Data Science and Machine Learning with R

Overview

🕮 Off the shelf (OTS)

Target Audience
Members of the audience are required to have some technical expertise such as table structure, working with tabular data in R, and intermediate data analysis.
They may come from other technical backgrounds such as Data Analysts, Software Developers, and Data Engineers who either work with Data Scientists or are using this course in their journey towards training as a Data Scientist.
They may be Mid/Senior Leadership seeking a greater understanding of how to implement Data Science within their organization.

Prerequisites
•We recommend that delegates are familiar with fundamental data science concepts, such as those found on our Introduction to Data Science for Data Professionals, as well as programming techniques found in Data Handling in R.
•You should also have an interest in developing Data Science within your organisation or in becoming a Data Scientist.

Overview
This five-day course is aimed at those who are familiar with data analysis and are interested in learning about how Data Science, Analytics, Machine Learning, and Artificial Intelligence (AI) can be used to yield value from data assets.

This course will be of interest if you are interested in developing your own skills to move from analytics to Data Science, or if you are working with Data Scientists and want to learn more about what’s possible.

You will be introduced to key concepts and tools for use in Data Science, including typical Data Science Project lifecycles, potential applications & project pitfalls, relevant aspects of data governance and ethics, roles and responsibilities, Machine Learning and AI model development, exploratory analysis and visualisation, as well as techniques and strategies for model deployment.

Throughout the course you will engage in activities and discussions with one of our Data Science technical specialists. Theoretical modules are complemented with comprehensive practical labs.

Outline
•Introduction to Data Science & Machine Learning
•Introduction to R for Data Science
•Descriptive & Inferential Statistics with R
•Preprocessing Data for Analysis
•Supervised Learning: Regression
•Supervised Learning: Classification
•Model Selection & Evaluation
•Unsupervised Learning
•Ethics for Data Scientists
•Deploying Models & Insights
•Where to Go Next"