Dwight Nuwan Fonseka is Head of Data Science at Plano Pte. Ltd. and an ACLP-certified trainer with deep expertise in data analytics, machine learning, and AI applications. He has extensive hands-on experience developing predictive models, RShiny dashboards, and deep learning solutions using R, Python, TensorFlow, and Keras. With a strong professional background in healthcare, finance, and customer analytics, Dwight brings an applied perspective to teaching AI, focusing on both the opportunities and risks of emerging technologies.
Course Details
Course Details
What You'll Learn
Topic 1: Overview of Basic Plots in R
Scatter and Bubble Plots
Partition Plots
Bar Plot
Histogram
Pie Chart
Box Plot
Heatmap
Treemap
3D plots
Topic 2: Data Visualization with ggplot2
What is ggplot2
Components of ggplot2
Scatter Plot
Add Attributes to Aesthetics
Add Smoothing Line
Bar Plot
Histogram
Box Plot
Data Piping
Topic 3: Customize Visualization
Modifying Background
Modify Axis, Limits and Legend
Annotation and Titles
Add Reference Lines
Pre-Built Themes
Install Additional Themes
Topic 4: New Topic
Google Map API
Install ggmap
Get Google Map
Get Geo Coding
Plot Points on Map
Add Text to Map
Modifying Points on Map
Course Info
Prerequisite
The following knowledge is assumed
Software Requirement
Please download and install the following software prior to the class
Job Roles
Job Roles
- Data analysts
- Financial analysts
- Marketers
- Researchers
Trainers
Trainers
Richard Wan is an ACLP-certified lecturer and software consultant with over 40 years of experience in software and hardware development, spanning AI, computer vision, and machine learning. He began his programming career with 8-bit computing in the late 1970s and went on to earn his M.Sc. in Electrical Engineering (Computer Vision) from the University of Wisconsin–Madison. His professional contributions include co-founding multiple high-tech companies, pioneering digital publishing technologies, and leading AI-driven software development in healthcare, defense, and manufacturing.
Richard has taught a wide range of technical courses, including machine learning with Scikit-Learn, deep learning with TensorFlow and PyTorch, and computer vision with OpenCV. In predictive analytics, he emphasizes the use of PyTorch for building deep learning models that can forecast trends, detect anomalies, and classify outcomes. His teaching approach blends decades of hands-on development with structured, beginner-friendly instruction, equipping learners with practical skills to transform data into prediction.