Podcast
Designing FinTech Data Analytics Curriculum: Fraud Detection, BigQuery Labs & Mentoring
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Designing FinTech Data Analytics Curriculum: Fraud Detection, BigQuery Labs & Mentoring
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Episode Overview
How do you design a FinTech data analytics curriculum that teaches fraud detection, chargeback modeling, and real-world cloud skills while also mentoring diverse learners? In this episode, Irina Brudaru — Head of Data & Analytics at Finlex, former Google data leader, and long-time mentor and teacher — walks through building practical FinTech courses informed by industry experience across Berlin, Amsterdam and the Bay Area.
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Chapter Summary
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- 0:00 - Podcast Introduction
- 1:08 - Guest Overview: Irina Brudaru — teacher, curriculum developer, mentor in
- 2:13 - Career Origins: early computing, Romania education, Max Planck research
- 3:41 - Industry Transition: data consulting, BI, Google and product analytics experience
- 6:16 - International Roles & Management: San Francisco, Netherlands, Berlin; leading
- 8:57 - Early Mentoring Wins: mentoring family, interns, and career pivot stories
- 9:34 - Mentoring Methods: visual explanations, learner-centered teaching techniques
- 9:57 - Community Teaching: NGOs, bootcamps, and FrauenLoop volunteer work
- 13:18 - Curriculum Design for FinTech: AI Guild program planning and certification
- 14:56 - Curriculum Components: fraud, chargeback, ML in production, and business
- 18:27 - Instructor Sourcing & Storytelling: finding teachers and teaching data storytelling
- 22:14 - Fraud Detection & Chargeback Modeling: rule-based vs neural approaches in
- 25:43 - Hands-on Cloud Teaching: BigQuery labs, student cloud access, demystifying
- 28:54 - Overcoming Cloud Reluctance: focusing on essential cloud skills for analysts
- 29:51 - Managerial Scope: balancing analytics, data engineering, and technical credibility
- 31:50 - Cohort Analysis Explained: retention metrics, product analytics visualization
- 35:34 - Path to Formal Teaching: outreach, invitations, and joining teaching programs
- 38:49 - Gender Diversity Research: plans to analyze company data for inclusion insights
- 41:16 - Recruiting Women to Zoomcamps: targeted outreach, partnerships, and scheduling
- 45:24 - Securing Technical Feedback: finding reviewers, advocating for code review
- 49:39 - Learning Antipatterns: ML hype, overengineering, and tool-centric approaches
- 54:46 - Career Transition Advice: moving into data science from non-technical roles
- 58:08 - Core Analyst Fundamentals: SQL, data visualization, soft skills, and product
- 1:00:32 - Community Partnerships: collaborating with Women in Tech groups and volunteer