Data science courses seem to be everywhere but what about an avenue to learning data science for the non-specialist?

Here we deliver the solution with a Non-Technical Introduction to Data Science which covers concepts and applications in an approachable way, without coding nor previous experience required.

Data science is not only for coders and we feel strongly that knowledge of data science concepts is for all of us. Understanding these opens up the world of data-driven decision making which benefits both our lives and our careers by using data analysis to support decisions even when we are not the ones coding it.

We want to make sure everyone can join the discussion on data science and that this engagement is not done only by the specialists. We bring the topics to you in an accessible, approachable way, without the need to understand coding or its syntax.

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.