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