Data Analytics with Python
4.0
(5)
45 learners
What you'll learn
This course includes
- 28 hours of video
- Certificate of completion
- Access on mobile and TV
Course content
1 modules • 61 lessons • 28 hours of video
Data Analytics with Python
61 lessons
• 28 hours
Data Analytics with Python
61 lessons
• 28 hours
- Data Analytics with Python 02:13
- Lec 1, Introduction to Data Analytics 34:44
- Lec 2, Python Fundamentals -I 26:30
- Lec 3, Python Fundamentals -II 36:35
- Lec 4, Central Tendency and Dispersion - I 31:47
- Lec 5, Central Tendency and Dispersion - II 32:36
- Lec 6, Introduction to Probability-I 28:19
- Lec 7, Introduction to Probability-II 29:14
- Lec 8, Probability Distribution - I 28:50
- Lec 9, Probability Distribution - II 29:34
- Lec 10, Probability Distributions - III 26:01
- Lecture 11, Python Demo for Distribution 21:15
- Lec 12, Sampling and Sampling Distribution 34:16
- Lec 13, Distribution of Sample Means, population, and variance 24:37
- Lec 14: Confidence interval estimation: Single population - I 26:03
- Lec 15, Confidence Interval Estimation: Single Population - II 19:48
- Lec 16, Hypothesis Testing- I 32:33
- Lec 17, Hypothesis testing- II 26:25
- Lec 18, Hypothesis Testing-III 25:31
- Lec 19, Errors in Hypothesis Testing 43:41
- Lec 20, Hypothesis Testing about the Difference in Two Sample Means 29:03
- Lec 21, Hypothesis testing : Two sample test -II 29:49
- Lec 22, Hypothesis Testing: Two sample test - III 25:02
- Lec 23, ANOVA- I 22:57
- Lec 24, ANOVA- II 22:58
- Lec 25, Post Hoc Analysis(Tukey’s test) 36:00
- Lec 26, Randomize block design (RBD) 25:59
- Lec 27, Two Way ANOVA 26:49
- Lec 28, Linear Regression - I 35:02
- Lec 29, Linear Regression - II 22:24
- Lec 30, Linear Regression-III 29:25
- Lec 31, Estimation, Prediction of Regression Model Residual Analysis 22:09
- Lec 32, Estimation, Prediction of Regression Model Residual Analysis - II 25:32
- Lec 33, MULTIPLE REGRESSION MODEL - I 30:09
- Lec 34, MULTIPLE REGRESSION MODEL-II 34:38
- Lec 35, Categorical variable regression 34:35
- Lec 36, Maximum Likelihood Estimation- I 25:49
- Lec 37, Maximum Likelihood Estimation-II 29:44
- Lec 38, LOGISTIC REGRESSION- I 28:26
- Lec 39, LOGISTIC REGRESSION-II 25:21
- Lec 40, Linear Regression Model Vs Logistic Regression Model 28:57
- Lec 41, Confusion matrix and ROC- I 30:42
- Lec 42, Confusion Matrix and ROC-II 29:36
- Lec 43, Performance of Logistic Model-III 25:01
- Lec 44, Regression Analysis Model Building - I 23:01
- Lec 45, Regression Analysis Model Building (Interaction)- II 24:29
- Lec 46, Chi - Square Test of Independence - I 31:44
- Lec 47, Chi-Square Test of Independence - II 28:41
- Lec 48, Chi-Square Goodness of Fit Test 25:39
- Lec 49, Cluster analysis: Introduction- I 21:55
- Lec 50, Clustering analysis: part II 21:45
- Lec 51, Clustering analysis: Part III 27:01
- Lec 52, Cluster analysis: Part IV 28:51
- Lec 53, Cluster analysis: Part V 19:10
- Lec 54, K- Means Clustering 27:34
- Lec 55, Hierarchical method of clustering -I 28:05
- Lec 56, Hierarchical method of clustering- II 30:19
- Lec 57, Classification and Regression Trees (CART : I) 33:24
- Lec 58, Measures of attribute selection 27:40
- Lec 59, Attribute selection Measures in CART : II 25:37
- Lec 60, Classification and Regression Trees (CART) - III 31:42
Take this course with you
Download the app to continue lessons from your phone anytime.
