PROGRAM OVERVIEW

CURRICULUM

  Online Class Videos

  • Session 1 - Data Science Introduction
  • Session 2 & 3 - Variable Types and Central Tendency
  • Session 4/1 - Measure of Dispersion
  • section 4/2 & 5 Measure of Dispersion - Quartiles
  • Session 6 - Quartiles and Outliers
  • Session 7 - Covariance and Correlation
  • Session 8- Skewness , Kurtosis and Sampling Techniques
  • Session 9 - Probability Distributions
  • Session 10 - Normal and Standard Normal Distributions
  • Session 11- Hypothesis Testing
  • Session 12- Hypothesis Testing ,z and t tests
  • Session 13 - z and t test examples
  • Session 14- ANOVA
  • Session 15 - Chi Square
  • Python 1 - Basic Operation
  • Python 2
  • Python 3
  • Python 4 - Identifiers, Keywords,Operators,Delimiters and Literals
  • Python 5 - Print,Type and Type conversions
  • Python - Operations and Strings
  • Python - List, Tuple
  • Python - Sets
  • Python - Sets - II
  • Python - if and else
  • Python - Loops if and Else continuation
  • Python - Sign up Application using loops
  • Python - Loops For Statements
  • Loops - While and Function Introduction
  • Python - Advance Functions and Lambda, Map and Filter
  • File, Exception and error Handling
  • Modules
  • Python - Numpy
  • Python - Numpy Part - II
  • Python - Pandas - Series and DataFrame
  • Pandas
  • Pandas - BASIC OPERATION , READING FILE AND JOINING & CONCATINATION
  • HR use case
  • Python - Hr Usecase - Part 2
  • Python Regular Expression - Part 1
  • Python - Regular Expression Part 2
  • Python - Web Scrapping
  • Python Data Analysis Matplotlib
  • Python Data Analysis - Seaborn
  • Python - OOPS

  Old Online Videos Repository

  • Data Science Introduction
  • Variables and Types
  • Measure Of Central Tendency - Mean, Median and Mode
  • Variance and Standard deviation
  • Box Plot and Outliers
  • Normal Distribution
  • Covariance and Correlation
  • Correlation and Causation
  • Probability Distributions
  • Bernouli and Binomial Distribution
  • Dataset
  • Poisson Distribution and Normal Distribution
  • Sampling Methods and Central Limit Theorem
  • Hypothesis Testing
  • Hypothesis Testing - 2
  • Z Tables
  • Z test and t-test examples
  • Installation of R
  • Basics of R Operation
  • Introduction to Machine Learning
  • Linear Regression 1
  • Linear Regression 2
  • SAT Datset
  • Linear Regression 3
  • -Linear Regression 4
  • R Square and Adjusted R Square
  • SAT Case Study (Partly)
  • SAT Case Study(Partly) - 2
  • SAT Case Study - 3
  • LGD based Dataset
  • LGD Case Study - 1
  • LGD Case Study - 2
  • LGD Case Study - 3
  • LGD Case Study - 4
  • LGD - 5
  • Logistic Regression
  • Logistic Regression - 2
  • Use case - Insurance Prediction
  • Use case - Insurance Data Set
  • Insurance Case Study
  • Insurance Case Study - 2
  • Confusion Matrix
  • Insurance Case study
  • Decision tree
  • Decision Tree 2
  • Entropy and Information Gain
  • Gini Index
  • Carseats case study Decision Tree
  • Carseats Case study - 2
  • Boston Case Study
  • Random Forest - 1
  • Random Forest 2
  • Random Forest Case Study
  • Support Vector Machines
  • Support Vector Machines - 2
  • SVM - 3
  • SVM - 4
  • SVM Case Study
  • Project and KNN Intro
  • KNN Case Study
  • KNN Case Study - 2
  • Clustering 1
  • Clustering 2
  • Clustering Case Study
  • _PCA

  Statistics - Assignments

  • Assignment - 1
  • Dataset
  • Assignment - 2
  • Assignment - 3

  Statistics Materials

  • Data its kinds , Central Tendency and Measures of Dispersion
  • Basic Probability
  • Probability Distribution
  • Normal Distribution
  • Sampling Distributions
  • Confidence Interval
  • t-distributions
  • Hypothesis Testing
  • Chi-square
  • F - Distribution
  • ANOVA

  Python - Basic , Core and Advanced

  • Lexical Structure
  • Introduction to Basic Operation
  • Dictionary
  • Set
  • Tuple
  • List
  • File Handling
  • Functions

  Datasets

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