5 Days Data Analytics with R Specialization

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

Prerequisite

The learner must meet the minimum requirement below :

• Read, write, speak and understand English

Target Audience

• NSF
• Full Time Students
• Data Analysts

Software Requirement

This course will use Google Colab for training. Please ensure you have a Google account.

• Data Analyst
• Programmers
• IT Engineers
• Data Scientist

Dwight Nuwan Fonseka: Dwight Nuwan Fonseka is a ACLP certified trainer. He have a degree in Biotechnology from NUS ,Advanced diploma in Pharmaceutical management from MDIS and Masters in Education from NTU. He have 8 years experience of teaching biology at O and A levels/ IB level in international schools in Singapore and overseas.

Marcel Leng: Marcel Leng is a ACTA certified. Marcel graduated with majors in Applied Mathematics and Physics from the National University of Singapore.

His core specialisation skills are R, Python, Machine Learning, Statistical Analysis, and Data Visualisation in Tableau. His current interests include Machine Learning, Deep Learning, Artificial Intelligence, Internet of Things, Robotics and Programming.