A Non-Technical Introduction to Data Science: Concepts and Applications

Topics covered include:

• What is data science? To introduce data science and explain how to break down a problem such that a computer can understand and make sense of it.  Humans make decisions using judgment and intuition, but computers need problems presented in binary format. 
• What is Machine Learning? To introduce the concepts of supervised & unsupervised machine learning, as well as reinforcement learning.  To understand the different types of input data for those. 
• Real world applications of machine learning To learn about applications of machine learning across different industries such as climate, drug discovery and robotics.
• What are machine ethics and why does it matter? To introduce the concepts of machine ethics and to understand how misrepresentative data can alter your outcomes with serious consequences 
• Simple steps for preparing a dataset To understand the steps that need to be performed on a raw dataset to make it usable for analysis

On completion of this masterclass, participants will be able to:

• Understand what data science and machine learning are.
• Understand the concept of Computational Thinking and why it is relevant.
• Describe three main types of machine learning.
• Understand applications of machine learning and the impact it has.
• Understand the importance of machine ethics, data bias and its consequences.
• Understand the steps necessary to clean a raw dataset for analysis.
• Design and develop an idea for a machine learning application.

Team based learning via group project:

As part of this masterclass, students will have the opportunity to work in small project teams to explore a novel idea for a machine learning application that builds on concepts learned in this course.   No coding is required.

This masterclass is designed for undergraduate or postgraduate students studying a non-technical subject.  This is for those with NO coding, programming nor technical experience, yet for those wishing to learn data science and its concepts and applications.