Teaching Stats for Data Science

“Data science” is a useful catchword for methods and concepts original to the field of statistics, but typically being applied to large, multivariate, observational records. Such datasets call for techniques not often part of an introduction to statistics: modeling, consideration of covariates, sophisticated visualization, and causal reasoning. This article re-imagines introductory statistics as an introduction to data science and proposes a sequence of 10 blocks that together compose a suitable course for extracting information from contemporary data. Recent extensions to the mosaic packages for R together with tools from the “tidyverse” provide a concise and readable notation for wrangling, visualization, model-building, and model interpretation: the fundamental computational tasks of data science

Models for Paired Comparison Data: A Review with Emphasis on Dependent Data

Thurstonian and Bradley-Terry models are the most commonly applied models in the analysis of paired comparison data. Since their introduction, numerous developments have been proposed in different areas. This paper provides an updated overview of these extensions, including how to account for object- and subject-specific covariates and how to deal with ordinal paired comparison data. Special emphasis is given to models for dependent comparisons. Although these models are more realistic, their use is complicated by numerical difficulties. We therefore concentrate on implementation issues. In particular, a pairwise likelihood approach is explored for models for dependent paired comparison data, and a simulation study is carried out to compare the performance of maximum pairwise likelihood with other limited information estimation methods. The methodology is illustrated throughout using a real data set about university paired comparisons performed by students.Comment: Published in at http://dx.doi.org/10.1214/12-STS396 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Point Spread Functions in Identification of Astronomical Objects from Poisson Noised Image

This article deals with modeling of astronomical objects, which is one of the most fundamental topics in astronomical science. Introduction part is focused on problem description and used methods. Point Spread Function Modeling part deals with description of basic models used in astronomical photometry and further on introduction of more sophisticated models such as combinations of interference, turbulence, focusing, etc. This paper also contains a~way of objective function definition based on the knowledge of Poisson distributed noise, which is included in astronomical data. The proposed methods are further applied to real astronomical data

Applying project-based learning to teach software analytics and best practices in data science

Due to recent industry needs, synergies between data science and software engineering are starting to be present in data science and engineering academic programs. Two synergies are: applying data science to manage the quality of the software (software analytics) and applying software engineering best practices in data science projects to ensure quality attributes such as maintainability and reproducibility. The lack of these synergies on academic programs have been argued to be an educational problem. Hence, it becomes necessary to explore how to teach software analytics and software engineering best practices in data science programs. In this context, we provide hands-on for conducting laboratories applying project-based learning in order to teach software analytics and software engineering best practices to data science students. We aim at improving the software engineering skills of data science students in order to produce software of higher quality by software analytics. We focus in two skills: following a process and software engineering best practices. We apply project-based learning as main teaching methodology to reach the intended outcomes. This teaching experience shows the introduction of project-based learning in a laboratory, where students applied data science and best software engineering practices to analyze and detect improvements in software quality. We carried out a case study in two academic semesters with 63 data science bachelor students. The students found the synergies of the project positive for their learning. In the project, they highlighted both utility of using a CRISP-DM data mining process and best software engineering practices like a software project structure convention applied to a data science project.This paper was partly funded by a teaching innovation project of ICE@UPC-BarcelonaTech (entitled ‘‘Audiovisual and digital material for data engineering, a teaching innovation project with open science’’), and the ‘‘Beatriz Galindo’’ Spanish Program BEA-GAL18/00064.Peer ReviewedPostprint (published version

CS/MTH 316/516: Numerical Methods for Digital Computers

Introduction to numerical methods used in the sciences. Methods of interpolation, data smoothing, functional approximation, numerical differentiation and integration. Solution techniques for linear and nonlinear equations. Discussion of sources of error in numerical methods. Applications of interest to engineering, science, and applied mathematics students are an integral part of the course. Special topics presented as schedule permits

CS/MTH 316/516: Survey of Numerical Methods for Computational Science

Introduction to numerical methods used in the sciences and engineering. Included will be methods for interpolation, data smoothing, integration, differentiation, and solution of systems of linear and nonlinear equations. Discussion of sources of error in numerical methods. Applications to science, engineering and applied mathematics are an integral part of the course. Special topics presented as schedule permits. Four hours lecture

A new kind of first year physics prac

In 2008 the first year units of the restructured SC01 applied science program were introduced. One of these new units is SCB110 Science Concepts and Global Systems, which covers a very broad range of subjects such as the history of philosophy, geology, physics, climate change etc. A practical exercise to compliment the physics component of the course was required. The purpose of this practical assignment was to introduce students to some fundamental aspects of experimental science that included topics such as hypothesis testing, statistics, experimental error, referencing, visual display of results etc. Due to logistical problems of providing a single hands-on physics experiment for approximately 250 students, an activity was devised which involved students viewing a 14-minute video of an experiment to measure the speed of light in two blocks of glass using a laser beam. The video was placed on Blackboard in Podcast and QuickTime formats and a DVD was placed in the Gardens Point and Carseldine libraries. Students were given a document (via Blackboard) which included data collected from the experiment shown in the video and instructions on how to analyse the data and write a short scientific report with a 200-word abstract, introduction, method, results, discussion and reference sections. The document also included a photograph and diagram of the experimental set-up for students to include in their reports. Students were required to work in teams of two or three although an allowance was made for students who couldn’t manage to find a partner or who were intent on writing the report on their own. A survey was included in the document on Blackboard. Of the 242 enrolled students, 191 (78.3%) performed the prac and 112 (58.6%) of these students responded to the survey. The survey results are presented in this poster