Machine Learning with R for Data Analysis

Workshop Description

Machine learning, at its core, is concerned with transforming data into actionable knowledge. This fact makes machine learning well-suited to the present-day era of "big data" and "data science". Given the growing prominence of R—a cross-platform, zero-cost statistical programming environment—there has never been a better time to start applying machine learning. Whether you are new to data science or a veteran, machine learning with R offers a powerful set of methods for quickly and easily gaining insight from your data.

This workshop is a practical tutorial that uses hands-on examples to step through real-world application of machine learning. Without shying away from the technical details, we will explore Machine Learning with R using clear and practical examples. This workshop is well-suited to machine learning beginners or those with experience. Using practical examples, we will explore how to prepare data for analysis, choose a machine learning method, and measure the success of the process.

We will learn how to apply machine learning methods to a variety of common tasks including classification, prediction, forecasting, market basket analysis, and clustering. By applying the most effective machine learning methods to real-world problems, you will gain hands-on experience that will transform the way you think about data.

This workshop will provide you with the analytical tools you need to quickly gain insight from complex data.

What You Will Learn

  • Understand the basic terminology of machine learning and how to differentiate among various machine learning approaches
  • Use R to prepare data for machine learning
  • Explore and visualize data with R
  • Classify data using nearest neighbor methods
  • Learn about Bayesian methods for classifying data
  • Predict values using decision trees, rules, and support vector machines
  • Forecast numeric values using linear regression
  • Model data using neural networks
  • Find patterns in data using association rules for market basket analysis
  • Group data into clusters for segmentation
  • Evaluate and improve the performance of machine learning models

Program Schedule (26th November 2017)

Time

Programme

8:30 – 9:00

Registration

9:00 – 10:30

Session 1

  • Machine Learning With R
  • R Data Structures and Managing Data with R
  • Classification Using Nearest Neighbors

10:30 – 11:00

Tea Break

11:00 – 12:30

Session 2

  • Classification Using Naïve Bayes
  • Classification Using  Decision Trees and Rules
  • Forecasting Numeric Data – Regression Methods

12:30 – 2:00

Lunch Break

2:00 – 3:30

Session 3

  • Neural Networks and Support Vector Machines.
  • Market Basket Analysis Using Association Rules

3:30 – 4:00

Tea Break

4:00 – 5:00

Session 4

  • Clustering with k-means
  • Meta Learning

 

Registration Fee

Participants

Early Bird Rate Register on or Before 15th November 2017

Normal Rate Register After 15th November 2017

International

USD150.00

USD200.00

Local

RM500

RM700

 

Facillitator

Assoc. Prof. Dr. Rayner Alfred
PhD Computer Science (Knowledge Discovery and Machine Learning)

Email: ralfred@ums.edu.my / ralfred121@gmail.com

Email us to register and reserve your seat.

Leveraging the Power of R for Business Intelligence

Workshop Description

"Most organizations early on in the data-science learning curve spend most of their time assembling data and not analyzing it. Mature data science organizations realize that in order to be successful they must enable their members to access and use all available data—not some of the data, not a subset, not a sample, but all data. A lawyer wouldn’t go to court with only some of the evidence to support their case—they would go with all appropriate evidence. ...The fundamental building block of a successful and mature data science capability is the ability to ask the right types of questions of the data. This is rooted in the understanding of how the business runs... The mature data science organization has a collaborative culture in which the data science team works side by side with the business to solve critical problems using data. ... [it] includes one or more people with the skills of a data artist and a data storyteller. Stories and visualizations are where we make connections between facts. They enable the listener to understand better the context (What?), the why (So what?), and “what will work” in the future (Now what?)."

         - Peter Guerra and Kirk Borne in Ten Signs of Data Science Maturity (2016)

 

Course Description (Register HERE)

Explore the world of Business Intelligence through the use cases supporting different functions within that company. This workshop provides data-driven and analytically focused approaches to help you answer questions in operations, marketing, and finance.

In Part 1, you will learn about extracting data from different sources, cleaning that data, and exploring its structure. In Part 2, you will explore predictive models and cluster analysis for Business Intelligence and analyze financial times series. Finally, in Part 3, you will learn to communicate results with sharp visualizations and interactive, web-based dashboards.

After completing the use cases, you will be able to work with business data in the R programming environment and realize how data science helps make informed decisions and develops business strategy. Along the way, you will find helpful tips about R and Business Intelligence.

What You Will Learn

  • Extract, clean, and transform data
  • Validate the quality of the data and variables in datasets
  • Learn exploratory data analysis
  • Build regression models
  • Implement popular data-mining algorithms
  • Visualize results using popular graphs
  • Publish the results as a dashboard through Interactive Web Application frameworks

 

Program Schedule (25th November 2017)

Time

Programme

8:30 – 9:00

Registration

9:00 – 10:30

Session 1

  • Extract, Transform and Load
  • Data Cleaning

10:30 – 11:00

Tea Break

11:00 – 12:30

Session 2

  • Exploratory Data Analysis
  • Linear Regression for Business

12:30 – 2:00

Lunch Break

2:00 – 3:30

Session 3

  • Data Mining with Cluster Analysis
  • Time Series Analysis

3:30 – 4:00

Tea Break

4:00 – 5:00

Session 4

  • Visualizing the Data's Story
  • Web Dashboards with Shiny

 

Registration Fee

Participants

Early Bird Rate Register on or Before 15th November 2017

Normal Rate Register After 15th November 2017

International

USD150.00

USD200.00

Local

RM500

RM700

 

Facillitator

Assoc. Prof. Dr. Rayner Alfred
PhD Computer Science (Knowledge Discovery and Machine Learning)

Email: ralfred@ums.edu.my / ralfred121@gmail.com

Email us to register and reserve your seat.