Introduction To Data Science

2016
5:52 hours

├── 01 Course Overview
│ ├── 001 Course Introduction.mp4
│ ├── 002 Walk-through of a data science project.mp4
│ ├── 003 Starting with R and data.mp4
│ ├── homedata_lesson1_clean.rds
│ ├── homedata_lesson1_dirty.rds
│ └── Sec1_Lesson1_Walkthru.Rmd
├── 02 Modeling and Machine Learning
│ ├── 001 Mapping Business to Machine Learning Tasks.mp4
│ ├── 002 Validating Models.mp4
│ ├── 003 Naive Bayes background.mp4
│ ├── 004 Naive Bayes practice.mp4
│ ├── 005 Linear Regression background.mp4
│ ├── 006 Linear Regression practice.mp4
│ ├── 007 Logistic Regression background.mp4
│ ├── 008 Logistic Regression practice.mp4
│ ├── 009 Decision Trees and Random Forest background.mp4
│ ├── 010 Random Forest practice.mp4
│ ├── 011 Generalized Additive Models.mp4
│ ├── 012 Support Vector Machines.mp4
│ ├── 013 Gradient Boosting.mp4
│ ├── 014 Regularization for Linear and Logistic Regression.mp4
│ ├── 015 Evaluating Models.mp4
│ ├── Examples.R
│ ├── salaryData.rds
│ ├── TestTrainExample.Rproj
│ └── TestTrainSplit.Rmd
├── 03 Data
│ ├── 001 Loading Data in R.mp4
│ ├── 002 Visualizing Data.mp4
│ ├── 003 Missing Values.mp4
│ ├── 004 The Shape of Data.mp4
│ ├── 005 Dealing with Categorical Variables.mp4
│ └── 006 Useful Data Transformations.mp4
└── 04 Moving On
├── 001 Recommended Books.mp4
├── 002 Further Topics.mp4
└── 003 Next Steps.mp4