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