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Data Science/Machine Leaning Principles for Natural Sciences
Rating: 4.3 out of 5(341 ratings)
868 students

Data Science/Machine Leaning Principles for Natural Sciences

Learn the basics and principles of data and machine learning for scientific problems
Created byGuilherme Matos Passarini, phD, Jones Granatyr, AI Expert Academy
Last updated 1/2025
English

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

Course content

7 sections • 50 lectures • 3h 54m total length

  • Welcome to the course4:13

    This lecture aims to introduce the course to students

  • How to watch the course1:25

    This lecture aims to explain some features of the platform to watch the course

Requirements

  • Basic math knowledge is desirable

Description

The course "Principles of Data Science and Machine Learning for Natural Sciences" 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.

Who this course is for:

  • The primary target audience are people from STEM interested to understand and use concepts of DS/ML
  • People from an IT/Computer science interested to know how the algorithms can be used in science projects
  • Prople from a math background interested to understand concepts of DS/ ML, and science

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