Related papers: Teaching Data Science
Causal inference from observational data is the goal of many data analyses in the health and social sciences. However, academic statistics has often frowned upon data analyses with a causal objective. The introduction of the term "data…
The field of data science currently enjoys a broad definition that includes a wide array of activities which borrow from many other established fields of study. Having such a vague characterization of a field in the early stages might be…
We introduce a newly designed undergraduate-level interdisciplinary course in scientific computing that aims to prepare students as the next generation of research-oriented computational scientists and engineers. The course offers students…
In this article we describe how we successfully incorporated data analysis in Python in a first-year laboratory course without significantly altering the course structure and without overburdening students. We show how we created and used…
The unprecedented growth in the availability of data of all types and qualities and the emergence of the field of data science has provided an impetus to finally realizing the implementation of the full breadth of the Nolan and Temple Lang…
The 2016 Guidelines for Assessment and Instruction in Statistics Education (GAISE) College Report emphasized six recommendations to teach introductory courses in statistics. Among them: use of real data with context and purpose. Many…
Should one teach coding in a required introductory statistics and data science class for non-major students? Many professors advise against it, considering it a distraction from the important and challenging statistical topics that need to…
The aim of this paper is to present a set of Python-based tools to develop forecasts using time series data sets. The material is based on a four week course that the author has taught for seven years to students on operations research,…
Teaching data science presents unique challenges and opportunities that cannot be fully addressed by simply borrowing pedagogical strategies from its parent disciplines of statistics and computer science. Here, we present ten simple rules…
The discipline of data science emerged to combine statistical methods with computing. At Aalto University, Finland, we have taken first steps to bring educational data science as a part of daily operations of Management Information…
The undergraduate data science curriculum at the University of California, Berkeley is anchored in five new courses that emphasize computational thinking, inferential thinking, and working on real-world problems. We believe that…
Data and Science has stood out in the generation of results, whether in the projects of the scientific domain or business domain. CERN Project, Scientific Institutes, companies like Walmart, Google, Apple, among others, need data to present…
While recent years have seen a growing interest in accessible visualization tools and techniques for blind people, little attention is paid to the learning opportunities and teaching strategies of data science and visualization tailored for…
As data science emerges as a distinct academic discipline, introductory data science (IDS) courses have also drawn attention to their role in providing foundational knowledge of data science to students. IDS courses not only help students…
It is becoming increasingly important that physics educators equip their students with the skills to work with data effectively. However, many educators may lack the necessary training and expertise in data science to teach these skills. To…
The twenty-first century has ushered in the age of big data and data economy, in which data DNA, which carries important knowledge, insights and potential, has become an intrinsic constituent of all data-based organisms. An appropriate…
In an increasingly data-driven world, facility with statistics is more important than ever for our students. At institutions without a statistician, it often falls to the mathematics faculty to teach statistics courses. This paper presents…
As data have become more prevalent in academia, industry, and daily life, it is imperative that undergraduate students are equipped with the skills needed to analyze data in the modern environment. In recent years there has been a lot of…
Modern students encounter big, messy data sets long before setting foot in our classrooms. Many of our students need to develop skills in exploratory data analysis and multivariate analysis techniques for their jobs after college, but these…
While data science has emerged as a contentious new scientific field, enormous debates and discussions have been made on it why we need data science and what makes it as a science. In reviewing hundreds of pieces of literature which include…