Full-stack Data Scientist
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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
- 3. ● I’m Alexey ● Data Scientist
- 4. Plan ● Data science process ● Roles in the team ● Full-stack data scientist: Jack of all trades ● Becoming full-stack
- 5. Data science
- 7. Picture: CRISP-DM Identify the business problem, understand how we can solve it
- 8. Picture: CRISP-DM Analyze available data sources, decide if we need to get more data
- 9. Picture: CRISP-DM Transform the data so it can be put into a ML algorithm
- 10. Picture: CRISP-DM Training the models: the actual machine learning happens here
- 11. Picture: CRISP-DM Measure how well the model solves the business problem
- 12. Picture: CRISP-DM Deploy the model to production
- 13. We need a team for that!
- 14. Plan ● Data science process ● Roles in the team ● Full-stack data scientist: Jack of all trades ● Becoming full-stack
- 22. Product Manager Data Analyst Data Engineer Data Scientist Data Analyst ML Engineer Picture: CRISP-DM
- 24. Plan ● Data science process ● Roles in the team ● Full-stack data scientist: Jack of all trades ● Becoming full-stack
- 32. Software engineering DeploymentEvaluationModeling Data Preparation Data Understanding Business Understanding Web services Microservices Infrastructure Data engineering Databases Data pipelines
- 33. Product management DeploymentEvaluationModeling Data Preparation Data Understanding Business Understanding Product management Domain expertise Data analysis Communication
- 34. We need to learn ● Product management ● Data analysis ● Data engineering ● Backend engineering ● DevOps ● …
- 35. We need to learn ● Product management ● Data analysis ● Data engineering ● Backend engineering ● DevOps ● …
- 36. We need to learn ● Product management ● Data analysis ● Data engineering ● Backend engineering ● DevOps ● … 🤯
- 37. Plan ● Data science process ● Roles in the team ● Full-stack data scientist: Jack of all trades ● Becoming full-stack
- 38. Accuracy Data
- 45. Skill proficiency ● OK — can use the skill, mostly independently ● Good — can use the skill independently ● Great — expert
- 46. We don’t need to be experts in everything!
- 55. Areas Depth Product Management Data Engineering Machine Learning Backend Engineering DevOps Me now
- 56. Areas Depth Product Management Data Engineering Machine Learning Backend Engineering DevOps How other DS see me * * maybe not
- 57. Areas Depth Product Management Data Engineering Machine Learning Backend Engineering DevOps How engineers see me * * maybe not
- 61. 80/20 rule ● Break down 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 ● SQL Databases ● NoSQL Databases ● Stream processing ● Batch processing ● Data pipelines
- 65. Data engineering ● SQL Databases ● 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 science process (CRISP-DM) ● Roles in the team ● Full-stack data scientist: Jack of all trades ● Becoming full-stack
- 71. Summary ● Cover the whole ML project lifecycle ● Invest in software engineering ● It’s not only about technical skills ● Be a T-shaped professional ● Focus on what matters
- 73. mlbookcamp.com ● Learn Machine Learning by doing projects ● http://bit.ly/mlbookcamp ● Get 40% off with code “grigorevpc” Machine Learning Bookcamp