https://speakerdeck.com/minecr 2024-09-23 11:32:19 -0400 Statistics education stands at a critical juncture as we navigate the intersection of traditional statistical theory, modern computational approaches, and emerging AI technologies. This talk examines how statisticians can reimagine curricula by embracing computation as a foundational element rather than an afterthought. While traditional statistics education has prioritized theoretical frameworks and applications, computation has emerged as the backbone of contemporary data analysis—from data acquisition and wrangling to visualization, modeling, and communication. Now, AI tools are further transforming this landscape, creating both opportunities and challenges for statistics and data science educators. The presentation will outline a forward-looking curriculum model for introductory courses that balances statistical thinking, data science methods, and explicit computational instruction. Statistics education stands at a critical juncture as we navigate the intersection of traditional statistical theory, modern computational approaches, and emerging AI technologies. This talk examines how statisticians can reimagine curricula by embracing computation as a foundational element rather than an afterthought. While traditional statistics education has prioritized theoretical frameworks and applications, computation has emerged as the backbone of contemporary data analysis—from data acquisition and wrangling to visualization, modeling, and communication. Now, AI tools are further transforming this landscape, creating both opportunities and challenges for statistics and data science educators. The presentation will outline a forward-looking curriculum model for introductory courses that balances statistical thinking, data science methods, and explicit computational instruction. Tue, 21 Jul 2026 00:00:00 -0400 https://speakerdeck.com/minecr/the-future-of-statistics-education-a-computational-perspective-8c9f60b1-5291-44fc-abb0-ab6de97cf75b https://speakerdeck.com/minecr/the-future-of-statistics-education-a-computational-perspective-8c9f60b1-5291-44fc-abb0-ab6de97cf75b AI tools can now generate a polished ggplot in seconds, scaffold an entire Quarto report from a prompt, and debug a dplyr pipeline faster than most students can type. This raises a reasonable question for statistics and data science educators: should we still be asking students to learn to do these things themselves? I argue yes — and that the case for teaching programming and reproducible workflows is stronger than ever. When AI produces code freely, mastery becomes the differentiator: the ability to read a generated script and assess whether it is doing what you think it is doing, to recognize when a pipeline quietly produces the wrong answer, and to structure an analysis so that every step can be followed and verified. Beyond code correctness, learning modern data science workflows teaches students to think with data — to ask sharper questions, notice what a dataset can and cannot answer, and build the habits of mind that underpin rigorous reasoning about uncertainty and statistical modeling. These are the foundations of statistical thinking; the code is how we practice them. Reproducibility, in this framing, is not a technical nicety but a cornerstone of scientific integrity — and tools like version control that enforce it turn out to be essential for working productively with AI as well. Drawing on experience designing introductory data science courses and curricula, this talk presents a case for reframing programming instruction in the AI era — not as syntax acquisition, but as a pathway to statistical thinking. It features concrete code and workflow examples alongside ideas for assignments and assessments designed for a moment when a plausible-looking answer is only a prompt away, and where the real pedagogical challenge is crafting tasks that reward genuine understanding over fluent generation. The goal is not to keep AI out of the classroom, but to produce graduates who can do more than write a good prompt and hope for the best. AI tools can now generate a polished ggplot in seconds, scaffold an entire Quarto report from a prompt, and debug a dplyr pipeline faster than most students can type. This raises a reasonable question for statistics and data science educators: should we still be asking students to learn to do these things themselves? I argue yes — and that the case for teaching programming and reproducible workflows is stronger than ever. When AI produces code freely, mastery becomes the differentiator: the ability to read a generated script and assess whether it is doing what you think it is doing, to recognize when a pipeline quietly produces the wrong answer, and to structure an analysis so that every step can be followed and verified. Beyond code correctness, learning modern data science workflows teaches students to think with data — to ask sharper questions, notice what a dataset can and cannot answer, and build the habits of mind that underpin rigorous reasoning about uncertainty and statistical modeling. These are the foundations of statistical thinking; the code is how we practice them. Reproducibility, in this framing, is not a technical nicety but a cornerstone of scientific integrity — and tools like version control that enforce it turn out to be essential for working productively with AI as well. Drawing on experience designing introductory data science courses and curricula, this talk presents a case for reframing programming instruction in the AI era — not as syntax acquisition, but as a pathway to statistical thinking. It features concrete code and workflow examples alongside ideas for assignments and assessments designed for a moment when a plausible-looking answer is only a prompt away, and where the real pedagogical challenge is crafting tasks that reward genuine understanding over fluent generation. The goal is not to keep AI out of the classroom, but to produce graduates who can do more than write a good prompt and hope for the best. Sun, 12 Jul 2026 00:00:00 -0400 https://speakerdeck.com/minecr/beyond-the-prompt-programming-as-a-pathway-to-statistical-thinking https://speakerdeck.com/minecr/beyond-the-prompt-programming-as-a-pathway-to-statistical-thinking A considerable recent challenge for learners and teachers of data science courses is the proliferation of the use of LLM-based tools in generating answers. In this talk, I will introduce an R package that leverages LLMs to produce immediate feedback on student work to motivate them to give it a try themselves first. I will discuss technical details of augmenting models with course materials, backend and user interface decisions, challenges around evaluations that are not done correctly by the LLM, and student feedback from the first set of users. Finally, I will touch on incorporating this tool into low-stakes assessment and ethical considerations for the formal assessment structure of the course relying on LLMs. A considerable recent challenge for learners and teachers of data science courses is the proliferation of the use of LLM-based tools in generating answers. In this talk, I will introduce an R package that leverages LLMs to produce immediate feedback on student work to motivate them to give it a try themselves first. I will discuss technical details of augmenting models with course materials, backend and user interface decisions, challenges around evaluations that are not done correctly by the LLM, and student feedback from the first set of users. Finally, I will touch on incorporating this tool into low-stakes assessment and ethical considerations for the formal assessment structure of the course relying on LLMs. Thu, 28 May 2026 00:00:00 -0400 https://speakerdeck.com/minecr/leveraging-llms-for-student-feedback-in-introductory-data-science-courses-277873b4-43c9-4722-85c7-84da5ec0e7f6 https://speakerdeck.com/minecr/leveraging-llms-for-student-feedback-in-introductory-data-science-courses-277873b4-43c9-4722-85c7-84da5ec0e7f6 Statistics education stands at a critical juncture as we navigate the intersection of traditional statistical theory, modern computational approaches, and emerging AI technologies. This talk examines how statisticians can reimagine curricula by embracing computation as a foundational element rather than an afterthought. While traditional statistics education has prioritized theoretical frameworks and applications, computation has emerged as the backbone of contemporary data analysis—from data acquisition and wrangling to visualization, modeling, and communication. Now, AI tools are further transforming this landscape, creating both opportunities and challenges for statistics and data science educators. The presentation will outline a forward-looking curriculum model for introductory courses that balances statistical thinking, data science methods, and explicit computational instruction. Statistics education stands at a critical juncture as we navigate the intersection of traditional statistical theory, modern computational approaches, and emerging AI technologies. This talk examines how statisticians can reimagine curricula by embracing computation as a foundational element rather than an afterthought. While traditional statistics education has prioritized theoretical frameworks and applications, computation has emerged as the backbone of contemporary data analysis—from data acquisition and wrangling to visualization, modeling, and communication. Now, AI tools are further transforming this landscape, creating both opportunities and challenges for statistics and data science educators. The presentation will outline a forward-looking curriculum model for introductory courses that balances statistical thinking, data science methods, and explicit computational instruction. Tue, 26 May 2026 00:00:00 -0400 https://speakerdeck.com/minecr/the-future-of-statistics-education-a-computational-perspective-a8230056-a4a3-431e-b8d1-53364913d988 https://speakerdeck.com/minecr/the-future-of-statistics-education-a-computational-perspective-a8230056-a4a3-431e-b8d1-53364913d988 Statistics education stands at a critical juncture as we navigate the intersection of traditional statistical theory, modern computational approaches, and emerging AI technologies. This talk examines how statisticians can reimagine curricula by embracing computation as a foundational element rather than an afterthought. While traditional statistics education has prioritized theoretical frameworks and applications, computation has emerged as the backbone of contemporary data analysis—from data acquisition and wrangling to visualization, modeling, and communication. Now, AI tools are further transforming this landscape, creating both opportunities and challenges for statistics and data science educators. The presentation will outline a forward-looking curriculum model for introductory courses that balances statistical thinking, data science methods, and explicit computational instruction. Statistics education stands at a critical juncture as we navigate the intersection of traditional statistical theory, modern computational approaches, and emerging AI technologies. This talk examines how statisticians can reimagine curricula by embracing computation as a foundational element rather than an afterthought. While traditional statistics education has prioritized theoretical frameworks and applications, computation has emerged as the backbone of contemporary data analysis—from data acquisition and wrangling to visualization, modeling, and communication. Now, AI tools are further transforming this landscape, creating both opportunities and challenges for statistics and data science educators. The presentation will outline a forward-looking curriculum model for introductory courses that balances statistical thinking, data science methods, and explicit computational instruction. Mon, 04 May 2026 00:00:00 -0400 https://speakerdeck.com/minecr/the-future-of-statistics-education-a-computational-perspective-uw https://speakerdeck.com/minecr/the-future-of-statistics-education-a-computational-perspective-uw A considerable recent challenge for learners and teachers of data science courses is the proliferation of the use of LLM-based tools in generating answers. In this talk, I will introduce an R package that leverages LLMs to produce immediate feedback on student work to motivate them to give it a try themselves first. I will discuss technical details of augmenting models with course materials, backend and user interface decisions, challenges around evaluations that are not done correctly by the LLM, and student feedback from the first set of users. Finally, I will touch on incorporating this tool into low-stakes assessment and ethical considerations for the formal assessment structure of the course relying on LLMs. A considerable recent challenge for learners and teachers of data science courses is the proliferation of the use of LLM-based tools in generating answers. In this talk, I will introduce an R package that leverages LLMs to produce immediate feedback on student work to motivate them to give it a try themselves first. I will discuss technical details of augmenting models with course materials, backend and user interface decisions, challenges around evaluations that are not done correctly by the LLM, and student feedback from the first set of users. Finally, I will touch on incorporating this tool into low-stakes assessment and ethical considerations for the formal assessment structure of the course relying on LLMs. Tue, 10 Mar 2026 00:00:00 -0400 https://speakerdeck.com/minecr/leveraging-llms-for-student-feedback-in-introductory-data-science-courses-wsc https://speakerdeck.com/minecr/leveraging-llms-for-student-feedback-in-introductory-data-science-courses-wsc Statistics education stands at a critical juncture as we navigate the intersection of traditional statistical theory, modern computational approaches, and emerging AI technologies. This talk examines how statisticians can reimagine curricula by embracing computation as a foundational element rather than an afterthought. While traditional statistics education has prioritized theoretical frameworks and applications, computation has emerged as the backbone of contemporary data analysis—from data acquisition and wrangling to visualization, modeling, and communication. Now, AI tools are further transforming this landscape, creating both opportunities and challenges for statistics and data science educators. The presentation will outline a forward-looking curriculum model for introductory courses that balances statistical thinking, data science methods, and explicit computational instruction. Statistics education stands at a critical juncture as we navigate the intersection of traditional statistical theory, modern computational approaches, and emerging AI technologies. This talk examines how statisticians can reimagine curricula by embracing computation as a foundational element rather than an afterthought. While traditional statistics education has prioritized theoretical frameworks and applications, computation has emerged as the backbone of contemporary data analysis—from data acquisition and wrangling to visualization, modeling, and communication. Now, AI tools are further transforming this landscape, creating both opportunities and challenges for statistics and data science educators. The presentation will outline a forward-looking curriculum model for introductory courses that balances statistical thinking, data science methods, and explicit computational instruction. Mon, 09 Mar 2026 00:00:00 -0400 https://speakerdeck.com/minecr/the-future-of-statistics-education-a-computational-perspective-69a94e1a-3605-42d4-bce0-24684faeafc5 https://speakerdeck.com/minecr/the-future-of-statistics-education-a-computational-perspective-69a94e1a-3605-42d4-bce0-24684faeafc5 Statistics education stands at a critical juncture as we navigate the intersection of traditional statistical theory, modern computational approaches, and emerging AI technologies. This talk examines how statisticians can reimagine curricula by embracing computation as a foundational element rather than an afterthought. While traditional statistics education has prioritized theoretical frameworks and applications, computation has emerged as the backbone of contemporary data analysis—from data acquisition and wrangling to visualization, modeling, and communication. Now, AI tools are further transforming this landscape, creating both opportunities and challenges for statistics and data science educators. The presentation will outline a forward-looking curriculum model for introductory courses that balances statistical thinking, data science methods, and explicit computational instruction. Statistics education stands at a critical juncture as we navigate the intersection of traditional statistical theory, modern computational approaches, and emerging AI technologies. This talk examines how statisticians can reimagine curricula by embracing computation as a foundational element rather than an afterthought. While traditional statistics education has prioritized theoretical frameworks and applications, computation has emerged as the backbone of contemporary data analysis—from data acquisition and wrangling to visualization, modeling, and communication. Now, AI tools are further transforming this landscape, creating both opportunities and challenges for statistics and data science educators. The presentation will outline a forward-looking curriculum model for introductory courses that balances statistical thinking, data science methods, and explicit computational instruction. Tue, 24 Feb 2026 00:00:00 -0500 https://speakerdeck.com/minecr/the-future-of-statistics-education-a-computational-perspective-itsa https://speakerdeck.com/minecr/the-future-of-statistics-education-a-computational-perspective-itsa A considerable recent challenge for learners and teachers of data science courses is the increasing use of LLM-based tools in generating answers. In this talk, I will introduce an R package that leverages LLMs to produce immediate feedback on student work to motivate them to give it a try themselves first. I will discuss the technical details of augmenting models with course materials, as well as backend and user interface decisions, challenges surrounding evaluations that are not performed correctly by the LLM, and student feedback from the first set of users. Finally, I will discuss incorporating this tool into low-stakes assessments and address ethical considerations for the formal assessment structure of the course, which relies on LLMs. A considerable recent challenge for learners and teachers of data science courses is the increasing use of LLM-based tools in generating answers. In this talk, I will introduce an R package that leverages LLMs to produce immediate feedback on student work to motivate them to give it a try themselves first. I will discuss the technical details of augmenting models with course materials, as well as backend and user interface decisions, challenges surrounding evaluations that are not performed correctly by the LLM, and student feedback from the first set of users. Finally, I will discuss incorporating this tool into low-stakes assessments and address ethical considerations for the formal assessment structure of the course, which relies on LLMs. Fri, 16 Jan 2026 00:00:00 -0500 https://speakerdeck.com/minecr/leveraging-llms-for-student-feedback-in-introductory-data-science-courses-stats-up-ai https://speakerdeck.com/minecr/leveraging-llms-for-student-feedback-in-introductory-data-science-courses-stats-up-ai A considerable recent challenge for learners and teachers of data science courses is the proliferation of the use of LLM-based tools in generating answers. In this talk, I will introduce an R package that leverages LLMs to produce immediate feedback on student work to motivate them to give it a try themselves first. I will discuss technical details of augmenting models with course materials, backend and user interface decisions, challenges around evaluations that are not done correctly by the LLM, and student feedback from the first set of users. Finally, I will touch on incorporating this tool into low-stakes assessment and ethical considerations for the formal assessment structure of the course relying on LLMs. A considerable recent challenge for learners and teachers of data science courses is the proliferation of the use of LLM-based tools in generating answers. In this talk, I will introduce an R package that leverages LLMs to produce immediate feedback on student work to motivate them to give it a try themselves first. I will discuss technical details of augmenting models with course materials, backend and user interface decisions, challenges around evaluations that are not done correctly by the LLM, and student feedback from the first set of users. Finally, I will touch on incorporating this tool into low-stakes assessment and ethical considerations for the formal assessment structure of the course relying on LLMs. Wed, 17 Sep 2025 00:00:00 -0400 https://speakerdeck.com/minecr/leveraging-llms-for-student-feedback-in-introductory-data-science-courses-posit-conf-2025 https://speakerdeck.com/minecr/leveraging-llms-for-student-feedback-in-introductory-data-science-courses-posit-conf-2025 A considerable recent challenge for learners and teachers of data science courses is the proliferation of the use of LLM-based tools in generating answers. In this talk, I will introduce an R package that leverages LLMs to produce immediate feedback on student work to motivate them to give it a try themselves first. I will discuss technical details of augmenting models with course materials, backend and user interface decisions, challenges around evaluations that are not done correctly by the LLM, and student feedback from the first set of users. Finally, I will touch on incorporating this tool into low-stakes assessment and ethical considerations for the formal assessment structure of the course relying on LLMs. A considerable recent challenge for learners and teachers of data science courses is the proliferation of the use of LLM-based tools in generating answers. In this talk, I will introduce an R package that leverages LLMs to produce immediate feedback on student work to motivate them to give it a try themselves first. I will discuss technical details of augmenting models with course materials, backend and user interface decisions, challenges around evaluations that are not done correctly by the LLM, and student feedback from the first set of users. Finally, I will touch on incorporating this tool into low-stakes assessment and ethical considerations for the formal assessment structure of the course relying on LLMs. Sat, 09 Aug 2025 00:00:00 -0400 https://speakerdeck.com/minecr/leveraging-llms-for-student-feedback-in-introductory-data-science-courses-user-2025 https://speakerdeck.com/minecr/leveraging-llms-for-student-feedback-in-introductory-data-science-courses-user-2025 Statistics education stands at a critical juncture as we navigate the intersection of traditional statistical theory, modern computational approaches, and emerging AI technologies. This talk examines how statisticians can reimagine curricula by embracing computation as foundational elements rather than afterthoughts. While traditional statistics education has prioritized theoretical frameworks and applications, computation has emerged as the backbone of contemporary data analysis—from data acquisition and wrangling to visualization, modeling, and communication. Now, AI tools are further transforming this landscape, creating both opportunities and challenges for statistics and data science educators. The presentation will outline a forward-looking curriculum model for introductory courses that balances statistical thinking, data science methods, and explicit computational instruction. Statistics education stands at a critical juncture as we navigate the intersection of traditional statistical theory, modern computational approaches, and emerging AI technologies. This talk examines how statisticians can reimagine curricula by embracing computation as foundational elements rather than afterthoughts. While traditional statistics education has prioritized theoretical frameworks and applications, computation has emerged as the backbone of contemporary data analysis—from data acquisition and wrangling to visualization, modeling, and communication. Now, AI tools are further transforming this landscape, creating both opportunities and challenges for statistics and data science educators. The presentation will outline a forward-looking curriculum model for introductory courses that balances statistical thinking, data science methods, and explicit computational instruction. Thu, 10 Jul 2025 00:00:00 -0400 https://speakerdeck.com/minecr/the-future-of-statistics-education-a-computational-perspective-dsc https://speakerdeck.com/minecr/the-future-of-statistics-education-a-computational-perspective-dsc For the Teaching with AI Showcase in the Triangle AI Summit For the Teaching with AI Showcase in the Triangle AI Summit Fri, 30 May 2025 00:00:00 -0400 https://speakerdeck.com/minecr/leveraging-llms-for-student-feedback-in-introductory-data-science-courses https://speakerdeck.com/minecr/leveraging-llms-for-student-feedback-in-introductory-data-science-courses Lightning talk at ICERM: Applied Math in Statistics and Data Science Education Lightning talk at ICERM: Applied Math in Statistics and Data Science Education Tue, 20 May 2025 00:00:00 -0400 https://speakerdeck.com/minecr/help-from-ai https://speakerdeck.com/minecr/help-from-ai Statistics education stands at a critical juncture as we navigate the intersection of traditional statistical theory, modern computational approaches, and emerging AI technologies. This talk examines how statisticians can reimagine curricula by embracing computation as a foundational element rather than an afterthought. While traditional statistics education has prioritized theoretical frameworks and applications, computation has emerged as the backbone of contemporary data analysis, from data acquisition and wrangling to visualization, modeling, and communication. Now, AI tools are further transforming this landscape, creating both opportunities and challenges for statistics and data science educators. The presentation will outline a forward-looking curriculum model for introductory courses that balances statistical thinking, data science methods, and explicit computational instruction. Statistics education stands at a critical juncture as we navigate the intersection of traditional statistical theory, modern computational approaches, and emerging AI technologies. This talk examines how statisticians can reimagine curricula by embracing computation as a foundational element rather than an afterthought. While traditional statistics education has prioritized theoretical frameworks and applications, computation has emerged as the backbone of contemporary data analysis, from data acquisition and wrangling to visualization, modeling, and communication. Now, AI tools are further transforming this landscape, creating both opportunities and challenges for statistics and data science educators. The presentation will outline a forward-looking curriculum model for introductory courses that balances statistical thinking, data science methods, and explicit computational instruction. Tue, 20 May 2025 00:00:00 -0400 https://speakerdeck.com/minecr/the-future-of-statistics-education-a-computational-perspective-65573561-4c42-4dc3-9681-ac64c780c5e9 https://speakerdeck.com/minecr/the-future-of-statistics-education-a-computational-perspective-65573561-4c42-4dc3-9681-ac64c780c5e9 Statistics education stands at a critical juncture as we navigate the intersection of traditional statistical theory, modern computational approaches, and emerging AI technologies. This talk examines how statisticians can reimagine curricula by embracing computation as foundational elements rather than afterthoughts. While traditional statistics education has prioritized theoretical frameworks and applications, computation has emerged as the backbone of contemporary data analysis—from data acquisition and wrangling to visualization, modeling, and communication. Now, AI tools are further transforming this landscape, creating both opportunities and challenges for statistics and data science educators. The presentation will outline a forward-looking curriculum model for introductory courses that balances statistical thinking, data science methods, and explicit computational instruction. Talk at UBC Statistics. Statistics education stands at a critical juncture as we navigate the intersection of traditional statistical theory, modern computational approaches, and emerging AI technologies. This talk examines how statisticians can reimagine curricula by embracing computation as foundational elements rather than afterthoughts. While traditional statistics education has prioritized theoretical frameworks and applications, computation has emerged as the backbone of contemporary data analysis—from data acquisition and wrangling to visualization, modeling, and communication. Now, AI tools are further transforming this landscape, creating both opportunities and challenges for statistics and data science educators. The presentation will outline a forward-looking curriculum model for introductory courses that balances statistical thinking, data science methods, and explicit computational instruction. Talk at UBC Statistics. Tue, 13 May 2025 00:00:00 -0400 https://speakerdeck.com/minecr/the-future-of-statistics-education-a-computational-perspective https://speakerdeck.com/minecr/the-future-of-statistics-education-a-computational-perspective In the age of data science, traditional statistical methods are crucial, but they are increasingly combined with computational tools and predictive modeling techniques. This talk highlights Duke University's large introductory data science course, which provides students with a strong foundation in exploratory data analysis encompassing data importing, visualization, transformation, and summarization, as well as statistical inference and descriptive and predictive modeling techniques using the R programming language. The course emphasizes real-world applications, ethical considerations, and the importance of reproducibility in data analysis. By integrating classical statistical theory with modern computational approaches, the course equips students to succeed in a data-driven world. We will share the pedagogical strategies, challenges, and successes in preparing students for careers in data science. In the age of data science, traditional statistical methods are crucial, but they are increasingly combined with computational tools and predictive modeling techniques. This talk highlights Duke University's large introductory data science course, which provides students with a strong foundation in exploratory data analysis encompassing data importing, visualization, transformation, and summarization, as well as statistical inference and descriptive and predictive modeling techniques using the R programming language. The course emphasizes real-world applications, ethical considerations, and the importance of reproducibility in data analysis. By integrating classical statistical theory with modern computational approaches, the course equips students to succeed in a data-driven world. We will share the pedagogical strategies, challenges, and successes in preparing students for careers in data science. Fri, 01 Nov 2024 00:00:00 -0400 https://speakerdeck.com/minecr/statistics-in-the-age-of-data-science-tsu https://speakerdeck.com/minecr/statistics-in-the-age-of-data-science-tsu In the age of data science, traditional statistical methods are crucial, but they are increasingly combined with computational tools and predictive modeling techniques. This talk highlights Duke University's large introductory data science course, which provides students with a strong foundation in exploratory data analysis encompassing data importing, visualization, transformation, and summarization, as well as statistical inference and descriptive and predictive modeling techniques using the R programming language. The course emphasizes real-world applications, ethical considerations, and the importance of reproducibility in data analysis. By integrating classical statistical theory with modern computational approaches, the course equips students to succeed in a data-driven world. We will share the pedagogical strategies, challenges, and successes in preparing students for careers in data science. In the age of data science, traditional statistical methods are crucial, but they are increasingly combined with computational tools and predictive modeling techniques. This talk highlights Duke University's large introductory data science course, which provides students with a strong foundation in exploratory data analysis encompassing data importing, visualization, transformation, and summarization, as well as statistical inference and descriptive and predictive modeling techniques using the R programming language. The course emphasizes real-world applications, ethical considerations, and the importance of reproducibility in data analysis. By integrating classical statistical theory with modern computational approaches, the course equips students to succeed in a data-driven world. We will share the pedagogical strategies, challenges, and successes in preparing students for careers in data science. Mon, 23 Sep 2024 00:00:00 -0400 https://speakerdeck.com/minecr/statistics-in-the-age-of-data-science-b5b46e0f-3eb1-40d6-a9c8-c3785f945101 https://speakerdeck.com/minecr/statistics-in-the-age-of-data-science-b5b46e0f-3eb1-40d6-a9c8-c3785f945101