Hands-on with Azure Databricks

Soh, Julian; Singh, Priyanshi

doi:10.1007/978-1-4842-6405-8_7

Cited by 2 publications

(1 citation statement)

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“… 54 The potential of Jupyter notebooks in other areas is astounding (e.g., engineering, physics, mathematics). The impact of Jupyter notebooks as an interface for cloud computing has reached the high-tech companies: (i) Microsoft, with the Azure notebook; 55 (ii) Amazon, with the Segemaker notebook; 56 and (iii) Google, with Colaboratory (also known as Colab). 57 Among these, Colab is a cloud service based on Jupyter notebooks, which is a service linked to a Google Drive account, and free of charge.…”

Section: Introductionmentioning

confidence: 99%

Google Colab and Virtual Simulations: Practical e-Learning Tools to Support the Teaching of Thermodynamics and to Introduce Coding to Students

2022

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Various studies have reported the versatility and great scope of programming tools in all areas of knowledge. Coding is generally of paramount importance to chemistry students regardless of whether they intend to work with theoretical chemistry. Google Colab notebooks can introduce students to programming concepts and could be a convenient tool to assist in the chemistry teaching process. In this article, we implemented Google Colab notebooks to aid in the teaching of thermodynamics in a physical chemistry class. We presented six notebooks, covering introductory concepts of both coding and thermodynamics as a set of learning objects that can be useful in a virtual learning environment. In addition, in some of the notebooks, we included a step-by-step guide on how to run virtual lab simulations. The Colab notebooks were created for students without previous experience in programming. All of the Colab notebooks contain exercises of the activities and the solutions of the proposed exercises. Furthermore, all of the Colab notebooks can be modified and downloaded from the Github repository. Finally, we used the Python programming language and Colab because they are free and widely used by the academic community.

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Section: Introductionmentioning

confidence: 99%

Google Colab and Virtual Simulations: Practical e-Learning Tools to Support the Teaching of Thermodynamics and to Introduce Coding to Students

2022

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Revealing the Mysteries of Population Mobility Amid the COVID-19 Pandemic in Canada: Comparative Analysis With Internet of Things–Based Thermostat Data and Google Mobility Insights (Preprint)

Sahu,

Dubin,

Majowicz

et al. 2023

Preprint

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BACKGROUND The COVID-19 pandemic necessitated public health policies to limit human mobility and curb infection spread. Human mobility, which is often underestimated, plays a pivotal role in health outcomes, impacting both infectious and chronic diseases. Collecting precise mobility data is vital for understanding human behavior and informing public health strategies. Google’s GPS-based location tracking, which is compiled in Google Mobility Reports, became the gold standard for monitoring outdoor mobility during the pandemic. However, indoor mobility remains underexplored. OBJECTIVE This study investigates in-home mobility data from ecobee’s smart thermostats in Canada (February 2020 to February 2021) and compares it directly with Google’s residential mobility data. By assessing the suitability of smart thermostat data, we aim to shed light on indoor mobility patterns, contributing valuable insights to public health research and strategies. METHODS Motion sensor data were acquired from the ecobee “Donate Your Data” initiative via Google’s BigQuery cloud platform. Concurrently, residential mobility data were sourced from the Google Mobility Report. This study centered on 4 Canadian provinces—Ontario, Quebec, Alberta, and British Columbia—during the period from February 15, 2020, to February 14, 2021. Data processing, analysis, and visualization were conducted on the Microsoft Azure platform using Python (Python Software Foundation) and R programming languages (R Foundation for Statistical Computing). Our investigation involved assessing changes in mobility relative to the baseline in both data sets, with the strength of this relationship assessed using Pearson and Spearman correlation coefficients. We scrutinized daily, weekly, and monthly variations in mobility patterns across the data sets and performed anomaly detection for further insights. RESULTS The results revealed noteworthy week-to-week and month-to-month shifts in population mobility within the chosen provinces, aligning with pandemic-driven policy adjustments. Notably, the ecobee data exhibited a robust correlation with Google’s data set. Examination of Google’s daily patterns detected more pronounced mobility fluctuations during weekdays, a trend not mirrored in the ecobee data. Anomaly detection successfully identified substantial mobility deviations coinciding with policy modifications and cultural events. CONCLUSIONS This study’s findings illustrate the substantial influence of the Canadian stay-at-home and work-from-home policies on population mobility. This impact was discernible through both Google’s out-of-house residential mobility data and ecobee’s in-house smart thermostat data. As such, we deduce that smart thermostats represent a valid tool for facilitating intelligent monitoring of population mobility in response to policy-driven shifts.

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Hands-on with Azure Databricks

Abstract: In Chapter 10.1007/978-1-4842-6405-8_6, we explored the concepts of Spark and Azure Databricks’ implementation of the platform. In this chapter, we will be doing a hands-on exploration of these concepts in Azure Databricks.

Cited by 2 publications

References 0 publications

Google Colab and Virtual Simulations: Practical e-Learning Tools to Support the Teaching of Thermodynamics and to Introduce Coding to Students

Google Colab and Virtual Simulations: Practical e-Learning Tools to Support the Teaching of Thermodynamics and to Introduce Coding to Students

Revealing the Mysteries of Population Mobility Amid the COVID-19 Pandemic in Canada: Comparative Analysis With Internet of Things–Based Thermostat Data and Google Mobility Insights (Preprint)

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