Download as PDF, PPTX
![map('state', region = c(row.names(USArrests)), col=cm.colors(16, 1)[floor(USArrests$Rape/max(USArrests$Rape)*28)], fill=T)](https://image.slidesharecdn.com/datakeynotebelgium-131017190350-phpapp02/75/Data-Science-what-even-103-2048.jpg)
![from scipy.stats import
describe
s = [1,2,1,3,4,5]
print describe(s)](https://image.slidesharecdn.com/datakeynotebelgium-131017190350-phpapp02/75/Data-Science-what-even-108-2048.jpg)
![import scipy.stats.poisson
p = poisson.pmf([1,2,3,4,1,2,3], 2)](https://image.slidesharecdn.com/datakeynotebelgium-131017190350-phpapp02/75/Data-Science-what-even-112-2048.jpg)
![import numpy as np
x = np.array([ [1, 0], [0, 1] ])
vec, val = np.linalg.eig(x)
np.linalg.eigvals(x)](https://image.slidesharecdn.com/datakeynotebelgium-131017190350-phpapp02/75/Data-Science-what-even-117-2048.jpg)
![>>> np.linalg.eig(x)
(
array([ 1., 1.]),
array([
[ 1., 0.],
[ 0., 1.]
])
)](https://image.slidesharecdn.com/datakeynotebelgium-131017190350-phpapp02/75/Data-Science-what-even-118-2048.jpg)
![from sklearn import tree
X = [[0, 0], [1, 1]]
Y = [0, 1]
clf = tree.DecisionTreeClassifier()
clf = clf.fit(X, Y)
clf.predict([[2., 2.]])
>>> array([1])](https://image.slidesharecdn.com/datakeynotebelgium-131017190350-phpapp02/75/Data-Science-what-even-123-2048.jpg)
![from pandas import *
x = DataFrame([
{"age": 26},
{"age": 19},
{"age": 21},
{"age": 18}
])
print x[x['age'] > 20].count()
print x[x['age'] > 20].mean()](https://image.slidesharecdn.com/datakeynotebelgium-131017190350-phpapp02/75/Data-Science-what-even-130-2048.jpg)
Uploaded byDavid Coallier
Data Science, what even...
The document discusses the field of data science, emphasizing the importance of a scientific method that includes forming hypotheses, analyzing data, and deriving actionable insights. It highlights various tools and languages like R and Python that are essential for data scientists, including libraries for statistical analysis and machine learning. The author stresses that understanding data is not just about statistics but also involves human interpretation and emotional connections to results.
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- 5. And I cook.. Alot.
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- 11. Young mathematically inclinedminds We knew everything.
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- 13. So we asked“experts”.
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- 20. From Our Results Wehad questions.
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- 23. Find a Question YourHypothesis
- 24. Current Data What doyou have?
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- 26. Analyse Results Won’t bepretty.
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- 29. Good Discussions Imply gooddata scientists
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- 38. 1. Hacking 2. Maths& Stats 3. Expertise
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- 40. 1. Question 2. CurrentData 3. Features/Tests 4. Analyse 5. Converse
- 41. Find a Question Let’simagine Github
- 42. Upgrade Repos Affect usersas little as possible
- 43. import csv content =csv.read('repo1.csv')
- 44. λ e f (k;λ ) = k! k −k for k >= 0
- 46.
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- 48. KPIs are key Indicatorsfrom experience
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- 54. We are Human. EmotionalConnection
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- 57. Focus on Data Relevantto your KPIs.
- 59. Data gives youthe what Humans give you the why
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- 103. map('state', region =c(row.names(USArrests)), col=cm.colors(16, 1)[floor(USArrests$Rape/max(USArrests$Rape)*28)], fill=T)
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- 108. from scipy.stats import describe s= [1,2,1,3,4,5] print describe(s)
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- 111. λ e f (k;λ ) = k! k −k for k >= 0
- 112. import scipy.stats.poisson p =poisson.pmf([1,2,3,4,1,2,3], 2)
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- 116. ⎛ 1 0⎞ ⎜ 0 1 ⎟ ⎝ ⎠
- 117. import numpy asnp x = np.array([ [1, 0], [0, 1] ]) vec, val = np.linalg.eig(x) np.linalg.eigvals(x)
- 118. >>> np.linalg.eig(x) ( array([ 1.,1.]), array([ [ 1., 0.], [ 0., 1.] ]) )
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- 123. from sklearn importtree X = [[0, 0], [1, 1]] Y = [0, 1] clf = tree.DecisionTreeClassifier() clf = clf.fit(X, Y) clf.predict([[2., 2.]]) >>> array([1])
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- 126. Pattern Web Mining forPython
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- 130. from pandas import* x = DataFrame([ {"age": 26}, {"age": 19}, {"age": 21}, {"age": 18} ]) print x[x['age'] > 20].count() print x[x['age'] > 20].mean()
- 131. Python vs R? DifferentPurposes
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- 153. Edward Tufte Go readhis books.
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- 155. Original Question What isData Science?
- 156. Back to you Forquestioning