This curriculum was designed for high school students with no prior coding experience who are interested in learning Python programming for data science. However, this course material would be useful for anyone interested in teaching or learning basic programming for data analysis.

The curriculum features short lessons to deliver course material in “bite sized” chunks, followed by practices to solidify the learners' understanding. Pre-recorded videos of lessons enable effective virtual learning and flipped classroom approaches.

The learning objectives of this curriculum are:

1. Write code in Python with correct syntax and following best practices.
2. Implement fundamental programming concepts when presented with a programmatic problem set.
3. Apply data analysis to real world data to answer scientific questions.
4. Create informative summary statistics and data visualizations in Python.
5. These skills provide a solid foundation for basic data analysis in Python. Participation in our program exposes students to the many ways coding and data science can be impactful across many disciplines.

Our curriculum design consists of 27 lessons broken up into 5 modules that cover Jupyter notebook setup, Python coding fundamentals, use of essential data science packages including pandas and numpy, basic statistical analyses, and plotting using seaborn and matplotlib. Each lesson consists of a lesson notebook, used for teaching the concept via live coding, and a practice notebook containing similar exercises for the student to complete on their own following the lesson. Each lesson builds on those before it, beginning with relevant content reminders from the previous lessons and ending with a concise summary of the skills presented within.

How did scientists figure out the structure of atoms without looking at them? Try out different models by shooting light at the atom. Check how the prediction of the model matches the experimental results.

Help scientists recover worldwide weather observations made by Royal Navy ships around the time of World War I. These transcriptions will contribute to climate model projections and improve a database of weather extremes. Historians will use your work to track past ship movements and the stories of the people on board.

The research publications on Our World in Data are dedicated to a large range of global problems in health, education, violence, political power, human rights, war, poverty, inequality, energy, hunger, and humanity’s impact on the environment. On the homepage we list all the global problems and important long-term changes that we have researched.

Thanks to the work of thousands of researchers around the world who dedicate their lives to it, we often have a good understanding of how it is possible to make progress against the large problems we are facing. The world has the resources to do much better and reduce the suffering in the world.

We believe that a key reason why we fail to achieve the progress we are capable of is that we do not make enough use of this existing research and data: the important knowledge is often stored in inaccessible databases, locked away behind paywalls and buried under jargon in academic papers.

The goal of our work is to make the knowledge on the big problems accessible and understandable. As we say on our homepage, Our World in Data’s mission is to publish the “research and data to make progress against the world’s largest problems”.

Licenses: All visualizations, data, and articles produced by Our World in Data are open access under the Creative Commons BY license. You have permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited. All the software and code that we write is open source and made available via GitHub under the permissive MIT license. All other material, including data produced by third parties and made available by Our World in Data, is subject to the license terms from the original third-party authors.

CK-12 Foundation's new and improved Advanced Probability and Statistics-Second Edition FlexBook introduces students to basic topics in statistics and probability, but finishes with the rigorous topics an advanced placement course requires.

CK-12 Foundation's Basic Probability and Statistics - A Short Course is an introduction to theoretical probability and data organization. Students learn about events, conditions, random variables, and graphs and tables that allow them to manage data.

This document provides learning-by-doing materials for Analytics software skill development using SAS JMP. It integrates Analytics concepts and techniques with real-world scenarios based on the COVID-19 pandemic to illustrate how real-world data can be transformed into actionable insights to offer decision support for COVID-19 related issues. A holistic treatment of the Analytics process from data acquisition and cleansing to data analysis and interpretation is emphasized using five studies:
Characterize COVID-19 mortality demographic risk factors,
Visualize COVID-19 mortality demographics,
Conduct COVID-19 mortality time series forecasting,
Predict COVID-19 mortality, and
Analyze COVID-19 vaccine acceptance, uptake, and experiences.

Each study is structured with guiding questions to engage students to think critically, relate Analytic concepts to the given situation, and arrive at their own answers/solutions for active knowledge exploration and discovery.

This course is an introduction to principles and techniques of visual communication, and provides opportunities for science and engineering majors to acquire practical skills in the visual computer arts, in a studio environment. Students will learn how to create graphics for print and web, animations, and interactive media, and how to use these techniques to effectively communicate scientific and engineering concepts for learning and teaching. This class involves three hands-on creative projects, which will be presented in class.