlike generic data science courses, this program focuses specifically on the artefacts, documentation standards, and communication patterns that insurance-adjacent analytics hiring managers value in early-career candidates.
Frequently asked
Is this course technical?
It focuses on applied analytics thinking and documentation, not coding. Tools like Excel, Sheets, or Lightdash are used conceptually.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help me get certified?
The course builds practical capability rather than preparing for a specific certification exam.
$199 one-time. Approximately 3-4 hours per module, designed for completion over 12 weeks with real-world application between sections..
Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.
30-day money-back guarantee·
144 chapters·
Hand-built playbook included·
Account access within 24 hours