In this session we'll run you through the entire workflow of a machine learning application and introduce you to the different phases of a data science project: data exploration, prototyping, validation, and productization. From there on we will guide you to work hands-on on improving prediction accuracy or other features of the application.

1. A Hands on Introduction to
Your First Data Science
Project
Emma Grasmeder
ThoughtWorks_de Data Witch

2. A Hands on Introduction to
Your First Data Science
Project
Emma Grasmeder
ThoughtWorks_de Data Witch

3. What is data science

5. Once again, what is data
science

10. Don’t worry about that

11. Don’t worry about that
yet

12. So how do you do data
science

13. So how do you do data
science
You read the documentation

15. Don’t worry about algorithms,
either

16. Don’t worry about algorithms,
either (yet)

19. It’s important to have
conversations about how AI
is out to get you

20. It’s important to have
conversations about how AI
is out to get you
but this talk is about how the
data is also out to get you

21. Let’s talk about bias

22. Let’s talk about bias
You’re more likely to die in a car on the
way to the airport than in the airplane.

23. Let’s talk about bias
You’re more likely to die in a car on the
way to the airport than in the airplane.
So why are people more afraid of
airplanes?

24. Let’s talk about bias
The Wikipedia article about cognitive
biases lists more than 150 distinct
forms of cognitive bias

25. Let’s talk about bias
Airplane crashes are often scarier
because they’re more memorable.

26. Let’s talk about bias
Airplane crashes are often scarier
because they’re more memorable.
Because of availability bias you’re more
likely to believe something is true if they
are easier to remember.

27. Let’s talk about bias

28. If you want to analyze data objectively,
you need to know your biases first.
Let’s talk about bias

29. If you want to analyze data objectively,
you need to know your biases first.
Your brain is also kinda out to get you.
Let’s talk about bias

30. Here’s a cute
lion cub

31. Lies, damned lies, and statistics

32. Lies, damned lies, and statistics
“You’re more likely to die in a car on the
way to the airport than in the airplane.”

33. Lies, damned lies, and statistics
“You’re more likely to die in a car on the
way to the airport than in the airplane.”
Who believes this?

34. Lies, damned lies, and statistics
“You’re more likely to die in a car on the
way to the airport than in the airplane.”
Do you have any follow up questions?

35. Lies, damned lies, and statistics
You’re more likely to die in a car than in an
airplane:
- Per kilometer or per minute?
- Is this considering both commercial airlines
and private flights?
- Is this true for urban and city dwellers alike?
- Where is the data to support this claim?

36. Numbers don’t tell the whole truth

37. Numbers don’t tell the whole truth

38. Numbers don’t tell the whole truth
Data: U.S. National Center for Health Statistics (1994-2003); U.S. Social Security Administration (2004-2014)

39. Birthdays in India
Numbers don’t tell the whole truth

40. Numbers don’t tell the whole truth

41. Numbers don’t tell the whole truth

42. Numbers don’t tell the whole truth

43. Numbers don’t tell the whole truth
http://tylervigen.com/spurious-correlations

44. The
Anscombe
Quartet
Numbers don’t tell the whole truth

45. Numbers don’t tell the whole truth

46. A picture’s worth a thousand lies

47. A picture’s worth a thousand lies
Copyright © 2007
Stephen Few, Perceptual
Edge

48. A picture’s worth a thousand lies
Copyright © 2007 Stephen
Few, Perceptual Edge

49. A picture’s worth a thousand lies

50. A picture’s worth a thousand lies
Answer Miner: The Mystery of
Circle Visualization

51. A picture’s worth a thousand lies

52. Garbage in, garbage out
Meet Tay

53. Garbage in, garbage out
Tay is a real bot that Microsoft released
into the wild

54. Garbage in, garbage out
The internet ruined Tay

55. Garbage in, garbage out
This is called Weaponized Design
Tay

56. Garbage in, garbage out
Even when your data is well structured,
your model will be biased through things
like:

57. Garbage in, garbage out
Even when your data is well structured,
your model will be biased through things
like:
- Collection

58. Garbage in, garbage out
Even when your data is well structured,
your model will be biased through things
like:
- Collection
- Cleaning

59. Garbage in, garbage out
Even when your data is well structured,
your model will be biased through things
like:
- Collection
- Cleaning
- Feature selection

60. Garbage in, garbage out
Your first attempt at data science is
going to be kind of a mess.

61. Garbage in, garbage out
Your first attempt at data science is
going to be kind of a mess.
The entire field of data science is kinda a
mess.

62. Garbage in, garbage out
Your first attempt at data science is
going to be kind of a mess.
The entire field of data science is kinda a
mess.
(like much of the tech ecosystem)

63. Garbage in, garbage out
Your second attempt at data science is
going to be less of a mess (probably).

64. Garbage in, garbage out
Your second attempt at data science is
going to be less of a mess (probably).
If Tay teaches us anything, you’re never
too big or too experienced to mess up

65. If you’re feeling paranoid
about data, I’ve
accomplished my goal

66. Science is full of danger

67. Even though
everything is
out to get you,
go start
analyzing data

68. For questions & comments:
@emilyagras
egrasmed@thoughtworks.com
Thank you!