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Full-stack Data Scientist
The document discusses the role of a full-stack data scientist. It begins with an introduction of the author, Alexey Grigorev, as a data scientist. It then outlines the plan to discuss the data science process, roles in a data science team, what defines a full-stack data scientist, and how to become a full-stack data scientist. It proceeds to explain the CRISP-DM process for data science projects. It describes the different roles in a data science team including product manager, data analyst, data engineer, data scientist, and ML engineer. It defines a full-stack data scientist as someone who can work across the entire data science lifecycle and discusses the breadth of skills required to become a
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Full-stack Data Scientist
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- 3. ● I’m Alexey ●Data Scientist
- 4. Plan ● Data scienceprocess ● Roles in the team ● Full-stack data scientist: Jack of all trades ● Becoming full-stack
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- 7. Picture: CRISP-DM Identify thebusiness problem, understand how we can solve it
- 8. Picture: CRISP-DM Analyze available datasources, decide if we need to get more data
- 9. Picture: CRISP-DM Transform thedata so it can be put into a ML algorithm
- 10. Picture: CRISP-DM Training themodels: the actual machine learning happens here
- 11. Picture: CRISP-DM Measure howwell the model solves the business problem
- 12. Picture: CRISP-DM Deploy themodel to production
- 13. We need ateam for that!
- 14. Plan ● Data scienceprocess ● Roles in the team ● Full-stack data scientist: Jack of all trades ● Becoming full-stack
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- 22. Product Manager Data Analyst DataEngineer Data Scientist Data Analyst ML Engineer Picture: CRISP-DM
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- 24. Plan ● Data scienceprocess ● Roles in the team ● Full-stack data scientist: Jack of all trades ● Becoming full-stack
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- 33. Product management DeploymentEvaluationModeling Data Preparation Data Understanding Business Understanding Product management Domainexpertise Data analysis Communication
- 34. We need tolearn ● Product management ● Data analysis ● Data engineering ● Backend engineering ● DevOps ● …
- 35. We need tolearn ● Product management ● Data analysis ● Data engineering ● Backend engineering ● DevOps ● …
- 36. We need tolearn ● Product management ● Data analysis ● Data engineering ● Backend engineering ● DevOps ● … 🤯
- 37. Plan ● Data scienceprocess ● Roles in the team ● Full-stack data scientist: Jack of all trades ● Becoming full-stack
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- 45. Skill proficiency ● OK— can use the skill, mostly independently ● Good — can use the skill independently ● Great — expert
- 46. We don’t needto be experts in everything!
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- 61. 80/20 rule ● Breakdown the role into core areas and skills ● Order the skills by importance ● Pick the most important ones ● Practice practice practice
- 62. Product management ● Strategy ●UX & Design ● Communication ● Planning ● Evaluation
- 63. Product management ● Strategy ●UX & Design ● Communication ● Planning ● Evaluation ● Requirement gathering ● Prioritization ● Stakeholder management
- 64. Data engineering ● SQLDatabases ● NoSQL Databases ● Stream processing ● Batch processing ● Data pipelines
- 65. Data engineering ● SQLDatabases ● NoSQL Databases ● Stream processing ● Batch processing ● Data pipelines ● MySQL ● AWS (S3, Kinesis) ● Spark ● Airflow
- 66. Backend engineering ● SQL& NoSQL Databases ● CS fundamentals ● Languages and frameworks ● Web services ● Best practices
- 67. Backend engineering ● SQL& NoSQL Databases ● CS fundamentals ● Languages and frameworks ● Web services ● Best practices ● Python ● Docker ● Tests ● Clean code
- 68. DevOps ● Infrastructure ● Automation ●Monitoring ● Reliability
- 69. DevOps ● Infrastructure ● Automation ●Monitoring ● Reliability ● AWS ● Kubernetes ● Terraform
- 70. Plan ● Data scienceprocess (CRISP-DM) ● Roles in the team ● Full-stack data scientist: Jack of all trades ● Becoming full-stack
- 71. Summary ● Cover thewhole ML project lifecycle ● Invest in software engineering ● It’s not only about technical skills ● Be a T-shaped professional ● Focus on what matters
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- 73. mlbookcamp.com ● Learn MachineLearning by doing projects ● http://bit.ly/mlbookcamp ● Get 40% off with code “grigorevpc” Machine Learning Bookcamp
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