My slides from a talk at the Scotland Data Science & Technology meetup

1. Full-stack
data scientist
Alexey Grigorev
02.07.2020

2. https://www.goodfreephotos.com/vector-images/unicorn-with-rainbow-vector-clipart.png.php

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

6. Picture: CRISP-DM

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

15. PM
Data
Analyst
Data
Engineer
Data
Scientist
Software/ML
Engineer
DevOps/
SRE

17. Product Manager
Picture: CRISP-DM

18. Picture: CRISP-DM
Data Analyst

19. Picture: CRISP-DM
Data Engineer

20. Picture: CRISP-DM
Data Scientist

21. Picture: CRISP-DM
ML Engineer

22. Product Manager
Data Analyst
Data Engineer
Data Scientist
Data Analyst
ML Engineer
Picture: CRISP-DM

23. Picture: CRISP-DM
Data Scientist

24. Plan
● Data science process
● Roles in the team
● Full-stack data scientist: Jack of all trades
● Becoming full-stack

25. DeploymentEvaluationModeling
Data
Preparation
Data
Understanding
Business
Understanding

26. DeploymentEvaluationModeling
Data
Preparation
Data
Understanding
Business
Understanding

27. DeploymentEvaluationModeling
Data
Preparation
Data
Understanding
Business
Understanding
Full-stack
Non-full-stack
Legend

28. DeploymentEvaluationModeling
Data
Preparation
Data
Understanding
Business
Understanding
Full-stack
Non-full-stack
Legend
DeploymentEvaluationModeling
Data
Preparation
Data
Understanding
Business
Understanding

29. Full-stack
Non-full-stack
Legend
DeploymentEvaluationModeling
Data
Preparation
Data
Understanding
Business
Understanding

30. Full-stack
Non-full-stack
Legend
You
DeploymentEvaluationModeling
Data
Preparation
Data
Understanding
Business
Understanding

31. Core data science
DeploymentEvaluationModeling
Data
Preparation
Data
Understanding
Business
Understanding

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

39. Good
Accuracy
Data

40. Good
Accuracy
Data
Great

41. Skill proficiency
Time

42. Great
Skill proficiency
Time

43. Good
Skill proficiency
Time
Great

44. Great
Good
OK
Skill proficiency
Time

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!

47. Areas
Depth
Product
Management
Data
Engineering
Machine
Learning
Backend
Engineering
DevOps

48. Areas
Depth
Product
Management
Data
Engineering
Machine
Learning
Backend
Engineering
DevOps
Me a while
ago

49. Areas
Depth
Product
Management
Data
Engineering
Machine
Learning
Backend
Engineering
DevOps

50. Areas
Depth
Product
Management
Data
Engineering
Machine
Learning
Backend
Engineering
DevOps

51. Areas
Depth
Product
Management
Data
Engineering
Machine
Learning
Backend
Engineering
DevOps

52. Areas
Depth
Product
Management
Data
Engineering
Machine
Learning
Backend
Engineering
DevOps

53. Areas
Depth
Product
Management
Data
Engineering
Machine
Learning
Backend
Engineering
DevOps

54. Areas
Depth
Product
Management
Data
Engineering
Machine
Learning
Backend
Engineering
DevOps

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

58. Areas
Depth
Product
Management
Data
Engineering
Machine
Learning
Backend
Engineering
DevOps

59. Depth
Breadth
Expert level
Good level

60. Good
Skill proficiency
Time
Great

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

72. olxgroup.com/careers

73. mlbookcamp.com
● Learn Machine Learning by doing
projects
● http://bit.ly/mlbookcamp
● Get 40% off with code “grigorevpc”
Machine Learning
Bookcamp

74. @Al_Grigoragrigorev
alexeygrigorev.com
mlbookcamp.com