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

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C'est l'histoire d'un mec ... qui veut se mettre à la DataScience .

Après moult péripéties, je me suis lancé à corps perdu dans la Data Science au printemps 2017 : motivé et armé de MOOC, je suis parti à l’assaut des ressources pour être Data Scientist.

Ah mais au fait, ça veut dire quoi Data Scientist ? Scientifique de la donnée ?
Plus on apprend, plus on se rend compte de son ignorance. Beaucoup de vocabulaire et de jargon dans ce domaine ! Piouf, heureusement, sur mon chemin j’ai aussi appris sur moi-même, ma manière et mes capacités d’apprendre.

Vous voulez vous lancer vous aussi ? Mais ne savez pas par où commencer ? En fait, c’est simple. C’est l’histoire d’un mec...


Talk donné à la soirée Toulouse Data Science (TDS) du lundi 11 décembre 2017. : https://www.meetup.com/Tlse-Data-Science/events/245536866/

Plus d'infos sur mon blog : https://medium.com/@Maxime_/

Published in: Data & Analytics
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Se lancer dans la Data Science - Maxime Pawlak - TDS 11/12/2017

  1. 1. Se lancer dans la Data Science ...
  2. 2. Objectifs
  3. 3. Objectifs ● Démystifier ce domaine et son jargon pour les néophytes
  4. 4. Objectifs ● Démystifier ce domaine et son jargon pour les néophytes ● Partager les ressources utiles / inutiles pour débuter dans le domaine
  5. 5. 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
  6. 6. Roadmap
  7. 7. Roadmap 1. My journey
  8. 8. Roadmap 1. My journey 2. Mooc
  9. 9. Roadmap 1. My journey 2. Mooc 3. Ressources & Tips
  10. 10. Qui es Data Scientist ?
  11. 11. 1. C’est l’histoire d’un mec
  12. 12. Mathias @herberts
  13. 13. Mathias @herberts
  14. 14. Méthode CRISP-DM
  15. 15. Minute Papillote
  16. 16. Leonardo Noleto
  17. 17. OverFitting
  18. 18. ● en R
  19. 19. ● en R ● 5-7h /semaine
  20. 20. ● en R ● 5-7h /semaine ● 9 semaines de cours
  21. 21. ● en R ● 5-7h /semaine ● 9 semaines de cours ● Très concret
  22. 22. 1h tous les matins
  23. 23. Après la pluie ...
  24. 24. C’était le début de l’histoire
  25. 25. 2. MOOC
  26. 26. ● en R ● 5-7h /semaine ● 9 semaines de cours ● Très concret
  27. 27. 1. Intro
  28. 28. 1. Intro 2. Linear Regression
  29. 29. 1. Intro 2. Linear Regression ● MoneyBall ● MoneyBall in NBA
  30. 30. 1. Intro 2. Linear Regression ● MoneyBall ● MoneyBall in NBA 3. Logistic Regression ● Framingham Heart Study ● Election Forecasting
  31. 31. 1. Intro 2. Linear Regression ● MoneyBall ● MoneyBall in NBA 3. Logistic Regression ● Framingham Heart Study ● Election Forecasting 4. Trees ● Décisions de la cour suprême
  32. 32. 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
  33. 33. 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
  34. 34. 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
  35. 35. 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
  36. 36. 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
  37. 37. 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
  38. 38. 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
  39. 39. 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
  40. 40. ● Excellente introduction à la DS
  41. 41. ● Excellente introduction à la DS ● Très haut niveau : cas pratiques
  42. 42. ● Excellente introduction à la DS ● Très haut niveau : cas pratiques ● Dégrossir le vocabulaire
  43. 43. ● Excellente introduction à la DS ● Très haut niveau : cas pratiques ● Dégrossir le vocabulaire ● Intuiter certaines notions
  44. 44. ● Excellente introduction à la DS ● Très haut niveau : cas pratiques ● Dégrossir le vocabulaire ● Intuiter certaines notions ● Fil pédagogique bien mené
  45. 45. ● Slides #so2015 ○ mais remasterisé
  46. 46. ● Slides #so2015 ○ mais remasterisé ● On veut en savoir plus ! ○ Quelle est la magie ?
  47. 47. ● 11 semaines
  48. 48. ● 11 semaines ● 3-4h de cours
  49. 49. ● 11 semaines ● 3-4h de cours ● 3-5h de “homeworks”
  50. 50. ● 11 semaines ● 3-4h de cours ● 3-5h de “homeworks” ● Octave
  51. 51. ● 1. & 2. Linear Regression ○ Cost Function ○ Gradient Descent
  52. 52. ● 1. & 2. Linear Regression ○ Cost Function ○ Gradient Descent ● 3. Logistic Regression & Regularization
  53. 53. ● 1. & 2. Linear Regression ○ Cost Function ○ Gradient Descent ● 3. Logistic Regression & Regularization ● 4. & 5. Neural Networks ○ BackPropagation
  54. 54. ● 1. & 2. Linear Regression ○ Cost Function ○ Gradient Descent ● 3. Logistic Regression & Regularization ● 4. & 5. Neural Networks ○ BackPropagation ● 6. Advice for Applying Machine Learning
  55. 55. ● 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
  56. 56. ● 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
  57. 57. ● 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
  58. 58. ● 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
  59. 59. ● 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
  60. 60. ● Andrew est un cador et extrêmement pédagogue
  61. 61. ● Andrew est un cador et extrêmement pédagogue ● Comprend les mécanismes sous-jacents ○ Gradient Descent ○ back propagation …
  62. 62. ● Andrew est un cador et extrêmement pédagogue ● Comprend les mécanismes sous-jacents ○ Gradient Descent ○ back propagation … ● Pas mal de math
  63. 63. ● 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
  64. 64. ● Mooc enregistré avec sa webcam, dans un placard
  65. 65. Udacity
  66. 66. NanoDegree
  67. 67. Bilan des MOOC
  68. 68. 3. Ressources & Tips
  69. 69. Overfitting !
  70. 70. Siraj Raval
  71. 71. Pocket
  72. 72. CS231 - Stanford
  73. 73. Learning how to learn
  74. 74. https://medium.com/@Maxime_
  75. 75. Speed x1.25
  76. 76. 3. Ressources & Tips ● Kaggle ● Veille ● Youtube ● Communauté de DataScience
  77. 77. 3. Ressources & Tips LAST BUT NOT LEAST
  78. 78. Leonardo
  79. 79. Leonardo Alexia
  80. 80. Leonardo Alexia Ivan
  81. 81. Leonardo Alexia Ivan Sam Hee
  82. 82. 1. My journey 2. Mooc 3. Ressources & Tips
  83. 83. https://jeanjo.xyz
  84. 84. @Maxime_Pawlak

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