Day 1
Topic 1: R Fundamental

Topic 1.1 Getting Started in R

• What is R
• Install R and RStudio IDE
• Explore RStudio Interface

Topic 1.2. Data Types

• Numbers
• String
• Vector
• Matrix
• Array
• Data Frame
• List
• Factor

Topic 1.3. R Packages & Data I/O

• Import R Packages
• Import R Data Sets
• Import External Data
• Export Data

Topic 1.4. Data Visualization

• Scatter Plot
• Boxplot
• Bar chart
• Pie chart
• Histogram

Topic 1.5. R Programming

• Conditional
• Loop
• Break & Next
• Function Syntax
• Default Arguments

Topic 1.6. Statistics Analysis with R

• Descriptive Statistics
• Correlation
• Linear and Multiple Regression
• Hypothesis Testing
• Analysis of Variance (ANOVA)

Day 2
Topic 2: Data Analytics and Visualization with R

Topic 2.1 Data Preparation and Transformation

• Overview of Data Analysis of Research Data
• Install R Data Analysis Packages - Tidyverse and ggplot2
• Import and Export Dataset
• Filter and Slice Data
• Clean Data
• Join Data
• Transform Data
• Aggregate Data
• Pipe Data

Topic 2.2 Data Summary

• Categorical vs Continuous Data
• Quantitative vs Qualitative Data
• Descriptive Statistics of Data
• Summarize Data
• Basic Plots and Tables

Topic 2.3 Quantitative Data Analysis

• Quantitative Data Analysis Overview
• Correlation Analysis
• Regression Analysis
• Hypothesis Testing
• Analysis of Variances (ANOVA)

Topic 2.4 Qualitative Data Analysis

• Qualitative Data Analysis Overview
• Install R Packages for Qualitative Data Analysis
• Word Cloud Analysis
• Text Analysis

Topic 2.5 Data Visualization

• Grammar of Graphics
• Plots for Quantitative Data
• Plots for Qualitative Data
• Customize Visualizations
• Interpret Findings

Day 3
Topic 3: Basic Machine Learning with R

Topic 3.1 Overview of Machine Learning

• Introduction to Machine Learning
• Pattern Recognition Problems Suitable for Machine Learning
• Supervised vs Unsupervised Learnings
• Types of Machine Learning
• Machine Learning Techniques
• R Packages for Machine Learning

Topic 3.2 Regression

• What is Regression
• Applications of Regression
• Least Square Error Minimization
• Data Pre-processing
• Bias vs Variance Trade-off
• Regression Methods with Regularization
• Logistic Regression

Topic 3.3 Classification

• What is Classification
• Applications of Classification
• Classification Algorithms
• Confusion Matrix
• Classification Performance Evaluation

Day 4
Topic 4: Pattern Recognition with R

Topic 4.1 Clustering

• What is Clustering
• Applications of Clustering
• Distance Measure
• Clustering Algorithms
• Clustering Performance Evaluation
• Anomaly Detection Problem

Topic 4.2 Principal Component Analysis

• Principal Component Analysis (PCA) and Dimension Reduction
• Applications of PCA
• PCA Workflow

Topic 4.3 Deep Learning

• What is Neural Network
• Activation Functions
• Loss Function Minimization
• Gradient Descent Algorithms and Learning Rate
• Deep Neural Network for Visual Recognition
• Improve Visual Recognition with Convolutional Neural Network
• The Future of AI
• AI Ethics

Day 5
Topic 5: Text Mining with R

Topic 5.1: Introduction to Text Mining

• What is text mining
• Applications of text mining

Topic 5.2: Basic Text Functions

• Text manipulation functions
• Working with strings
• Working with gsub
• Advanced methods
• Convert to corpus

Topic 5.3: Importing Data

• Converting docx into corpus
• Converting pdf into corpus
• Converting html to corpus
• Web scraping

Topic 5.4: Tidytext Package

• Tidying text objects
• Tidying document term matrix objects
• Tidying document frequency matrix objects
• Tidying corpus objects
• Mining literacy works

Topic 5.5: Word Frequencies & Relationships

• Pre-processing text
• Wordcloud
• Frequency analysis
• nGrams & bigrams
• Bigrams for sentiment analysis
• Visualizing bigrams network

Topic 5.6: Sentiment Analysis

• Sentiment libraries
• Analyzing positive & negative words
• Comparing 3 sentiment libraries
• Common positive & negative words

Topic 5.7: Topic Modelling

• Latent Semantic Indexing (LSI)
• Latent Dirichlet Allocation (LDA)
• Word topic probabilities
• Document - topic probabilities
• Chapters probabilities
• Per document classification

Topic 5.8: Document Similarity & Classifier

• Text alignment & pairwise comparison
• Minihashing and locality sensitive hashing
• Extract key words
• Classify by location, language, topic