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Introductory data science across disciplines, using Python, case studies, and industry consulting projects
Abstract Data and its applications are increasingly ubiquitous in the rapidly digitizing world and consequently, students across different disciplines face increasing demand to develop skills to answer both academia's and businesses' increasing need to collect, manage, evaluate, apply and extract knowledge from data and critically reflect upon the derived insights. On the basis of recent experiences at the University of Ttingen, Germany, we present a new approach to teach the relevant data science skills as an introductory service course at the university or advanced college level. We describe the outline of a complete course that relies on case studies and project work built around contemporary data sets, including openly available online teaching resources.
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 ...
Broadening Access to Data Science Education: Open Source Tools and Training in High School and Higher Education · Issue 8.3, Summer 2026
As data play a more integral part to our daily lives, there is a growing need for data science education. However, access to curriculum, tooling, and infrastructure is not readily available to many students in the United States. We review the current state of data science education tools and implementations as well as highlight a set of emerging tools and technologies. We conclude that broadening data science education requires a multifaceted approach, involving technological accessibility, instructional equity, curricular relevance, and long-term sustainability.
Broadening Access to Data Science Education in High School and Higher Education through Open Source Tools and Training
As data plays a more integral part to our daily lives, there is a growing need for data science education. However, access to curriculum, tooling, and infrastructure is not readily available to many students in the U.S. We review the current state of data science education tools and implementations as well as highlight a set of emerging tools and technologies. We conclude that broadening data science education requires a multifaceted approach, involving technological accessibility, instructional equity, curricular relevance, and long-term sustainability.
Building Bridges for Data Science Education
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Teaching Programming for First-Year Data Science
This summary was generated using automated tools and was not authored or reviewed by the article's author(s). It is provided to support discovery, help readers assess relevance, and assist readers from adjacent research areas in understanding the work. It is intended to complement the author-supplied abstract, which remains the primary summary of the paper. The full article remains the authoritative version of record. Click here to learn more.
12 Python Data Visualization Libraries to Explore for Business Analysis | Mode
This list is an overview of 10 interdisciplinary Python data visualization libraries including matplotlib, Seaborn, Plotly, Bokeh, pygal, geoplotlib, & more.
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.
Understanding the Data in K-12 Data Science · Issue 6.2, Spring 2024
Our increasingly data-driven world is amplifying the need for everyone to develop foundational data literacy skills. In response, a growing number of K-12 data science curricula are being designed to introduce all students to data. These curricula define what data science is at the high school level and directly shape how students are introduced to and understand the discipline. Ensuring these curricula are effective, engaging, and, most critically, equitable is of paramount importance. This article presents a qualitative analysis of four curricula, focusing on the data used to introduce learners to the field of data science. The analysis uses a series of analytical lenses to evaluate the 296 distinct data sets used across the curricula and identifies trends and best practices in data set selection. The analysis includes using data collected from high school students about their interests and experiences with data to understand if and how contemporary data science curricula are tapping into students’ lived experiences to situate data science learning experiences. The findings show that the curricula use relatively recent and small data sets covering a range of topics and that learner involvement in data set selection is limited. Further, the analysis reveals gaps between the data sets used and students' self-reported interests. This work highlights the importance of data set selection, especially as it relates to supporting learners from historically excluded populations in technology fields. Finally, this article provides practical implications to assess existing curricula and advances our understanding of how to situate the field of data science in the interests, ideas, and values of today’s students.
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.
Coding Club: a positive peer-learning community
Free and accessible tutorials on data analysis, manipulation, visualisation and more.
3 Powerful Takeaways from the National Workshop on Data Science Education
As the field flourishes, there is much we can do to benefit its countless students.
Highlights of the National Academies Report on "Undergraduate Data Science: Opportunities and Options” · Issue 1.1, Summer 2019
An interview with Laura Haas and Alfred Hero by Robert Lue
pandas-dev/pandas: Pandas
Pandas is a powerful data structures for data analysis, time series, and statistics.
Dynamic Data Science
<p>Use our Common Online Data Analysis Platform (CODAP) to explore dynamic data science activities and gain fluency in data moves to examine large datasets.</p>
Data Science in 2020: Computing, Curricula, and Challenges for the Next 10 Years
In the past 10 years, new data science courses and programs have proliferated at the collegiate level. As faculty and administrators enter the race to provide data science training and attract new ...
