What you'll learn:
- Understand the concepts of data science and machine learning and how they can be used in science
- Know the main algorithms used in tasks of classification, regression, and clustering
- Know the main architectures of neural networks
- Understand how you can use algorithms/analyses in science projects/investigations/studies
The course "Principles of Data Science and Machine Learning for Science" is designed to connect traditional scientific disciplines with the rapidly growing fields of Data Science (DS) and Machine Learning (ML). As research increasingly depends on large datasets and advanced computational methods, it’s becoming essential for scientists to know how to leverage DS and ML techniques to improve their work.
This course offers a solid introduction to the key concepts of Data Science and Machine Learning, specifically aimed at scientists and researchers in areas like biology, chemistry, physics, and environmental science. Participants will learn the basics of data analysis, including data collection, cleaning, and visualization, before moving on to machine learning algorithms that can help identify patterns and make predictions from data.
The course doesn’t require any programming skills and focuses on fundamental theoretical concepts. It's structured into six main sections:
1. Introduction
We'll start by introducing the course, covering its main features, content, and how to follow along.
2. Core DS/ML Concepts
We’ll go over basic concepts like variables, data scaling, training, datasets, and data visualization.
3. Classification
In this section, we’ll discuss key classification algorithms such as decision trees, random forests, Naive Bayes, and KNN, with examples of how they can be applied in scientific research.
4. Regression
We’ll briefly cover linear and multiple linear regression, discussing the main ideas and providing examples relevant to science.
5. Clustering
This section will focus on standard and hierarchical clustering methods, along with practical examples for scientific applications.
6. Neural Networks
Finally, we’ll introduce neural networks, discussing their biological inspiration and common architectures like Feedforward Neural Networks (FNN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Hopfield Networks.
