R For Data Science

• R Installation
• R Studio
• Understanding Data Structures in R - Lists
• Matrices
• Vectors
• R Studio the IDE
• Basic Building Blocks in R
• UnderstandingVectors in R
• Basic Operations Operators and Types 
• Handling Missing
• Values in R
• Subsetting Vectors in R
• Matrices and Data Frames in R
• Logical Statements in R
• Lapply, Sapply, Vapply and Tapply Functions

Data Visualization Using R

• Grammar of Graphics
• Bar Charts
• Histograms
• Pie Charts |
• Scatter Plots
• Line Plots and Regression
• Word Clouds
• Box Plots
• GGPLOT2

Statistical Learning

• Measures of Central Tendency in Data
• Measures of Dispersion 
• Understanding Skewness in Data
• Probability Theory
• Bayes Theorem 
• Probability Distributions
• Hypothesis Testing

Analysis Of Variance And Covariance

• One-Way Analysis of Variance
• Assumption of ANOVA
• Statistics Associated with One-Way Analysis of Variance
• Interpreting the ANOVA Results
• Two- Way Analysis of Variance
• Interpreting the ANOVA Results
• Analysis of Covariance

Exploratory Data Analysis With R

• Merge, Rollup, Transpose and Append
• Missing Analysis and Treatment 
• Outlier Analysis and Treatment
• Summarizing and Visualizing the Important Characteristics of Data
• Univariate, Bivariate Analysis
• Crosstabs, Correlation

Linear Regression

• What is Regression Analysis
• Limitations of Regression
• Covariance and Correlation
• Multivariate Analysis
• Assumptions of Linearity Hypothesis Testing
• Limitations of Regression
• Implementing Simple & Multiple Linear Regression
• Making Sense of Result Parameters
• Model Validation 
• Handling Other Issues/Assumptions in Linear Regression
• Handling Outliers, Categorical Variables, Autocorrelation, Multicollinearity, Heteroskedasticity Prediction and Confidence Intervals

Logistic Regression

• Implementing Logistic Regression
• Making Sense of Result Parameters: Wald Test, Likelihood Ratio Test Statistic, Chi-Square Test Goodness of Fit Measures 
• Model Validation: Cross-Validation, ROC Curve, Confusion Matrix

Decision Trees

• Introduction to Predictive Modelling with Decision Trees
• Entropy & Information Gain
• Standard Deviation Reduction (SDR)
• Overfitting Problem
• Cross-Validation for Overfitting Problem
• Running as a Solution for Overfitting

Basics Of Python For Data Science

• Python Basics
• Data Structures in Python
• Control & Loop Statements in Python
• Functions & Classes in Python
• Working with Data

Data Frame Manipulation

• Data Acquisition (Import & Export)
• Indexing
• Selection and Filtering Sorting & Summarizing
• Descriptive Statistics
• Combining and Merging Data Frames
• Removing Duplicates
• Discretization and Binning
• String Manipulation

Exploration Of Data Analysis

• Data Visualization & EDA

Time Series Forecasting

• Understand Time Series Data
• Visualizing Time Series Components  Exponential Smoothing
• Holt's Model
• Holt-Winter's Model
• ARIMA 
• ARCH & GARCH

Unsupervised Learning

• K-Means Clustering

Dimensionality Reduction

• Principal Component Analysis (PCA)
• Scree Plot
• One-Eigen Value Criterion Factor Analysis

Introduction To Machine Learning

• Machine Learning Modelling Flow
• How to Treat Data in ML
• Parametric & Non-Parametric ML Algorithm
• Types of Machine Learning
• Performance Measures
• Bias-Variance Trade-Off
• Overfitting & Underfitting  Optimization Techniques
• Scikit-Learn Library

Logistic Regression

• Logistic Regression with Stochastic Gradient Descent, Batch GD 
• Optimizing Learning Rate
• Momentum

K Nearest Neighbor

• Understanding KNN
• Voronoi Tessellation
• Choosing K
• Distance Metrics - Euclidean, Manhattan, Chebyshev

Decision Tree & Random Forest

• Fundamental Concepts of Ensemble
• Hyper-Parameters