Citizen Data Scientist, Module VI: Mastering Models for Learning: A Deep Dive into Bagging, Neural Networks, and More
Learn about machine learning models like Random Forest, Neural Networks, and K-means clustering. This detailed guide explains concepts intuitively, with examples like predicting ice cream sales and classifying handwritten digits
Citizen Data Scientist, Module V: Unsupervised Learning: Discovering hidden patterns
Explore the world of unsupervised learning with intuitive examples. Learn about clustering, dimensionality reduction, and anomaly detection, and discover how these techniques reveal hidden structures in data.
Fluid-Structure Interaction Chpater 2: One-way physics coupling
Dive into the world of physics coupling and discover how fluids and structures interact. This guide covers the basics of 1-way coupling, the monolithic vs. partitioned approach, and the key differences between explicit and implicit time discretization methods.
Citizen Data Scientist, Module IV: Applying Data Science in Practice: Feature Engineering, Scaling, and Selection
Learn how to apply data science in practice by mastering essential preprocessing techniques like feature engineering, scaling, and selection. This post explains the importance of each step, with practical examples on how to improve your machine learning models.
Citizen Data Scientist, Module III: Measuring Model Performance: Metrics That Matter
Evaluating a machine learning model's performance is crucial to ensure it works well with unseen data. In this post, we explore key metrics for regression and classification, such as R², MSE, precision, recall, and the confusion matrix. With examples that clarify concepts like recall's importance in high-risk scenarios, we also explain k-fold cross-validation to enhance model reliability
Citizen Data Scientist, Module II: Supervised learning: Predicting the Future with Labeled Data
Supervised learning is at the heart of many machine learning applications, helping models make predictions based on labeled data. From predicting house prices to classifying emails, this blog post explores the basics of supervised learning, covering regression, classification, decision trees, and key concepts like gradient descent—all in an accessible and intuitive way.
Fluid-Structure Interaction Chapter 1: Introduction
In this post, we explore the world of Fluid-Structure Interaction (FSI), a key area of study that reveals how fluids and solids influence each other in systems like bridges, airplanes, and even the human body. From basic principles to real-world examples, this guide will help you understand how these forces shape the structures we interact with every day.
Citizen Data Scientist, Module I: Introduction to Data Science: Laying the Foundation
In this first module of the Citizen Data Scientist course series, we explore the foundational principles of Data Science. From understanding key concepts like machine learning and the CRISP-DM process to getting hands-on with Python and essential libraries, this post lays the groundwork for your journey into the world of data-driven decision-making.
Getting Started with Python for Data Science: A Hands-On Introduction
Welcome to the practical world of Python for Data Science, where we learn how to turn data into meaningful results.
This post walks you through key techniques and tools in Python, showing how they can be applied to real-world data science tasks. Whether you’re just starting out or looking to solidify your skills, this guide offers a hands-on approach to mastering Python for data science.