Computational statistics

Interleaving Computational and Inferential Thinking: Data Science for Undergraduates at Berkeley · Issue 3.2, Spring 2021

The undergraduate data science curriculum at the University of California, Berkeley is anchored in five new courses that emphasize computational thinking, inferential thinking, and the perspective gained by working on real-world problems. We believe that interleaving these elements within our core courses is essential to preparing students to engage in data-driven inquiry at the scale that contemporary scientific and industrial applications demand. This new curriculum is already reshaping the undergraduate experience at Berkeley, where these courses have become some of the most popular on campus and have led to a surging interest in a new undergraduate major and minor program in data science.

Computational Thinking for All Students

Posted by Maggie Johnson, Director of Education and University Relations, Google(Crossposted on the Google for Education Blog, and the the Huffingt...

Undergraduate Computational Science and Engineering Education

It is widely acknowledged that computational science and engineering (CSE) will play a critical role in the future of the scientific discovery process and engineering design. However, in recent years computational skills have been deemphasized in the curricula of many undergraduate programs in science and engineering. There is a clear need to provide training in CSE fundamentals at the undergraduate level. An undergraduate CSE program can train students for careers in industry, education, and for graduate CSE study. The courses developed for such a program will have an impact throughout the science, technology, engineering, and mathematics (STEM) undergraduate curriculum. This paper outlines the content of a CSE curriculum, the skills needed by successful graduates, the structure and experiences of some recently developed CSE undergraduate programs, and the potential career paths following a CSE undergraduate education.

Feminist Computer Science Education: "'Women in Computing' As Problematic": A Summary

I've long been interested in why, despite so much organized effort, there percentage of women in CS has been so stagnant. One hypothesis I h...

Niklaus Wirth

Niklaus Wirth (born 1934 in Switzerland) is one of the most prominent computer scientists, mostly known for creating Pascal and several other programming languages.

About Us - Siegel Family Endowment

About Our Chairman “Computational thinking is more than just a way to approach problem solving. It’s a way of processing and understanding the world through […]

Modular: The Claude C Compiler: What It Reveals About the Future of Software

Compilers occupy a special place in computer science. They're a canonical course in computer science education. Building one is a rite of passage. It forces you to confront how software actually works, by examining languages, abstractions, hardware, and the boundary between human intent and machine execution.

zach lieberman on Twitter / X

Today I was able to weave a line from Myron Krueger in a talk I gave : Computation is the medium of our lifetimes - it’s not just a technical medium but a cultural medium as well. We should explore the culture of computation.— zach lieberman (@zachlieberman) April 10, 2024

The education of a computer

An Introduction To Robust-First Computation

Did you know there's an entire field of computer science barely yet explored? Join me at the entrance to a deep rabbit hole as we take a look at Robust-First Computation.

Konrad Hinsen's blog

How can we document software and computational analyses in such a way that others can convince themselves of their validity, and build on them for their own work? The question has been around for many years, and a number of attempts have been made to provide partial answers. This post provides a brief review and describes my own tentative answer, inviting you to play with it.

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.