Podcast
From Project Manager to Data Scientist: Skills, Tools, ML Courses & Job Search
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From Project Manager to Data Scientist: Skills, Tools, ML Courses & Job Search
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Episode Overview
How do you move from project management into a data science career — and what skills, tools, and courses actually matter? In this episode, Ksenia Legostay, Manager/Data Scientist at momox GmbH, walks through her transition after four years as a project manager into three years researching fraud and anomaly detection and earning a degree in data analysis. We cover career foundations, the difference between analytics and data science, and a concrete learning strategy: assess strengths, target gaps, and build core.
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Chapter Summary
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- 0:00 - Podcast Introduction
- 2:24 - Guest Overview: Ksenia and episode focus (project management → data science)
- 3:00 - Career Foundations: math degree, management, and early PM roles
- 4:35 - Motivation for Analytics: customer-centric, data-driven decision making
- 6:54 - Transition Path: moving from data analysis into machine learning
- 7:30 - Analytics vs. Data Science: descriptive analysis vs. forecasting
- 8:33 - Learning Strategy: assess strengths and target skill gaps
- 11:10 - Education Choices: benefits of formal degrees vs. self-study
- 13:00 - Core Skill Set: programming, statistics, and domain expertise
- 17:18 - Recommended Coursework: machine learning, time series, graph analysis
- 19:36 - Online Resources & Course Picks (including mlcourse.ai)
- 22:32 - Transferable PM Skills: planning, stakeholder communication, business KPIs
- 30:20 - Project Frameworks: using CRISP-DM to structure data projects
- 32:43 - Starting as a Data Analyst: apply analysis at work and build portfolio
- 34:48 - Tools Progression: spreadsheets → BI tools (Tableau/Trifacta) → Python &
- 36:47 - Community Learning: OpenDataScience, DataTalks, and mentorship
- 38:54 - Kaggle Practice: studying notebooks and collaborative competitions
- 41:07 - Production Readiness: Git, testing, Docker, deployment, and Clean Code
- 43:16 - Domain Specialization: research experience in fraud detection and node2vec
- 48:35 - Job Search Reality: applications, interviews, and persistence
- 51:15 - Bridging Theory and Practice: applying university work in industry
- 54:09 - Part-time Learning Plan: nanodegrees and structured six-month paths
- 57:42 - Essential Math Topics: probability, statistics, and graph theory
- 1:01:01 - Career Habits: critical path, study techniques, and lifelong learning
- 1:01:27 - Final Advice: contribute to projects, narrow your scope, join communities