24h Pro data science in R

Course Curriculum

Chapter 1: Basics

Lecture 1: Introduction

Lecture 2: Setting up R

Chapter 2: General R programming

Lecture 1: The data frame

Lecture 2: Variables

Lecture 3: Reading data

Lecture 4: Reading data with dates: Classes for customized dates

Lecture 5: Text

Lecture 6: Functions

Lecture 7: The apply family of functions

Lecture 8: Histograms

Chapter 3: Random numbers, probability and statistics

Lecture 1: Generating random numbers

Lecture 2: Density and cumulative distribution function

Lecture 3: Comparing distributions

Chapter 4: Advanced data processing using sqldf

Lecture 1: sqldf – Part1

Lecture 2: sqldf – Part2

Chapter 5: Statistical modelling: Linear regression

Lecture 1: Dummy variables

Lecture 2: The lm() function: Part1

Lecture 3: The lm function: Part2

Lecture 4: Comparing models

Lecture 5: Normality, residuals and transformations

Lecture 6: Log-log models

Lecture 7: Linear mixed effects models: Part1

Lecture 8: Linear mixed effects models: Part2

Lecture 9: Robust regression

Chapter 6: Statistical modelling: GLM and Nonlinear regression

Lecture 1: Logistic regression – Part1

Lecture 2: Logistic regression – Part 2

Lecture 3: Logistic regression – Part 3

Lecture 4: Logistic regression – Part 4

Lecture 5: Poisson regression: Part1

Lecture 6: Poisson regression: Part2

Lecture 7: Poisson regression: Part3

Lecture 8: Nonlinear regression

Chapter 7: XGBOOST: Gradient Boosting

Lecture 1: How does it work? Relevant parameters – Part1

Lecture 2: How does it work? Relevant parameters – Part2

Lecture 3: Using XGBoost for regression

Lecture 4: Cross validation in XGBOOST: the xgb.cv function

Lecture 5: GridSearch for XGBoost via the caret package

Lecture 6: Using XGBoost for classification

Chapter 8: Principal components

Lecture 1: Selecting PCA and projecting the data

Lecture 2: PCA regression

Chapter 9: Machine learning – the CARET package – introduction

Lecture 1: Introduction

Lecture 2: Preprocessing data: Part1

Lecture 3: Preprocessing data: Part2

Chapter 10: Sound

Lecture 1: Extracting meaningful sound features

Chapter 11: Machine learning – the CARET package – Supervised problems

Lecture 1: Introduction to train / finalModel vs train

Lecture 2: Naive Bayes

Lecture 3: Support Vector Machines (SVMs) – Part1

Lecture 4: Support Vector Machines (SVMs) – Part2

Lecture 5: Lasso, Ridge and Elasticnet

Lecture 6: GLMNet

Lecture 7: Extra trees

Lecture 8: Random Forests

Lecture 9: Bagged CARTS

Lecture 10: Oblique random forests

Lecture 11: Adaboost

Lecture 12: Stochastic gradient boosting

Lecture 13: Boosted Logistic Regression

Lecture 14: Multilayer Perceptron

Chapter 12: Unsupervised problems

Lecture 1: K-Means

Lecture 2: DBSCAN

Lecture 3: Novelty detection

Lecture 4: Outliers

Chapter 13: Deep learning / Neural networks via Keras in R

Lecture 1: Brief introduction to Python – Setting up Keras

Lecture 2: Using Keras – Layers: P1

Lecture 3: Using Keras – Layers : P2

Lecture 4: Calling Keras from R : Regression

Lecture 5: Neural Nets: Classification

Chapter 14: Time series in R

Lecture 1: Time series basics

Lecture 2: ACF and PACF

Lecture 3: The auto.arima package

Lecture 4: Predicting global temperatures using auto.arima

Lecture 5: Predicting the US GDP via auto.arima

Chapter 15: Visualizing data

Lecture 1: Geo data using Google Maps

Lecture 2: Interactive plots via the iPlots package

Chapter 16: Creating R packages

Lecture 1: Creating R packages