Data Science Introduction
Data Science Life cylce
Business Statistics- Data Types
Business Statistics- Data Collection plan
Graphical Techniques
Retail Analytics
7 QC tools
R Studio
Advance Functionalities in R
Python and Libraries
Deep Dive into Python
Python - Functional Programming
Data Visualization
Machine Learning
Deep Learning
Random Variable
Sampling
Central Limit Theorem
Hypothesis testing principles
Advance Hypothesis testing
Anova - Statistical differences
Chisquare
Data Cleaning
Imputation Techniques
Data analysis and Visualization
Priciples of Regression
Intro to Simple Linear Regression
Multiple Linear Regression
Logistic Regression
Python Model Deployment
Clustering introduction
K-means, Hierarchical
Mean-shift, Spectral
Linear Discriminant Analysis & Auto Encoders
Apriori Algorithm
Frequent Pattern Growth
Association Rules Mining
Supervised Machine Learning Concept
Decision Tree
Encoding Methods
Feature Engineering
Model Validation Methods
Bagging & Random Forest,Boosting, Stacking
Bagging & Random Forest,Boosting, Stacking and AI
Gradient Boosting Algorithm and AI & ML
Gradient Boosting Algorithm and AI
K-Nearest Neighbors, KNN and AI
Support Vector Machines,intelligence of a machine, Random Forest
Lasso Regression and AI
Elastic Net and L2 - Ridge Regressions and MSE
ANN,Optimization Algorithm and AI
ANN,Optimization Algorithm and initialization in neural Networks
Deep dive and MATLAB
Text Mining and sentiment analysis models
LDA and deep learning library
Word clouds and Doucument
Powerful - Modeling sequence data
Generate synthetic data
Autoencoders
Probablistic Algortihsm
Text classification using Naïve Bayes
Introduction to Timeseries
Forecasting Error and it metric
AR Model for errors
Data driven approaches
Exp Smoothing
