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Data Analytics with Python

Overview

COURSE OUTLINE: This course includes examples of analytics in a wide variety of industries, and we hope that students will learn how one can use analytics in their careers and life. One of the most important aspects of this course is that hands-on experience creating analytics models will be shared.

INTENDED AUDIENCE: Management, Industrial Engineering and Computer Science Engineering Students

INDUSTRIES APPLICABLE TO: Any analytics company

Syllabus

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

Taught by

IIT Roorkee July 2018

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