Data Analytics with Python

Data Analytics with Python

IIT Roorkee July 2018 via YouTube Direct link

Data Analytics with Python

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Data Analytics with Python

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Data Analytics with Python

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  1. 1 Data Analytics with Python
  2. 2 Lec 1, Introduction to Data Analytics
  3. 3 Lec 2, Python Fundamentals -I
  4. 4 Lec 3, Python Fundamentals -II
  5. 5 Lec 4, Central Tendency and Dispersion - I
  6. 6 Lec 5, Central Tendency and Dispersion - II
  7. 7 Lec 6, Introduction to Probability-I
  8. 8 Lec 7, Introduction to Probability-II
  9. 9 Lec 8, Probability Distribution - I
  10. 10 Lec 9, Probability Distribution - II
  11. 11 Lec 10, Probability Distributions - III
  12. 12 Lecture 11, Python Demo for Distribution
  13. 13 Lec 12, Sampling and Sampling Distribution
  14. 14 Lec 13, Distribution of Sample Means, population, and variance
  15. 15 Lec 14: Confidence interval estimation: Single population - I
  16. 16 Lec 15, Confidence Interval Estimation: Single Population - II
  17. 17 Lec 16, Hypothesis Testing- I
  18. 18 Lec 17, Hypothesis testing- II
  19. 19 Lec 18, Hypothesis Testing-III
  20. 20 Lec 19, Errors in Hypothesis Testing
  21. 21 Lec 20, Hypothesis Testing about the Difference in Two Sample Means
  22. 22 Lec 21, Hypothesis testing : Two sample test -II
  23. 23 Lec 22, Hypothesis Testing: Two sample test - III
  24. 24 Lec 23, ANOVA- I
  25. 25 Lec 24, ANOVA- II
  26. 26 Lec 25, Post Hoc Analysis(Tukey’s test)
  27. 27 Lec 26, Randomize block design (RBD)
  28. 28 Lec 27, Two Way ANOVA
  29. 29 Lec 28, Linear Regression - I
  30. 30 Lec 29, Linear Regression - II
  31. 31 Lec 30, Linear Regression-III
  32. 32 Lec 31, Estimation, Prediction of Regression Model Residual Analysis
  33. 33 Lec 32, Estimation, Prediction of Regression Model Residual Analysis - II
  34. 34 Lec 33, MULTIPLE REGRESSION MODEL - I
  35. 35 Lec 34, MULTIPLE REGRESSION MODEL-II
  36. 36 Lec 35, Categorical variable regression
  37. 37 Lec 36, Maximum Likelihood Estimation- I
  38. 38 Lec 37, Maximum Likelihood Estimation-II
  39. 39 Lec 38, LOGISTIC REGRESSION- I
  40. 40 Lec 39, LOGISTIC REGRESSION-II
  41. 41 Lec 40, Linear Regression Model Vs Logistic Regression Model
  42. 42 Lec 41, Confusion matrix and ROC- I
  43. 43 Lec 42, Confusion Matrix and ROC-II
  44. 44 Lec 43, Performance of Logistic Model-III
  45. 45 Lec 44, Regression Analysis Model Building - I
  46. 46 Lec 45, Regression Analysis Model Building (Interaction)- II
  47. 47 Lec 46, Chi - Square Test of Independence - I
  48. 48 Lec 47, Chi-Square Test of Independence - II
  49. 49 Lec 48, Chi-Square Goodness of Fit Test
  50. 50 Lec 49, Cluster analysis: Introduction- I
  51. 51 Lec 50, Clustering analysis: part II
  52. 52 Lec 51, Clustering analysis: Part III
  53. 53 Lec 52, Cluster analysis: Part IV
  54. 54 Lec 53, Cluster analysis: Part V
  55. 55 Lec 54, K- Means Clustering
  56. 56 Lec 55, Hierarchical method of clustering -I
  57. 57 Lec 56, Hierarchical method of clustering- II
  58. 58 Lec 57, Classification and Regression Trees (CART : I)
  59. 59 Lec 58, Measures of attribute selection
  60. 60 Lec 59, Attribute selection Measures in CART : II
  61. 61 Lec 60, Classification and Regression Trees (CART) - III

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