Introductory data science across disciplines, using Python, case studies, and industry consulting projects

Lasser, Jana; Manik, Debsankha; Silbersdorff, Alexander; Säfken, Benjamin; Kneib, Thomas

doi:10.1111/test.12243

Cited by 15 publications

(9 citation statements)

References 12 publications

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“…Computational notebooks have become popular programming environments that facilitate collaboration, interactive development and reproducibility and are, in particular, valued for rapid prototyping, data exploration and training [18]. There are dozens of different notebook systems available [19], but the use of Jupyter certainly excels across many disciplines [20][21][22], including bio-and health-informatics [12,23,24], general data science [13,25,26] and, more recently, Earth Observation research [27,28]. In 2021, more than 10 million Jupyter notebooks were available on the code-sharing platform GitHub [17,29].…”

Section: Introductionmentioning

confidence: 99%

Five Guiding Principles to Make Jupyter Notebooks Fit for Earth Observation Data Education

Wagemann

Fierli

Mantovani³

et al. 2022

Remote Sensing

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There is a growing demand to train Earth Observation (EO) data users in how to access and use existing and upcoming data. A promising tool for data-related training is computational notebooks, which are interactive web applications that combine text, code and computational output. Here, we present the Learning Tool for Python (LTPy), which is a training course (based on Jupyter notebooks) on atmospheric composition data. LTPy consists of more than 70 notebooks and has taught over 1000 EO data users so far, whose feedback is overall positive. We adapted five guiding principles from different fields (mainly scientific computing and Jupyter notebook research) to make the Jupyter notebooks more educational and reusable. The Jupyter notebooks developed (i) follow the literate programming paradigm by a text/code ratio of 3, (ii) use instructional design elements to improve navigation and user experience, (iii) modularize functions to follow best practices for scientific computing, (iv) leverage the wider Jupyter ecosystem to make content accessible and (v) aim for being reproducible. We see two areas for future developments: first, to collect feedback and evaluate whether the instructional design elements proposed meet their objective; and second, to develop tools that automatize the implementation of best practices.

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Section: Introductionmentioning

confidence: 99%

Five Guiding Principles to Make Jupyter Notebooks Fit for Earth Observation Data Education

Wagemann

Fierli

Mantovani³

et al. 2022

Remote Sensing

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“…Since our focus is on providing students with skills that are useful when they have to practice the profession, we decided to use the Python language as a complement to data analysis (Lasser et al, 2021). Thus, there was a need to export data from Excel to CSV.…”

Section: Methodsmentioning

confidence: 99%

Data and Technology: One of the Challenges for Journalism

Silvestre¹,

Frazão²

2022

JHSR

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In the constantly changing society we live in, the way journalism is done has undergone major changes. However, it must be noted that the pillars of journalism continue to be based on ethical values, the search for truth and objectivity. One of the factors contributing to change in this profession is the widespread use of new technologies. In this work, in order to adjust teaching to the new emerging needs of future professionals, we will focus on the information contained in the data and how to analyze it, since the data have been increasingly used as a source of news. The proposed challenge was to analyze how three leading Portuguese newspapers covered the news of the pandemic caused by COVID-19. In this sense, it was necessary to prepare the database for analysis and plan what information to extract from the data. In addition to Excel, used to organize the database, we used the Python programming language, which allowed for a more detailed analysis of the news coverage of the pandemic.

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“…The programming language is syntactically simple, sim ple to learn, and straightforward. Additionally, Python is open source, free to use and has rich ecosystem of libraries for scientific computing (Lasser et al 2021;Robinson, 2017;Koulouri et al 2014;Ateeq et al 2014;Jayal et al 2011;Perez et al 2010;Lutz, 2001). The data preprocessing steps taken can be summarized as follows:…”

Section: Data Pre-processing Stage Imentioning

confidence: 99%

Statistical learning approaches to sentiment analysis in the Nigerian banking context

Ogunleye¹

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Banking is an essential component of our day-to-day activity, and the sector contributes to the development of every Nation's economy. To ensure bank stability, the banks have gone through several deregulation and reformation processes. Unfortunately, these processes and technological advancement have led to increased competitiveness, saturated market, and low profitability. Thus, the need for banks to build customer centric service to gain profitability and stability is vital. This opens the need to investigate customers attitude towards banking.With the growing usage of social network sites (SNS), the user generated content (UGC) has opened opportunities for banks to mine customers opinion . This can help the banks to generate insight into their product and services, create marketing strategies and manage their reputation. To the customers, this serves as source to information that can help in supporting decision making on product or service purchase. In this study, sentiment analysis (SA) techniques were employed to investigate customers attitude towards banking us ing Twitter data. However, the unstructured nature of the data and word ambiguity made sentiment analysis complicated. In the context of this study, SA is more difficult because it involves the natural language processing of Pidgin English and English words in the bank domain.Unfortunately, there are limited or no resources for this purpose.

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Introductory data science across disciplines, using Python, case studies, and industry consulting projects

Cited by 15 publications

References 12 publications

Five Guiding Principles to Make Jupyter Notebooks Fit for Earth Observation Data Education

Five Guiding Principles to Make Jupyter Notebooks Fit for Earth Observation Data Education

Data and Technology: One of the Challenges for Journalism

Statistical learning approaches to sentiment analysis in the Nigerian banking context

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