Day 1 - What is Data Science?
Day 2 - What is Machine Learning?
Day 3 - What is Deep Learning?
Day 4 - What is AI? Day 5 - Data Analytics & it’s types

Day 1 - What is R?
Day 2 - Why R?
Day 3 - Installing R
Day 4 - R environment
Day 5 - How to get help in R
Day 6 - R Studio Overview

Day 1 - Environment setup
Day 2 Data Types
Day 3 - Variables Vectors
Day 4 - Lists
Day 5 - Matrix
Day 6 - Array
Day 7 - Factors
Day 8 - Data Frames
Day 9 - Loops
Day 10 - Packages
Day 10 - Functions
Day 11 - In-Built Data sets

Day 1 - DMwR
Day 2 - Dplyr/plyr
Day 3 - Caret
Day 4 - Lubridate
Day 5 - E1071
Day 6 - Cluster/FPC
Day 7 - Data.table
Day 8 - Stats/utils
Day 9 - ggplot/ggplot2
Day 10 - Glmnet

Day 1 - Reading CSV files
Day 2 - Saving in Python data
Day 2 - Loading Python data objects
Day 3 - Writing data to CSV file

Day 1 - Selecting rows/observations
Day 2 - Rounding Number
Day 3 - Selecting columns/fields
Day 4 - Merging data
Day 5 - Data aggregation
Day 6 - Data munging techniques

Day 1 - Central Tendency
Day 2 - Mean
Day 3 - Median
Day 4 - Mode
Day 5 - Skewness
Day 6 - Normal Distribution
Day 7 - Probability Basics
Day 8 - What does it mean by probability?
Day 9 - Types of Probability
Day 10 - ODDS Ratio?
Day 11 - Standard Deviation
Day 12 - Data deviation & distribution
Day 13 - Variance
Day 14 - Bias variance Tradeoff
Day 15 - Underfitting
Day 16 - Overfitting
Day 17 - Distance metrics
Day 18 - Euclidean Distance
Day 19 - Manhattan Distance
Day 20 - Outlier analysis
Day 21 - What is an Outlier?
Day 22 - Inter Quartile Range
Day 23 - Box & whisker plot
Day 24 - Upper Whisker
Day 25 - Lower Whisker
Day 26 - Scatter plot
Day 27 - Cook’s Distance
Day 28 - Missing Value treatments
Day 29 - What is an NA?
Day 30 - Central Imputation
Day 31 - KNN imputation
Day 32 - Dummification
Day 33 - Correlation
Day 34 - Pearson correlation
Day 35 - Positive & Negative correlation

Day 1 - Classification
Day 2 - Confusion Matrix
Day 3 - Precision
Day 4 - Recall
Day 5 - Specificity
Day 6 - F1 Score
Day 7 - Regression
Day 8 - MSE
Day 9 - RMSE
Day 10 - MAPE

Machine Learning

Day 1 - Linear Regression
Day 2 - Linear Equation
Day 3 - Slope
Day 4 - Intercept
Day 5 - R square value
Day 6 - Logistic regression
Day 7 - ODDS ratio
Day 8 - Probability of success
Day 9 - Probability of failure
Day 10 - ROC curve
Day 11 - Bias Variance Tradeoff

Day 1 - K-Means
Day 2 - K-Means ++
Day 3 - Hierarchical Clustering

Day 1 - Linear Regression
Day 2 - Logistic Regression
Day 3 - K-Means
Day 4 - K-Means++
Day 5 - Hierarchical Clustering – Agglomerative
Day 6 - CART
Day 7 - 5.0
Day 8 - Random forest
Day 9 - Naïve Bayes