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Ainsley Miller; Kate Pyper – Journal of Statistics and Data Science Education, 2024
R is becoming the standard for teaching statistics due to its flexibility, and open-source nature, replacing software programs like Minitab and SPSS. The main driver for reform within Scottish statistical undergraduate programs is the creation of the Scottish Qualification Authority's Higher Applications of Mathematics course which has statistics…
Descriptors: College Freshmen, Undergraduate Study, Anxiety, Programming Languages
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Kim, Brian; Henke, Graham – Journal of Statistics and Data Science Education, 2021
One of the biggest hurdles of teaching data science and programming techniques to beginners is simply getting started with the technology. With multiple versions of the same coding language available (e.g., Python 2 and Python 3), various additional libraries and packages to install, as well as integrated development environments to navigate, the…
Descriptors: Computer Software, Data Analysis, Programming Languages, Computer Science Education
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Holman, Justin O.; Hacherl, Allie – Journal of Statistics and Data Science Education, 2023
It has become increasingly important for future business professionals to understand statistical computing methods as data science has gained widespread use in contemporary organizational decision processes in recent years. Used by scores of academics and practitioners in a variety of fields, Monte Carlo simulation is one of the most broadly…
Descriptors: Teaching Methods, Monte Carlo Methods, Programming Languages, Statistics Education
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Hoffman, Heather J.; Elmi, Angelo F. – Journal of Statistics and Data Science Education, 2021
Teaching students statistical programming languages while simultaneously teaching them how to debug erroneous code is challenging. The traditional programming course focuses on error-free learning in class while students' experiences outside of class typically involve error-full learning. While error-free teaching consists of focused lectures…
Descriptors: Statistics Education, Programming Languages, Troubleshooting, Coding
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Thompson, JaCoya; Arastoopour Irgens, Golnaz – Journal of Statistics and Data Science Education, 2022
Data science is a highly interdisciplinary field that comprises various principles, methodologies, and guidelines for the analysis of data. The creation of appropriate curricula that use computational tools and teaching activities is necessary for building skills and knowledge in data science. However, much of the literature about data science…
Descriptors: Data Analysis, Middle School Students, Statistics Education, Student Centered Learning
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Schwab-McCoy, Aimee; Baker, Catherine M.; Gasper, Rebecca E. – Journal of Statistics and Data Science Education, 2021
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 students, the road map for teaching data science remains elusive. In 2019, 69 college and university faculty teaching data science courses and…
Descriptors: Statistics Education, Higher Education, College Students, Teaching Methods
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Wang, Sabrina Luxin; Zhang, Anna Yinqi; Messer, Samuel; Wiesner, Andrew; Pearl, Dennis K. – Journal of Statistics and Data Science Education, 2021
This article describes a suite of student-created Shiny apps for teaching statistics and a field test of their short-term effectiveness. To date, more than 50 Shiny apps and a growing collection of associated lesson plans, designed to enrich the teaching of both introductory and upper division statistics courses, have been developed. The apps are…
Descriptors: Student Centered Learning, Teaching Methods, Statistics Education, Introductory Courses
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Curley, Brenna; Peterson, Anna – Journal of Statistics and Data Science Education, 2022
In this article, we outline several activities revolving around soccer players who participated in the 2018 FIFA World Cup and 2019 FIFA Women's World Cup. Classroom activities are described from different perspectives, useful for a range of different statistics courses. In a first semester probability theory course, students investigate the…
Descriptors: Team Sports, Competition, Teaching Methods, Data Analysis
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Gerbing, David W. – Journal of Statistics and Data Science Education, 2021
R and Python are commonly used software languages for data analytics. Using these languages as the course software for the introductory course gives students practical skills for applying statistical concepts to data analysis. However, the reliance upon the command line is perceived by the typical nontechnical introductory student as sufficiently…
Descriptors: Statistics Education, Teaching Methods, Introductory Courses, Programming Languages
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Savonen, Candace; Wright, Carrie; Hoffman, Ava M.; Muschelli, John; Cox, Katherine; Tan, Frederick J.; Leek, Jeffrey T. – Journal of Statistics and Data Science Education, 2023
Data science and informatics tools are developing at a blistering rate, but their users often lack the educational background or resources to efficiently apply the methods to their research. Training resources and vignettes that accompany these tools often deprecate because their maintenance is not prioritized by funding, giving teams little time…
Descriptors: Open Source Technology, Multiple Choice Tests, Summative Evaluation, Formative Evaluation
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Reinhart, Alex; Genovese, Christopher R. – Journal of Statistics and Data Science Education, 2021
Traditionally, statistical computing courses have taught the syntax of a particular programming language or specific statistical computation methods. Since Nolan and Temple Lang's seminal paper, we have seen a greater emphasis on data wrangling, reproducible research, and visualization. This shift better prepares students for careers working with…
Descriptors: Computer Software, Graduate Students, Computer Science Education, Statistics Education
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Beckman, Matthew D.; Çetinkaya-Rundel, Mine; Horton, Nicholas J.; Rundel, Colin W.; Sullivan, Adam J.; Tackett, Maria – Journal of Statistics and Data Science Education, 2021
A version control system records changes to a file or set of files over time so that changes can be tracked and specific versions of a file can be recalled later. As such, it is an essential element of a reproducible workflow that deserves due consideration among the learning objectives of statistics courses. This article describes experiences and…
Descriptors: Statistics Education, Data Analysis, Teaching Methods, Graduate Students