Document decisions about exploratory data analysis in a form that remains useful after the people change.
Designing Advanced Exploratory Data Analysis Models
An applied course in exploratory data analysis built around the decisions practitioners actually face.
📍 Istanbul🗓️ 5 training days📚 4 modules🎓 Accredited certificate
5intensive training days
4scientific modules
8training sessions
32detailed points
Course Overview
Technical debt in exploratory data analysis is borrowed against future delivery capacity, at compound interest. Most outages involving the technology and systems delivery capability are triggered by a change somebody considered routine. Post-incident reviews keep identifying weaknesses in this strand of technology and systems delivery that were visible long before the incident. Teams frequently over-invest in documenting exploratory data analysis and under-invest in testing it. The course sets out a working method for the practice within technology and systems delivery that participants can apply the week they return. It is appropriate for those preparing to take on wider responsibility for this strand of technology and systems delivery. Sessions alternate between guided analysis of exploratory data analysis and supervised application. The programme builds the judgement to know which parts of the technology and systems delivery discipline to standardise and which to leave flexible. The programme ends where implementation begins, with this area of technology and systems delivery broken into steps someone can start on Monday.
Expected Learning Outcomes
Document exploratory data analysis to the level a new engineer could operate it.
Build the internal capability for exploratory data analysis rather than depending on external support indefinitely.
Establish monitoring and alerting on exploratory data analysis that reflects user experience.
Apply access control and least privilege throughout exploratory data analysis.
Build a register of the risks attaching to exploratory data analysis and keep it current.
Design backup and recovery for exploratory data analysis and prove it by restoring.
Who Should Attend
Those responsible for briefing external stakeholders on exploratory data analysis.
Quality and test engineers verifying exploratory data analysis.
Service desk and support leads handling incidents in exploratory data analysis.
Network and communications engineers supporting exploratory data analysis.
Technical staff being prepared for supervisory responsibility over exploratory data analysis.
Solution architects designing exploratory data analysis.
Course Modules
Exploratory data analysis: architecture and designing for failure
2 sessions · 8 pointsSession 1Making releases of exploratory data analysis routine instead of risky
- Rank the weaknesses in exploratory data analysis by consequence rather than by ease of fixing.
- Measure current load on exploratory data analysis and project it forward twelve months.
- Identify the single points of failure in exploratory data analysis.
- Confirm that those complying with exploratory data analysis understand why it exists.
Session 2The hard cases in exploratory data analysis and how to reason about them
- Test the failover for exploratory data analysis rather than assuming it works.
- Estimate the resource exploratory data analysis requires to run as designed.
- Set out the decisions in exploratory data analysis that require sign-off and by whom.
- Check that records of exploratory data analysis answer the questions likely to be asked.
Exploratory data analysis: requirements, targets and service levels
2 sessions · 8 pointsSession 1Running an incident on exploratory data analysis calmly
- Establish what evidence demonstrates exploratory data analysis is under control.
- Review logging on exploratory data analysis for coverage and retention.
- Plan the sequence in which improvements to exploratory data analysis will be introduced.
- Confirm that reporting on exploratory data analysis reaches the people who can act.
Session 2Dependencies and supply chain risk in exploratory data analysis
- Decide what will be stopped to create capacity for exploratory data analysis.
- Confirm every release of exploratory data analysis can be rolled back within a defined time.
- State the availability and recovery objectives for exploratory data analysis as numbers.
- Document the runbook for exploratory data analysis to the level a new engineer could use.
Exploratory data analysis: security, identity and least privilege
2 sessions · 8 pointsSession 1Where exploratory data analysis typically breaks, and why
- Inventory third-party dependencies inside exploratory data analysis and their update status.
- Configure alerting on exploratory data analysis that reflects what users experience.
- Distinguish symptoms from causes when exploratory data analysis underperforms.
- Apply change control to exploratory data analysis including emergency changes.
Session 2Documenting exploratory data analysis so someone else can operate it
- Identify single points of dependency in exploratory data analysis and reduce them.
- Set out how exceptions to exploratory data analysis are requested and approved.
- Assess the exit route from any cloud or vendor dependency in exploratory data analysis.
- Run a load test on exploratory data analysis at expected peak plus a margin.
Exploratory data analysis: capacity, performance and load
2 sessions · 8 pointsSession 1Reviewing exploratory data analysis when nothing has gone wrong
- Confirm the support model and escalation path for exploratory data analysis.
- Restore a backup of exploratory data analysis in a test environment and time it.
- Prepare the response for the most likely failure in exploratory data analysis.
- Agree the smallest change to exploratory data analysis that would be visibly useful.
Session 2Designing exploratory data analysis around how it will fail
- Benchmark the organisation's exploratory data analysis against comparable operations.
- Agree what will be standardised in exploratory data analysis and what will not.
- Confirm data retention and deletion rules applied within exploratory data analysis.
- Set delivery and reliability indicators for exploratory data analysis the team trusts.
Choose the package that suits you
Silver Package
At least 3 people
USD1,250
- Workshop or Program Participation
- Airport Transfers
- Customized Badge
- Expert Mentorship (Private Sessions)
- Supervision & Secretarial Services
- Accredited Certificate of Participation
- Complete Training Kit
- Coffee Break
- Closing Ceremony
Gold Package
At least 3 people
USD1,850
- 5-night stay in a 5-star hotel
- Workshop or Program Participation
- Airport Transfers
- Customized Badge
- Expert Mentorship (Private Sessions)
- Supervision & Secretarial Services
- Accredited Certificate of Participation
- Complete Training Kit
- Coffee Break
- Closing Ceremony
Complete your registration
We will contact you within one business day to confirm.