What issues are data scientists talking about? Identification of current data science issues using semantic content analysis of Q&A communities.

Updating curricula in new computer science domains is a critical challenge faced by many instructors and programs. In this paper we present an approach for identifying emerging topics and issues in Data Science by using Question and Answer (Q&A) sites as a resource. Q&A sites provide a useful online platform for discussion of topics and through the sharing of information they become a valuable corpus of knowledge. We applied latent Dirichlet allocation (LDA), a statistical topic modeling technique, to analyze data science related threads from from two popular Q&A communities "Stack Exchange and Reddit". We uncovered both important topics as well as useful examples that can be incorporated into teaching. In addition to technical topics, our analysis also identified topics related to professional development. We believe that approaches such as these are critical in order to update curriculum and bridge the workplace-school divide in teaching of newer topics such as data science. Given the pace of technical development and frequent changes in the field, this is an inventive and effective method to keep teaching up to date. We also discuss the limitations of this approach whereby topics of importance such as data ethics are largely missing from online discussions.

Fixed underwater observatories (FUO), equipped with digital cameras and other sensors, become more commonly used to record different kinds of time series data for marine habitat monitoring. With increasing numbers of campaigns, numbers of sensors and campaign time, the volume and heterogeneity of the data, ranging from simple temperature time series to series of HD images or video call for new data science approaches to analyze the data. While some works have been published on the analysis of data from one campaign, we address the problem of analyzing time series data from two consecutive monitoring campaigns (starting late 2017 and late 2018) in the same habitat. While the data from campaigns in two separate years provide an interesting basis for marine biology research, it also presents new data science challenges, like the the marine image analysis in data form more than one campaign. In this paper, we analyze the polyp activity of two Paragorgia arborea cold water coral (CWC) colonies using FUO data collected from November 2017 to June 2018 and from December 2018 to April 2019. We successfully apply convolutional neural networks (CNN) for the segmentation and classification of the coral and the polyp activities. The result polyp activity data alone showed interesting temporal patterns with differences and similarities between the two time periods. A one month "sleeping" period in spring with almost no activity was observed in both coral colonies, but with a shift of approximately one month. A time series prediction experiment allowed us to predict the polyp activity from the non-image sensor data using recurrent neural networks (RNN). The results pave a way to a new multi-sensor monitoring strategy for Paragorgia arborea behaviour.

Computer vision is an interdisciplinary scientific field that deals with how computers obtain, store, interpret and understand digital images or videos using artificial intelligence based on neural networks, machine learning and deep learning methodologies. They are used in countless applications such as image retrieval and classification, driving and transport monitoring, medical diagnostics and aerial monitoring. Written by a team of international experts, this edited book covers the state-of-the-art of advanced research in the fields of computer vision and recognition systems from fundamental concepts to methodologies and technologies and real world applications including object detection, biometrics, Deepfake detection, sentiment and emotion analysis, traffic enforcement camera monitoring, vehicle control and aerial remote sensing imagery. The book will be useful for industry and academic researchers, scientists and engineers in the fields of computer vision, machine vision, image processing and recognition, multimedia, AI, machine and deep learning, data science, biometrics, security, and signal processing. It will also make a great course reference for advanced students and lecturers in these fields of research.

Crowd-Sourcing for Data Science and Quantifiable Challenges: Optimal Contest Design

This present research aims to determine the growing needs and challenges faced by Data Science and Cyber Security students at Al Istiqlal University's Faculty of Information Technology when learning the English language. It also tries to ascertain whether gender and level of competence affected their requirements and difficulties in mastering the English language. 35 cadets who are specializing in Data Science and Cyber Security make up the sample. The researcher gave out 39 questionnaire items divided into eight domains. The results show that cadets in Data Science and Cyber Security did not undergo any guidance regarding how to utilize English in the discipline while engaging in the analysis of data or cyber security keywords. Additionally, the study demonstrates that cadets majoring in data science and cyber security did not receive any guidance on how to learn to communicate in English, and the teaching activities in the English programs they had taken did not match their notions of the standards for expert English. Moreover, English proficiency requirements for cadets enrolling in Data Science and Cyber Security courses should be taken into consideration. Additionally, no statically meaningful differences in the demands for key competencies and barriers faced by Data Science and Cyber Security cadets are found when gender and competency traits are taken into consideration.

Machine learning and the electrocardiogram over two decades: Time series and meta-analysis of the algorithms, evaluation metrics and applications

Academic programme development primarily focuses on a product rather than a process. Thus, the dynamics among stakeholders and processes while designing and developing a programme receive little attention and are rarely studied. This study discusses the process of developing a four-year undergraduate data science programme at Sherubtse College, Royal University of Bhutan, through the analytic lens of routine dynamics. Routines are recurrent, recognisable action patterns enacted by different interdependent actors to accomplish a task. A need assessment survey was conducted to garner insights and perspectives of stakeholders on data science. Qualitative coding was employed to analyse and synthesise the survey responses to understand the needs and challenges of data science. Drawing on the survey responses, constructive recommendations from the stakeholders, and insights on data science, the programme committee successfully developed an academic programme that caters to the data science needs and demands in Bhutan. The programme was launched in the autumn academic session of 2021 with 29 students, guided by the aim of equipping students with sound theoretical knowledge and practical skills to perform complex data analytics and operationalise data technologies. This study contributes to the knowledge base of data science, namely the use of routine dynamics as an analytical instrument to study the underlying workflow processes and information exchanges in an undertaking. The methodical description of a programme development process by anchoring on an analytic framework offers valuable information for curriculum developers mandated to produce graduates with know-how in dealing with data of varying volume and variety.

The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and influence. 'Data science' and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? How does it all fit together? Now in paperback and fortified with exercises, this book delivers a concentrated course in modern statistical thinking. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov Chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. Each chapter ends with class-tested exercises, and the book concludes with speculation on the future direction of statistics and data science.

The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and influence. 'Data science' and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? How does it all fit together? Now in paperback and fortified with exercises, this book delivers a concentrated course in modern statistical thinking. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov Chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. Each chapter ends with class-tested exercises, and the book concludes with speculation on the future direction of statistics and data science.

The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and influence. 'Data science' and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? How does it all fit together? Now in paperback and fortified with exercises, this book delivers a concentrated course in modern statistical thinking. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov Chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. Each chapter ends with class-tested exercises, and the book concludes with speculation on the future direction of statistics and data science.

Chemical heuristics have been fundamental to the advancement of chemistry and materials science. These heuristics are typically established by scientists using knowledge and creativity to extract patterns from limited datasets. Machine learning offers opportunities to perfect this approach using computers and larger datasets. Here, we discuss the relationships between traditional heuristics and machine learning approaches. We show how traditional rules can be challenged by large-scale statistical assessment and how traditional concepts commonly used as features are feeding the machine learning techniques. We stress the waste involved in relearning chemical rules and the challenges in terms of data size requirements for purely data-driven approaches. Our view is that heuristic and machine learning approaches are at their best when they work together.

Given the emphasis placed on technology by the Department of Health and the recent high profile Topol review, the role of healthcare data is set to become highly prevalent in...

Big data is one of the most important phenomena of the 21st century, creating unique opportunities and challenges in its processing and interpretation. Machine learning (ML), a subset of artificial intelligence (AI), has become a foundation of data science, which enables applications ranging from computer vision, geoscience, aviation and medicine. ML becomes important when establishing mathematical models that connect explanatory variables to predicted variables is impossible due to complexity. Deep learning (DL), a subset of ML, has revolutionized fields such as speech recognition, email filtering, and time series analysis. However, DL methods face challenges such as high data demand, overfitting, and the “black box” problem.We review least-squares-based deep learning (LSBDL), a framework that combines the interpretability of linear least squares (LS) theory with the flexibility and power of deep learning (DL). LS theory, widely used in engineering and geosciences, provides powerful tools for parameter estimation, quality control, and reliability through linear models. DL, on the other hand, deals with modelling complex nonlinear relationships where the mapping between explanatory and predicted variables is unknown. LSBDL bridges these approaches by formulating DL within the LS framework: training networks to establish a design matrix, an essential element of linear models. Through this integration, LSBDL enhances DL with transparency, statistical inference, and reliability. Gradient descent methods such as steepest descent and Gauss-Newton methods are used to construct an adaptive design matrix. By combining the transparency of LS theory with the data-driven adaptability of DL, LSBDL addresses challenges in different fields including geoscience, aviation, and data science. This approach not only improves the interpretability of DL models, but also extends the applicability of LS theory to nonlinear and complex systems, offering new opportunities for innovation and research.By embedding statistical foundations in the DL workflow, LSBDL offers a three-fold advantage: i) Direct computation of covariance matrices for predicted outcomes allows for quantitative assessment of model uncertainty. ii) Well-established theories of hypothesis testing and outlier detection facilitate the identification of model misspecifications and outlying data, and iii) The covariance matrix of observations can be used to train networks with statistically correlated, inconsistent, or heterogeneous datasets. Incorporating least squares principles increases model explainability, a critical aspect of interpretable and explainable artificial intelligence, and bridges the gap between traditional statistical methods and modern DL techniques. For example, LSBDL can incorporate prior knowledge using soft and hard physics-based constraints, a technique known as physics-informed machine learning (PIML).The approach is illustrated through three illustrative examples: Surface fitting, time series forecasting, and groundwater storage downscaling. Beyond these examples, LSBDL offers opportunities for various applications including geoscience, inverse problems, aviation, data assimilation, sensor fusion, and time series analysis.

System modeling (identification) in complex systems like kinesiological and biological in general is extremely difficult due to the high dimensions of parameters and usually non-linear functional dependencies. Data Science and especially Machine Learning (Deep Learning) algorithms seem to be quite a good tool for analysis and problem-solving in sports today. Data Science (Machine or Deep Learning) algorithms rely on basic use of statistical algorithms, but extend those with models such as Decision tree, K-means clustering, Neural networks, and Reinforcement learning, creating new algorithms that handle input data by predicting outputs that describe correlation relations or predict future states at time points (regression). This study is an attempt to analyze and research applications of machine learning in Sport science - Kinanthropometry related problem of determining somatotype by using the Microsoft Azure Machine Learning platform and comparing several supervised classifier algorithms (Multiclass Neural Network, Multiclass Decision Forest, Multiclass Decision Jungle and Multiclass Logistic Regression) which were compared versus classical somatotype categorization algorithms with dataset based on the Heath-Carter method Somatotype determination to gain experience and expertise.

Historically, machine learning techniques and particularly neural networks have been applied in the domain of Geophysics to discern rock properties through a quantitative approach from seismic data and associated seismic attributes. However, the success or failure of neural network is dependent on amount of available data. In general, more labelled data during training of the neural network results in accurate predictions. Therefore, seismic based rock property prediction models and their calibration needs to be fed with labelled data coming from well logs in adequate amount. The fact that the number of wells are often limited can be a huge challenge in adopting neural networks predictions. Downton and Hampson (2019) developed a Hybrid Theory-guided Data Science (TGDS) technique to improve the training of the network by augmenting the amount of data used for training. This technique aims at overcoming the issue of scarcity of the well log data. By using the outputs of theory based components like Rock Physics theory as input to the data science component of augmenting the amount of the data. In this method, Rock Physics is applied to model the elastic response due to the changes in rock and fluid properties of the available well control to create a huge dataset of pseudo wells. Then, synthetic seismic gathers are modeled at the pseudo well locations to train a Deep Feed-Forwards Neural Network (DFNN). The parameters estimated during DFNN training with synthetic seismic gathers are then applied on a real seismic dataset. The final predicted P-impedance from this formerly described method was compared to conventional inverted Pre-Stack Elastic Inversion. This paper shows the application of the method of TGDS based synthetic seismic catalog generation followed by DFNN applied on real seismic dataset for a commercially oil producing field in North Sea. The reservoirs are injectite sands which are difficult to image but can be key pay zones with quite high porosity and permeability. Well log curves e.g. P-Impedance, Porosity and Water Saturation serve as guidelines to create the synthetic seismic data. Additionally, with the added advantage of increased synthetic data, pseudo wells were broken into training and validation datasets. So, the DFNN training is carried out on a portion of the training data subset. During the process of training the DFNN, the seismic data remains blind. The estimated DFNN non-linear operator is applied to the seismic data, however, it needs to be scaled to the real data as the training was performed on the synthetic seismic data. The final result of the data science and machine learning approach driven derived P-impedance showed higher frequency and better lateral continuity than the conventional theory based approach of Pre-Stack Elastic Inversion. Augmenting the amount of training data by generating synthetic data based on the statistics of from nearby well control and rock physics relationships is prime differentiator of this workflow. By using rock physics theory a large number of pseudo wells can be generated that gives a range of expected geologic scenarios. Key wells from nearby fields, that do not necessarily tie the seismic under consideration, can be incorporated into the analysis and also, if required additional wells could be incorporated into the analysis as drilled.