Module 1: Introduction to Data Science
What is Data Science?
What is Machine Learning?
What is Deep Learning?
What is AI?
Data Analytics & it’s types
Module 2: Introduction to R
What is R?
Why R?
Installing R
R environment
How to get help in R
R Studio Overview
Module 3: R Basics
Environment setup
Data Types
Variables Vectors
Lists
Matrix
Array
Factors
Data Frames
Loops
Packages
Functions
In-Built Data sets
Module 4: R Packages
DMwR
Dplyr/plyr
Caret
Lubridate
E1071
Cluster/fpc
Data.table
Stats/utils
Ggplot/ggplot2
Glmnet
Module 5: Importing Data
Reading CSV files
Saving in Python data
Loading Python data objects
Writing data to csv file
Module 6: Manipulating Data
Selecting rows/observations
Rounding Number
Selecting columns/fields
Merging data
Data aggregation
Data munging techniques
Module 7: Statistics Basics
Central Tendency
Mean
Median
Mode
Skewness
Normal Distribution
Probability Basics
What does mean by probability?
Types of Probability
ODDS Ratio?
Standard Deviation
Data deviation & distribution
Variance
Bias variance Trade off
Underfitting
Overfitting
Distance metrics
Euclidean Distance
Manhattan Distance
Outlier analysis
What is an Outlier?
Inter Quartile Range
Box & whisker plot
Upper Whisker
Lower Whisker
Scatter plot
Cook’s Distance
Missing Value treatments
What is a NA?
Central Imputation
KNN imputation
Dummification
Correlation
Pearson correlation
Positive & Negative correlation
Module 8: Error Metrics
Classification
Confusion Matrix
Precision
Recall
Specificity
F1 Score
Regression
MSE
RMSE
MAPE
Module 9: Machine Learning
Module 10: Supervised Learning
Linear Regression .
Read More Read Less
