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Course Curriculum

Data Science
Introduction to Data and Statistics
What is Analytics Details 00:00:00
CRISP-DM Methodology Details 00:00:00
Introduction to supervised and Unsupervised learning Details 00:00:00
All about Data
Basic Statistics – Mean, Medium, Mode, Variance, Standard Deviation (SD) Details 00:00:00
Outliers in Data Details 00:00:00
Data Sourcing Details 00:00:00
Sampling Techniques Details 00:00:00
Data Cleanup and Transformation Details 00:00:00
R and R Navigation
Introduction to R / R Studio Details 00:00:00
Navigation in R Details 00:00:00
Types of variables Details 00:00:00
Data operations in R
Arithmetic operations Details 00:00:00
String operations Details 00:00:00
Logical Details 00:00:00
Date Details 00:00:00
File operations in R
Reading a CSV file Details 00:00:00
Reading metadata of a file Details 00:00:00
Statistics from a file Details 00:00:00
Data exploration using R
Functions – Mean, Median, Mode, Max, Min, Var, SD Details 00:00:00
Functions – Table and Tapply Details 00:00:00
Summarization using dply package Details 00:00:00
Visualization concepts
Visualization Basics Details 00:00:00
Basic plots in R – Scatter, Bar, Pie, Boxplots, Line, Histogram Details 00:00:00
Advance plotting with GGPLOT2
Bar Details 00:00:00
Scatter plot Details 00:00:00
Boxplot with facet_grid Details 00:00:00
Raster plot Details 00:00:00
Geo Map Details 00:00:00
Word Cloud Details 00:00:00
Regression Technique
Introduction to Supervised Learning Details 00:00:00
Linear Regression – Single Variable R2 Details 00:00:00
Linear Regression – Multiple Variable Details 00:00:00
Dealing with categorical variable Details 00:00:00
Understanding Residual plot Details 00:00:00
Multi-collinearity Identification Details 00:00:00
Decision Tree
Introduction to Decision Trees Details 00:00:00
Truth Table / Type 1 and 2 Errors Details 00:00:00
Train, Test and Validate Details 00:00:00
Model Fit Evaluation Details 00:00:00
Tree Tuning and Pruning Details 00:00:00
Model Comparisons Details 00:00:00
Ensemble Method-Random Forest
Random Forest – Bagging and Ensemble Details 00:00:00
Classification with Random Forest Details 00:00:00
Variable Importance Plot Details 00:00:00
Mtry and number of trees Details 00:00:00
Bias and Variance TradeOff Details 00:00:00
Packages
Package keyword Details 00:00:00
Default and protected access modifier Details 00:00:00
CLASSPATH and PATH environment variables Details 00:00:00
Pre-defined system packages Details 00:00:00
XGBOOST Technique
Classification with XGBoost Details 00:00:00
Model Comparisons Details 00:00:00
Dimensionality reduction
Introduction to Principal Component Analysis Details 00:00:00
Machine Learning
Introduction to Neural Network Details 00:00:00
Introduction to Support Vector Machine Details 00:00:00
Clustering
Introduction to Clustering Details 00:00:00
Kmeans Clustering Details 00:00:00
Deciding Cluster Size – Screeplot Details 00:00:00
Hierarchical Cluster Details 00:00:00
Market Basket Analysis
Introduction to Association Rules Details 00:00:00
Apriori Algorithm Details 00:00:00
Support, Confidence and Life Details 00:00:00
Text Mining
Text Mining – Creation of Document Term Matrix Details 00:00:00
Creation of a Word Cloud Details 00:00:00
Sentiment Analysis Details 00:00:00
Text Clustering Details 00:00:00
N-Gram Analysis Details 00:00:00
Reading Facebook / Twitter Details 00:00:00
Time series Analysis
Time Series Components Details 00:00:00
Creating a TS Object Details 00:00:00
ARIMA Model for forecasting Details 00:00:00