As the scale and scope of data collection continue to increase across virtually all fields, statistical learning has become a critical toolkit for anyone who wishes to understand data. An Introduction to Statistical Learning provides a broad and less technical treatment of key topics in statistical learning. This book is appropriate for anyone who wishes to use contemporary tools for data analysis.
Datasheets for Datasets
The machine learning community currently has no standardized process for documenting datasets, which can lead to severe consequences in high-stakes domains. To address this gap, we propose...
Teaching Programming for First-Year Data Science
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Foundations of Data Science for Students in Grades K-12
On September 13 and 14, 2022, the Board on Science Education at the National Academies of Sciences, Engineering, and Medicine held a workshop entitled Foundations of Data Science for Students in Grades K–12. Speakers and participants explored the rapidly growing field of K-12 data science education, by surveying the current landscape, surfacing what is known, and identifying what is needed to support student learning, develop curriculum and tools, and prepare educators. To support these conversations, four papers were commissioned and discussed during the workshop. This publication summarizes the presentations and discussion of the workshop.
Data Integration
This book is an introduction to the problem of data integration and a rigorous account of one of the leading approaches to solving this problem.
The Roadmap of Mathematics for Machine Learning
A complete guide to linear algebra, calculus, and probability theory
Innovation in Data Science Education
Data science is a new field of research focuses on extracting knowledge and value from data. The radical growth in recent years in the availability of data and of the computational resources required to process them, has led to a corresponding increase ...
Data Structures for Statistical Computing in Python
In this paper we are concerned with the practical issues of working with data sets common to finance, statistics, and other related fields. pandas is a new library which aims to facilitate working with these data sets and to provide a set of fundamental building blocks for implementing statistical models.
A first introduction to data science education in secondary schools: Teaching and learning about data exploration with <span style="font-variant:small-caps;">CODAP</span> using survey data
Abstract In this paper, we will describe an introduction to Data Science for secondary school students. We will report on the design and implementation of an introductory unit on “Data and data detectives with CODAP” in which secondary school students used the online tool CODAP to explore real and meaningful survey data on leisure time activities and media use (so‐called JIM‐PB data) in a statistical project setting as a starting point for data science. The JIM‐PB data set served as a valuable data set that offered meaningful and exciting opportunities for data exploration for secondary school students, and CODAP proved to be a valuable tool for the first explorations of this data.
Coding Club: a positive peer-learning community
Free and accessible tutorials on data analysis, manipulation, visualisation and more.
Datasets Guide | Unsloth Documentation
Learn how to create & prepare a dataset for fine-tuning.
Personalized Machine Learning
This page contains collects information and supplementary material for my textbook Personalized Machine Learning:
Active Learning with Statistical Models
What is Machine Learning? | IBM
Machine learning is the subset of AI focused on algorithms that analyze and “learn” the patterns of training data in order to make accurate inferences about new data.
The Data Minimization Principle in Machine Learning
The principle of data minimization aims to reduce the amount of data collected, processed or retained to minimize the potential for misuse, unauthorized access, or data breaches. Rooted in...
