Se lancer dans la Data Science - Maxime Pawlak - TDS 11/12/2017

Se lancer dans la Data Science ...
Se lancer dans la Data Science - Maxime Pawlak - TDS 11/12/2017
Objectifs
Objectifs
● Démystifier ce domaine et son jargon pour les
néophytes
Objectifs
● Démystifier ce domaine et son jargon pour les
néophytes
● Partager les ressources utiles / inutiles pour
débuter dans le domaine
Objectifs
● Démystifier ce domaine et son jargon pour les
néophytes
● Partager les ressources utiles / inutiles pour
débuter dans le domaine
● Aider les gens qui se posent la question à
entamer ou pas une reconversion
Roadmap
Roadmap
1. My journey
Roadmap
1. My journey
2. Mooc
Roadmap
1. My journey
2. Mooc
3. Ressources & Tips
Qui es
Data Scientist ?
1. C’est l’histoire d’un mec
Se lancer dans la Data Science - Maxime Pawlak - TDS 11/12/2017
Se lancer dans la Data Science - Maxime Pawlak - TDS 11/12/2017
Mathias @herberts
Mathias @herberts
Méthode CRISP-DM
Se lancer dans la Data Science - Maxime Pawlak - TDS 11/12/2017
Minute Papillote
Se lancer dans la Data Science - Maxime Pawlak - TDS 11/12/2017
Se lancer dans la Data Science - Maxime Pawlak - TDS 11/12/2017
Leonardo Noleto
OverFitting
Se lancer dans la Data Science - Maxime Pawlak - TDS 11/12/2017
● en R
● en R
● 5-7h /semaine
● en R
● 5-7h /semaine
● 9 semaines de cours
● en R
● 5-7h /semaine
● 9 semaines de cours
● Très concret
1h tous les matins
Se lancer dans la Data Science - Maxime Pawlak - TDS 11/12/2017
Se lancer dans la Data Science - Maxime Pawlak - TDS 11/12/2017
Se lancer dans la Data Science - Maxime Pawlak - TDS 11/12/2017
Se lancer dans la Data Science - Maxime Pawlak - TDS 11/12/2017
Se lancer dans la Data Science - Maxime Pawlak - TDS 11/12/2017
Se lancer dans la Data Science - Maxime Pawlak - TDS 11/12/2017
Se lancer dans la Data Science - Maxime Pawlak - TDS 11/12/2017
Se lancer dans la Data Science - Maxime Pawlak - TDS 11/12/2017
Après la pluie ...
Se lancer dans la Data Science - Maxime Pawlak - TDS 11/12/2017
Se lancer dans la Data Science - Maxime Pawlak - TDS 11/12/2017
C’était le début de l’histoire
2. MOOC
Se lancer dans la Data Science - Maxime Pawlak - TDS 11/12/2017
Se lancer dans la Data Science - Maxime Pawlak - TDS 11/12/2017
Se lancer dans la Data Science - Maxime Pawlak - TDS 11/12/2017
● en R
● 5-7h /semaine
● 9 semaines de cours
● Très concret
1. Intro
1. Intro
2. Linear Regression
Se lancer dans la Data Science - Maxime Pawlak - TDS 11/12/2017
1. Intro
2. Linear Regression
● MoneyBall
● MoneyBall in NBA
1. Intro
2. Linear Regression
● MoneyBall
● MoneyBall in NBA
3. Logistic Regression
● Framingham Heart Study
● Election Forecasting
1. Intro
2. Linear Regression
● MoneyBall
● MoneyBall in NBA
3. Logistic Regression
● Framingham Heart Study
● Election Forecasting
4. Trees
● Décisions de la cour suprême
1. Intro
2. Linear Regression
● MoneyBall
● MoneyBall in NBA
3. Logistic Regression
● Framingham Heart Study
● Election Forecasting
4. Trees
● Décisions de la cour suprême
5. Text analytics
● Tweets : pro ou con apple
Se lancer dans la Data Science - Maxime Pawlak - TDS 11/12/2017
1. Intro
2. Linear Regression
● MoneyBall
● MoneyBall in NBA
3. Logistic Regression
● Framingham Heart Study
● Election Forecasting
4. Trees
● Décisions de la cour suprême
5. Text analytics
● Tweets : pro ou con apple
● Watson in Jeopardy
● Enron : Predictive coding
6. Clustering1. Intro
2. Linear Regression
● MoneyBall
● MoneyBall in NBA
3. Logistic Regression
● Framingham Heart Study
● Election Forecasting
4. Trees
● Décisions de la cour suprême
5. Text analytics
● Tweets : pro ou con apple
● Watson in Jeopardy
● Enron : Predictive coding
Se lancer dans la Data Science - Maxime Pawlak - TDS 11/12/2017
6. Clustering
● Netflix recommendation
● Predictive Diagnosis
1. Intro
2. Linear Regression
● MoneyBall
● MoneyBall in NBA
3. Logistic Regression
● Framingham Heart Study
● Election Forecasting
4. Trees
● Décisions de la cour suprême
5. Text analytics
● Tweets : pro ou con apple
● Watson in Jeopardy
● Enron : Predictive coding
6. Clustering
● Netflix recommendation
● Predictive Diagnosis
7. Visualization
● Analytical Policeman (LA)
1. Intro
2. Linear Regression
● MoneyBall
● MoneyBall in NBA
3. Logistic Regression
● Framingham Heart Study
● Election Forecasting
4. Trees
● Décisions de la cour suprême
5. Text analytics
● Tweets : pro ou con apple
● Watson in Jeopardy
● Enron : Predictive coding
6. Clustering
● Netflix recommendation
● Predictive Diagnosis
7. Visualization
● Analytical Policeman (LA)
8. Linear Optimization
● Airline Management Revue
● Radiation Therapy
● Optimisation Usine
1. Intro
2. Linear Regression
● MoneyBall
● MoneyBall in NBA
3. Logistic Regression
● Framingham Heart Study
● Election Forecasting
4. Trees
● Décisions de la cour suprême
5. Text analytics
● Tweets : pro ou con apple
● Watson in Jeopardy
● Enron : Predictive coding
6. Clustering
● Netflix recommendation
● Predictive Diagnosis
7. Visualization
● Analytical Policeman (LA)
8. Linear Optimization
● Airline Management Revue
● Radiation Therapy
● Optimisation Usine
1. Intro
2. Linear Regression
● MoneyBall
● MoneyBall in NBA
3. Logistic Regression
● Framingham Heart Study
● Election Forecasting
4. Trees
● Décisions de la cour suprême
5. Text analytics
● Tweets : pro ou con apple
● Watson in Jeopardy
● Enron : Predictive coding
6. Clustering
● Netflix recommendation
● Predictive Diagnosis
7. Visualization
● Analytical Policeman (LA)
8. Linear Optimization
● Airline Management Revue
● Radiation Therapy
● Optimisation Usine
9. Integer Optimization
● Sports Scheduling
● Operating Room Scheduling
1. Intro
2. Linear Regression
● MoneyBall
● MoneyBall in NBA
3. Logistic Regression
● Framingham Heart Study
● Election Forecasting
4. Trees
● Décisions de la cour suprême
5. Text analytics
● Tweets : pro ou con apple
● Watson in Jeopardy
● Enron : Predictive coding
Se lancer dans la Data Science - Maxime Pawlak - TDS 11/12/2017
● Excellente introduction à la DS
● Excellente introduction à la DS
● Très haut niveau : cas pratiques
● Excellente introduction à la DS
● Très haut niveau : cas pratiques
● Dégrossir le vocabulaire
● Excellente introduction à la DS
● Très haut niveau : cas pratiques
● Dégrossir le vocabulaire
● Intuiter certaines notions
● Excellente introduction à la DS
● Très haut niveau : cas pratiques
● Dégrossir le vocabulaire
● Intuiter certaines notions
● Fil pédagogique bien mené
Se lancer dans la Data Science - Maxime Pawlak - TDS 11/12/2017
● Slides #so2015
○ mais remasterisé
● Slides #so2015
○ mais remasterisé
● On veut en savoir plus !
○ Quelle est la magie ?
Se lancer dans la Data Science - Maxime Pawlak - TDS 11/12/2017
Se lancer dans la Data Science - Maxime Pawlak - TDS 11/12/2017
Se lancer dans la Data Science - Maxime Pawlak - TDS 11/12/2017
● 11 semaines
● 11 semaines
● 3-4h de cours
● 11 semaines
● 3-4h de cours
● 3-5h de “homeworks”
● 11 semaines
● 3-4h de cours
● 3-5h de “homeworks”
● Octave
● 1. & 2. Linear Regression
○ Cost Function
○ Gradient Descent
● 1. & 2. Linear Regression
○ Cost Function
○ Gradient Descent
● 3. Logistic Regression &
Regularization
● 1. & 2. Linear Regression
○ Cost Function
○ Gradient Descent
● 3. Logistic Regression &
Regularization
● 4. & 5. Neural Networks
○ BackPropagation
● 1. & 2. Linear Regression
○ Cost Function
○ Gradient Descent
● 3. Logistic Regression &
Regularization
● 4. & 5. Neural Networks
○ BackPropagation
● 6. Advice for Applying
Machine Learning
● 1. & 2. Linear Regression
○ Cost Function
○ Gradient Descent
● 3. Logistic Regression &
Regularization
● 4. & 5. Neural Networks
○ BackPropagation
● 6. Advice for Applying
Machine Learning
● 7. Support Vector Machines
● 1. & 2. Linear Regression
○ Cost Function
○ Gradient Descent
● 3. Logistic Regression &
Regularization
● 4. & 5. Neural Networks
○ BackPropagation
● 6. Advice for Applying
Machine Learning
● 7. Support Vector Machines
● 8. Unsupervised Learning &
Dimensionality Reduction
○ PCA
● 1. & 2. Linear Regression
○ Cost Function
○ Gradient Descent
● 3. Logistic Regression &
Regularization
● 4. & 5. Neural Networks
○ BackPropagation
● 6. Advice for Applying
Machine Learning
● 7. Support Vector Machines
● 8. Unsupervised Learning &
Dimensionality Reduction
○ PCA
● 9. Anomaly Detection &
Recommender Systems
○ Gaussian Distribution
● 1. & 2. Linear Regression
○ Cost Function
○ Gradient Descent
● 3. Logistic Regression &
Regularization
● 4. & 5. Neural Networks
○ BackPropagation
● 6. Advice for Applying
Machine Learning
● 7. Support Vector Machines
● 8. Unsupervised Learning &
Dimensionality Reduction
○ PCA
● 9. Anomaly Detection &
Recommender Systems
○ Gaussian Distribution
● 10. Large Scale Machine
Learning
○ Stochastic Gradient Descent
○ Mini-batch Gradient Descent
● 1. & 2. Linear Regression
○ Cost Function
○ Gradient Descent
● 3. Logistic Regression &
Regularization
● 4. & 5. Neural Networks
○ BackPropagation
● 6. Advice for Applying
Machine Learning
● 7. Support Vector Machines
● 8. Unsupervised Learning &
Dimensionality Reduction
○ PCA
● 9. Anomaly Detection &
Recommender Systems
○ Gaussian Distribution
● 10. Large Scale Machine
Learning
○ Stochastic Gradient Descent
○ Mini-batch Gradient Descent
● 11. Photo OCR
Se lancer dans la Data Science - Maxime Pawlak - TDS 11/12/2017
● Andrew est un cador et extrêmement
pédagogue
● Andrew est un cador et extrêmement
pédagogue
● Comprend les mécanismes sous-jacents
○ Gradient Descent
○ back propagation …
● Andrew est un cador et extrêmement
pédagogue
● Comprend les mécanismes sous-jacents
○ Gradient Descent
○ back propagation …
● Pas mal de math
● Andrew est un cador et extrêmement
pédagogue
● Comprend les mécanismes sous-jacents
○ Gradient Descent
○ back propagation …
● Pas mal de math
● C'est un vieux sujet mais tellement de
choses à faire
Se lancer dans la Data Science - Maxime Pawlak - TDS 11/12/2017
● Mooc enregistré avec sa
webcam, dans un placard
Udacity
NanoDegree
Bilan des MOOC
3. Ressources & Tips
Se lancer dans la Data Science - Maxime Pawlak - TDS 11/12/2017
Se lancer dans la Data Science - Maxime Pawlak - TDS 11/12/2017
Se lancer dans la Data Science - Maxime Pawlak - TDS 11/12/2017
Se lancer dans la Data Science - Maxime Pawlak - TDS 11/12/2017
Overfitting !
Se lancer dans la Data Science - Maxime Pawlak - TDS 11/12/2017
Siraj Raval
Se lancer dans la Data Science - Maxime Pawlak - TDS 11/12/2017
Pocket
CS231 - Stanford
Se lancer dans la Data Science - Maxime Pawlak - TDS 11/12/2017
Se lancer dans la Data Science - Maxime Pawlak - TDS 11/12/2017
Learning how to learn
https://medium.com/@Maxime_
Speed x1.25
3. Ressources & Tips
● Kaggle
● Veille
● Youtube
● Communauté de DataScience
3. Ressources & Tips
LAST BUT NOT
LEAST
Se lancer dans la Data Science - Maxime Pawlak - TDS 11/12/2017
Leonardo
Leonardo
Alexia
Leonardo
Alexia
Ivan
Leonardo
Alexia
Ivan
Sam Hee
Se lancer dans la Data Science - Maxime Pawlak - TDS 11/12/2017
1. My journey
2. Mooc
3. Ressources & Tips
Se lancer dans la Data Science - Maxime Pawlak - TDS 11/12/2017
Se lancer dans la Data Science - Maxime Pawlak - TDS 11/12/2017
https://jeanjo.xyz
Se lancer dans la Data Science - Maxime Pawlak - TDS 11/12/2017
@Maxime_Pawlak
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Se lancer dans la Data Science - Maxime Pawlak - TDS 11/12/2017

  • 1. Se lancer dans la Data Science ...
  • 4. Objectifs ● Démystifier ce domaine et son jargon pour les néophytes
  • 5. Objectifs ● Démystifier ce domaine et son jargon pour les néophytes ● Partager les ressources utiles / inutiles pour débuter dans le domaine
  • 6. Objectifs ● Démystifier ce domaine et son jargon pour les néophytes ● Partager les ressources utiles / inutiles pour débuter dans le domaine ● Aider les gens qui se posent la question à entamer ou pas une reconversion
  • 10. Roadmap 1. My journey 2. Mooc 3. Ressources & Tips
  • 26. ● en R ● 5-7h /semaine
  • 27. ● en R ● 5-7h /semaine ● 9 semaines de cours
  • 28. ● en R ● 5-7h /semaine ● 9 semaines de cours ● Très concret
  • 29. 1h tous les matins
  • 41. C’était le début de l’histoire
  • 46. ● en R ● 5-7h /semaine ● 9 semaines de cours ● Très concret
  • 48. 1. Intro 2. Linear Regression
  • 50. 1. Intro 2. Linear Regression ● MoneyBall ● MoneyBall in NBA
  • 51. 1. Intro 2. Linear Regression ● MoneyBall ● MoneyBall in NBA 3. Logistic Regression ● Framingham Heart Study ● Election Forecasting
  • 52. 1. Intro 2. Linear Regression ● MoneyBall ● MoneyBall in NBA 3. Logistic Regression ● Framingham Heart Study ● Election Forecasting 4. Trees ● Décisions de la cour suprême
  • 53. 1. Intro 2. Linear Regression ● MoneyBall ● MoneyBall in NBA 3. Logistic Regression ● Framingham Heart Study ● Election Forecasting 4. Trees ● Décisions de la cour suprême 5. Text analytics ● Tweets : pro ou con apple
  • 55. 1. Intro 2. Linear Regression ● MoneyBall ● MoneyBall in NBA 3. Logistic Regression ● Framingham Heart Study ● Election Forecasting 4. Trees ● Décisions de la cour suprême 5. Text analytics ● Tweets : pro ou con apple ● Watson in Jeopardy ● Enron : Predictive coding
  • 56. 6. Clustering1. Intro 2. Linear Regression ● MoneyBall ● MoneyBall in NBA 3. Logistic Regression ● Framingham Heart Study ● Election Forecasting 4. Trees ● Décisions de la cour suprême 5. Text analytics ● Tweets : pro ou con apple ● Watson in Jeopardy ● Enron : Predictive coding
  • 58. 6. Clustering ● Netflix recommendation ● Predictive Diagnosis 1. Intro 2. Linear Regression ● MoneyBall ● MoneyBall in NBA 3. Logistic Regression ● Framingham Heart Study ● Election Forecasting 4. Trees ● Décisions de la cour suprême 5. Text analytics ● Tweets : pro ou con apple ● Watson in Jeopardy ● Enron : Predictive coding
  • 59. 6. Clustering ● Netflix recommendation ● Predictive Diagnosis 7. Visualization ● Analytical Policeman (LA) 1. Intro 2. Linear Regression ● MoneyBall ● MoneyBall in NBA 3. Logistic Regression ● Framingham Heart Study ● Election Forecasting 4. Trees ● Décisions de la cour suprême 5. Text analytics ● Tweets : pro ou con apple ● Watson in Jeopardy ● Enron : Predictive coding
  • 60. 6. Clustering ● Netflix recommendation ● Predictive Diagnosis 7. Visualization ● Analytical Policeman (LA) 8. Linear Optimization ● Airline Management Revue ● Radiation Therapy ● Optimisation Usine 1. Intro 2. Linear Regression ● MoneyBall ● MoneyBall in NBA 3. Logistic Regression ● Framingham Heart Study ● Election Forecasting 4. Trees ● Décisions de la cour suprême 5. Text analytics ● Tweets : pro ou con apple ● Watson in Jeopardy ● Enron : Predictive coding
  • 61. 6. Clustering ● Netflix recommendation ● Predictive Diagnosis 7. Visualization ● Analytical Policeman (LA) 8. Linear Optimization ● Airline Management Revue ● Radiation Therapy ● Optimisation Usine 1. Intro 2. Linear Regression ● MoneyBall ● MoneyBall in NBA 3. Logistic Regression ● Framingham Heart Study ● Election Forecasting 4. Trees ● Décisions de la cour suprême 5. Text analytics ● Tweets : pro ou con apple ● Watson in Jeopardy ● Enron : Predictive coding
  • 62. 6. Clustering ● Netflix recommendation ● Predictive Diagnosis 7. Visualization ● Analytical Policeman (LA) 8. Linear Optimization ● Airline Management Revue ● Radiation Therapy ● Optimisation Usine 9. Integer Optimization ● Sports Scheduling ● Operating Room Scheduling 1. Intro 2. Linear Regression ● MoneyBall ● MoneyBall in NBA 3. Logistic Regression ● Framingham Heart Study ● Election Forecasting 4. Trees ● Décisions de la cour suprême 5. Text analytics ● Tweets : pro ou con apple ● Watson in Jeopardy ● Enron : Predictive coding
  • 65. ● Excellente introduction à la DS ● Très haut niveau : cas pratiques
  • 66. ● Excellente introduction à la DS ● Très haut niveau : cas pratiques ● Dégrossir le vocabulaire
  • 67. ● Excellente introduction à la DS ● Très haut niveau : cas pratiques ● Dégrossir le vocabulaire ● Intuiter certaines notions
  • 68. ● Excellente introduction à la DS ● Très haut niveau : cas pratiques ● Dégrossir le vocabulaire ● Intuiter certaines notions ● Fil pédagogique bien mené
  • 70. ● Slides #so2015 ○ mais remasterisé
  • 71. ● Slides #so2015 ○ mais remasterisé ● On veut en savoir plus ! ○ Quelle est la magie ?
  • 76. ● 11 semaines ● 3-4h de cours
  • 77. ● 11 semaines ● 3-4h de cours ● 3-5h de “homeworks”
  • 78. ● 11 semaines ● 3-4h de cours ● 3-5h de “homeworks” ● Octave
  • 79. ● 1. & 2. Linear Regression ○ Cost Function ○ Gradient Descent
  • 80. ● 1. & 2. Linear Regression ○ Cost Function ○ Gradient Descent ● 3. Logistic Regression & Regularization
  • 81. ● 1. & 2. Linear Regression ○ Cost Function ○ Gradient Descent ● 3. Logistic Regression & Regularization ● 4. & 5. Neural Networks ○ BackPropagation
  • 82. ● 1. & 2. Linear Regression ○ Cost Function ○ Gradient Descent ● 3. Logistic Regression & Regularization ● 4. & 5. Neural Networks ○ BackPropagation ● 6. Advice for Applying Machine Learning
  • 83. ● 1. & 2. Linear Regression ○ Cost Function ○ Gradient Descent ● 3. Logistic Regression & Regularization ● 4. & 5. Neural Networks ○ BackPropagation ● 6. Advice for Applying Machine Learning ● 7. Support Vector Machines
  • 84. ● 1. & 2. Linear Regression ○ Cost Function ○ Gradient Descent ● 3. Logistic Regression & Regularization ● 4. & 5. Neural Networks ○ BackPropagation ● 6. Advice for Applying Machine Learning ● 7. Support Vector Machines ● 8. Unsupervised Learning & Dimensionality Reduction ○ PCA
  • 85. ● 1. & 2. Linear Regression ○ Cost Function ○ Gradient Descent ● 3. Logistic Regression & Regularization ● 4. & 5. Neural Networks ○ BackPropagation ● 6. Advice for Applying Machine Learning ● 7. Support Vector Machines ● 8. Unsupervised Learning & Dimensionality Reduction ○ PCA ● 9. Anomaly Detection & Recommender Systems ○ Gaussian Distribution
  • 86. ● 1. & 2. Linear Regression ○ Cost Function ○ Gradient Descent ● 3. Logistic Regression & Regularization ● 4. & 5. Neural Networks ○ BackPropagation ● 6. Advice for Applying Machine Learning ● 7. Support Vector Machines ● 8. Unsupervised Learning & Dimensionality Reduction ○ PCA ● 9. Anomaly Detection & Recommender Systems ○ Gaussian Distribution ● 10. Large Scale Machine Learning ○ Stochastic Gradient Descent ○ Mini-batch Gradient Descent
  • 87. ● 1. & 2. Linear Regression ○ Cost Function ○ Gradient Descent ● 3. Logistic Regression & Regularization ● 4. & 5. Neural Networks ○ BackPropagation ● 6. Advice for Applying Machine Learning ● 7. Support Vector Machines ● 8. Unsupervised Learning & Dimensionality Reduction ○ PCA ● 9. Anomaly Detection & Recommender Systems ○ Gaussian Distribution ● 10. Large Scale Machine Learning ○ Stochastic Gradient Descent ○ Mini-batch Gradient Descent ● 11. Photo OCR
  • 89. ● Andrew est un cador et extrêmement pédagogue
  • 90. ● Andrew est un cador et extrêmement pédagogue ● Comprend les mécanismes sous-jacents ○ Gradient Descent ○ back propagation …
  • 91. ● Andrew est un cador et extrêmement pédagogue ● Comprend les mécanismes sous-jacents ○ Gradient Descent ○ back propagation … ● Pas mal de math
  • 92. ● Andrew est un cador et extrêmement pédagogue ● Comprend les mécanismes sous-jacents ○ Gradient Descent ○ back propagation … ● Pas mal de math ● C'est un vieux sujet mais tellement de choses à faire
  • 94. ● Mooc enregistré avec sa webcam, dans un placard
  • 107. Pocket
  • 111. Learning how to learn
  • 114. 3. Ressources & Tips ● Kaggle ● Veille ● Youtube ● Communauté de DataScience
  • 115. 3. Ressources & Tips LAST BUT NOT LEAST
  • 122. 1. My journey 2. Mooc 3. Ressources & Tips