First Annual West Antarctic Ice Sheet (WAIS) Science Workshop PDF Download
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Author: Robert A. Bindschadler
Publisher:
ISBN:
Category : Ice sheets
Languages : en
Pages : 76
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Book Description
History, current behavior, internal dynamics, and environmental interactions concerning future behavior and potential for rapid collapse of the West Antarctic Ice Sheet (WAIS).
Author: Jennifer K. Clark
Publisher: Plain Sight Publishing
ISBN: 9781462121670
Category : Crafts & Hobbies
Languages : en
Pages : 0
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Book Description
An imaginative science activity book for children.
Author: Wendy Saul
Publisher: Heinemann Educational Books
ISBN: 9780325005102
Category : Education
Languages : en
Pages : 0
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Book Description
This second edition, chock-full of new information and ideas, leaves teachers even more eager to implement an inquiry-based science curriculum.
Author: Heidi Olinger
Publisher: Rockport Publishers
ISBN: 1631595245
Category : Juvenile Nonfiction
Languages : en
Pages : 147
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Book Description
Leonardo’s Science Workshop leads children on an interactive adventure through key science concepts by following the multidisciplinary approach of the Renaissance period polymath Leonardo da Vinci: experimenting, creating projects, and exploring how art intersects with science and nature. Photos of Leonardo’s own notebooks, paintings, and drawings provide visual inspiration. More than 500 years ago, Leonardo knew that the fields of science, technology, engineering, art, and mathematics (STEAM) are all connected. The insatiably curious Leonardo examined not just the outer appearance of his art subjects, but the science that explained them. He began his studies as a painter, but his curiosity, diligence, and genius made him also a master sculptor, architect, designer, scientist, engineer, and inventor. The Leonardo’s Workshop series shares this spirit of multidisciplinary inquiry with children through accessible, engaging explanations and hands-on learning. This fascinating book harnesses children’s innate curiosity to explore some of Leonardo’s favorite subjects, including flight, motion, technology design, perspective, and astronomy. After each topic is explained with concepts from physics, chemistry, math, and engineering, kids can experience the principles first-hand with step-by-step STEAM projects. They will explore: The physics of flight by observing birds and experimenting with paper airplane designs The science of motion by building a windup dragonfly Gravitational acceleration with water balloons The movement of electrons by making cereal “dance” Technology design by making paper and fabric using recycled material Scientific perspective by drawing a 3D illusion Insight from other great thinkers—such as Galileo Galilei, James Clerk Maxwell, and Sir Isaac Newton—are woven into the lessons throughout. Introduce vital STEAM skills through visually rich, hands-on learning with Leonardo’s Science Workshop.
Author: Anthony So
Publisher: Packt Publishing Ltd
ISBN: 1838983082
Category : Computers
Languages : en
Pages : 817
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Book Description
Cut through the noise and get real results with a step-by-step approach to data science Key Features Ideal for the data science beginner who is getting started for the first time A data science tutorial with step-by-step exercises and activities that help build key skills Structured to let you progress at your own pace, on your own terms Use your physical print copy to redeem free access to the online interactive edition Book DescriptionYou already know you want to learn data science, and a smarter way to learn data science is to learn by doing. The Data Science Workshop focuses on building up your practical skills so that you can understand how to develop simple machine learning models in Python or even build an advanced model for detecting potential bank frauds with effective modern data science. You'll learn from real examples that lead to real results. Throughout The Data Science Workshop, you'll take an engaging step-by-step approach to understanding data science. You won't have to sit through any unnecessary theory. If you're short on time you can jump into a single exercise each day or spend an entire weekend training a model using sci-kit learn. It's your choice. Learning on your terms, you'll build up and reinforce key skills in a way that feels rewarding. Every physical print copy of The Data Science Workshop unlocks access to the interactive edition. With videos detailing all exercises and activities, you'll always have a guided solution. You can also benchmark yourself against assessments, track progress, and receive content updates. You'll even earn a secure credential that you can share and verify online upon completion. It's a premium learning experience that's included with your printed copy. To redeem, follow the instructions located at the start of your data science book. Fast-paced and direct, The Data Science Workshop is the ideal companion for data science beginners. You'll learn about machine learning algorithms like a data scientist, learning along the way. This process means that you'll find that your new skills stick, embedded as best practice. A solid foundation for the years ahead.What you will learn Find out the key differences between supervised and unsupervised learning Manipulate and analyze data using scikit-learn and pandas libraries Learn about different algorithms such as regression, classification, and clustering Discover advanced techniques to improve model ensembling and accuracy Speed up the process of creating new features with automated feature tool Simplify machine learning using open source Python packages Who this book is forOur goal at Packt is to help you be successful, in whatever it is you choose to do. The Data Science Workshop is an ideal data science tutorial for the data science beginner who is just getting started. Pick up a Workshop today and let Packt help you develop skills that stick with you for life.
Author: Robert P Crease
Publisher: National Geographic Books
ISBN: 0393292436
Category : Science
Languages : en
Pages : 0
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Book Description
A fascinating look at key thinkers throughout history who have shaped public perception of science and the role of authority. When does a scientific discovery become accepted fact? Why have scientific facts become easy to deny? And what can we do about it? In The Workshop and the World, philosopher and science historian Robert P. Crease answers these questions by describing the origins of our scientific infrastructure—the “workshop”—and the role of ten of the world’s greatest thinkers in shaping it. At a time when the Catholic Church assumed total authority, Francis Bacon, Galileo Galilei, and René Descartes were the first to articulate the worldly authority of science, while writers such as Mary Shelley and Auguste Comte told cautionary tales of divorcing science from the humanities. The provocative leaders and thinkers Kemal Atatürk and Hannah Arendt addressed the relationship between the scientific community and the public in in times of deep distrust. As today’s politicians and government officials increasingly accuse scientists of dishonesty, conspiracy, and even hoaxes, engaged citizens can’t help but wonder how we got to this level of distrust and how we can emerge from it. This book tells dramatic stories of individuals who confronted fierce opposition—and sometimes risked their lives—in describing the proper authority of science, and it examines how ignorance and misuse of science constitute the preeminent threat to human life and culture. An essential, timely exploration of what it means to practice science for the common good as well as the danger of political action divorced from science, The Workshop and the World helps us understand both the origins of our current moment of great anti-science rhetoric and what we can do to help keep the modern world from falling apart.
Author: Robert A. Bindschadler
Publisher:
ISBN:
Category : Ice sheets
Languages : en
Pages : 76
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Book Description
History, current behavior, internal dynamics, and environmental interactions concerning future behavior and potential for rapid collapse of the West Antarctic Ice Sheet (WAIS).
Author: Vivian Siahaan
Publisher: BALIGE PUBLISHING
ISBN:
Category : Computers
Languages : en
Pages : 398
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Book Description
In this "Heart Failure Analysis and Prediction" data science workshop, we embarked on a comprehensive journey through the intricacies of cardiovascular health assessment using machine learning and deep learning techniques. Our journey began with an in-depth exploration of the dataset, where we meticulously studied its characteristics, dimensions, and underlying patterns. This initial step laid the foundation for our subsequent analyses. We delved into a detailed examination of the distribution of categorized features, meticulously dissecting variables such as age, sex, serum sodium levels, diabetes status, high blood pressure, smoking habits, and anemia. This critical insight enabled us to comprehend how these features relate to each other and potentially impact the occurrence of heart failure, providing valuable insights for subsequent modeling. Subsequently, we engaged in the heart of the project: predicting heart failure. Employing machine learning models, we harnessed the power of grid search to optimize model parameters, meticulously fine-tuning algorithms to achieve the best predictive performance. Through an array of models including Logistic Regression, KNeighbors Classifier, DecisionTrees Classifier, Random Forest Classifier, Gradient Boosting Classifier, XGB Classifier, LGBM Classifier, and MLP Classifier, we harnessed metrics like accuracy, precision, recall, and F1-score to meticulously evaluate each model's efficacy. Venturing further into the realm of deep learning, we embarked on an exploration of neural networks, striving to capture intricate patterns in the data. Our arsenal included diverse architectures such as Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM) networks, Self Organizing Maps (SOMs), Recurrent Neural Networks (RNN), Deep Belief Networks (DBN), and Autoencoders. These architectures enabled us to unravel complex relationships within the data, yielding nuanced insights into the dynamics of heart failure prediction. Our approach to evaluating model performance was rigorous and thorough. By scrutinizing metrics such as accuracy, recall, precision, and F1-score, we gained a comprehensive understanding of the models' strengths and limitations. These metrics enabled us to make informed decisions about model selection and refinement, ensuring that our predictions were as accurate and reliable as possible. The evaluation phase emerges as a pivotal aspect, accentuated by an array of comprehensive metrics. Performance assessment encompasses metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. Cross-validation and learning curves are strategically employed to mitigate overfitting and ensure model generalization. Furthermore, visual aids such as ROC curves and confusion matrices provide a lucid depiction of the models' interplay between sensitivity and specificity. Complementing our advanced analytical endeavors, we also embarked on the creation of a Python GUI using PyQt. This intuitive graphical interface provided an accessible platform for users to interact with the developed models and gain meaningful insights into heart health. The GUI streamlined the prediction process, making it user-friendly and facilitating the application of our intricate models to real-world scenarios. In conclusion, the "Heart Failure Analysis and Prediction" data science workshop was a journey through the realms of data exploration, feature distribution analysis, and the application of cutting-edge machine learning and deep learning techniques. By meticulously evaluating model performance, harnessing the capabilities of neural networks, and culminating in the creation of a user-friendly Python GUI, we armed participants with a comprehensive toolkit to analyze and predict heart failure with precision and innovation.
Author: Vivian Siahaan
Publisher: BALIGE PUBLISHING
ISBN:
Category : Computers
Languages : en
Pages : 353
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Book Description
In this project, Data Science Workshop focused on Liver Disease Classification and Prediction, we embarked on a comprehensive journey through various stages of data analysis, model development, and performance evaluation. The workshop aimed to utilize Python and its associated libraries to create a Graphical User Interface (GUI) that facilitates the classification and prediction of liver disease cases. Our exploration began with a thorough examination of the dataset. This entailed importing necessary libraries such as NumPy, Pandas, and Matplotlib for data manipulation, visualization, and preprocessing. The dataset, representing liver-related attributes, was read and its dimensions were checked to ensure data integrity. To gain a preliminary understanding, the dataset's initial rows and column information were displayed. We identified key features such as 'Age', 'Gender', and various biochemical attributes relevant to liver health. The dataset's structure, including data types and non-null counts, was inspected to identify any potential data quality issues. We detected that the 'Albumin_and_Globulin_Ratio' feature had a few missing values, which were subsequently filled with the median value. Our exploration extended to visualizing categorical distributions. Pie charts provided insights into the proportions of healthy and unhealthy liver cases among different gender categories. Stacked bar plots further delved into the connections between 'Total_Bilirubin' categories and the prevalence of liver disease, fostering a deeper understanding of these relationships. Transitioning to predictive modeling, we embarked on constructing machine learning models. Our arsenal included a range of algorithms such as Logistic Regression, Support Vector Machines, K-Nearest Neighbors, Decision Trees, Random Forests, Gradient Boosting, Extreme Gradient Boosting, Light Gradient Boosting. The data was split into training and testing sets, and each model underwent rigorous evaluation using metrics like accuracy, precision, recall, F1-score, and ROC-AUC. Hyperparameter tuning played a pivotal role in model enhancement. We leveraged grid search and cross-validation techniques to identify the best combination of hyperparameters, optimizing model performance. Our focus shifted towards assessing the significance of each feature, using techniques such as feature importance from tree-based models. The workshop didn't halt at machine learning; it delved into deep learning as well. We implemented an Artificial Neural Network (ANN) using the Keras library. This powerful model demonstrated its ability to capture complex relationships within the data. With distinct layers, activation functions, and dropout layers to prevent overfitting, the ANN achieved impressive results in liver disease prediction. Our journey culminated with a comprehensive analysis of model performance. The metrics chosen for evaluation included accuracy, precision, recall, F1-score, and confusion matrix visualizations. These metrics provided a comprehensive view of the model's capability to correctly classify both healthy and unhealthy liver cases. In summary, the Data Science Workshop on Liver Disease Classification and Prediction was a holistic exploration into data preprocessing, feature categorization, machine learning, and deep learning techniques. The culmination of these efforts resulted in the creation of a Python GUI that empowers users to input patient attributes and receive predictions regarding liver health. Through this workshop, participants gained a well-rounded understanding of data science techniques and their application in the field of healthcare.
Author: W. Neil Johnson
Publisher:
ISBN:
Category : Astrophysics
Languages : en
Pages : 764
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Book Description
Author: Vivian Siahaan
Publisher: BALIGE PUBLISHING
ISBN:
Category : Computers
Languages : en
Pages : 361
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Book Description
In the captivating journey of our data science workshop, we embarked on the exploration of Chronic Kidney Disease classification and prediction. Our quest began with a thorough dive into data exploration, where we meticulously delved into the dataset's intricacies to unearth hidden patterns and insights. We analyzed the distribution of categorized features, unraveling the nuances that underlie chronic kidney disease. Guided by the principles of machine learning, we embarked on the quest to build predictive models. With the aid of grid search, we fine-tuned our machine learning algorithms, optimizing their hyperparameters for peak performance. Each model, whether K-Nearest Neighbors, Decision Trees, Random Forests, Gradient Boosting, Naive Bayes, Extreme Gradient Boosting, Light Gradient Boosting, or Multi-Layer Perceptron, was meticulously trained and tested, paving the way for robust predictions. The voyage into the realm of deep learning took us further, as we harnessed the power of Artificial Neural Networks (ANNs). By constructing intricate architectures, we designed ANNs to discern intricate patterns from the data. Leveraging the prowess of TensorFlow, we artfully crafted layers, each contributing to the ANN's comprehension of the underlying dynamics. This marked our initial foray into the world of deep learning. Our expedition, however, did not conclude with ANNs. We ventured deeper into the abyss of deep learning, uncovering the potential of Long Short-Term Memory (LSTM) networks. These networks, attuned to sequential data, unraveled temporal dependencies within the dataset, fortifying our predictive capabilities. Diving even further, we encountered Self-Organizing Maps (SOMs) and Restricted Boltzmann Machines (RBMs). These innovative models, rooted in unsupervised learning, unmasked underlying structures in the dataset. As our understanding of the data deepened, so did our repertoire of tools for prediction. Autoencoders, our final frontier in deep learning, emerged as our champions in dimensionality reduction and feature learning. These unsupervised neural networks transformed complex data into compact, meaningful representations, guiding our predictive models with newfound efficiency. To furnish a granular understanding of model behavior, we employed the classification report, which delineated precision, recall, and F1-Score for each class, providing a comprehensive snapshot of the model's predictive capacity across diverse categories. The confusion matrix emerged as a tangible visualization, detailing the interplay between true positives, true negatives, false positives, and false negatives. We also harnessed ROC and precision-recall curves to illuminate the dynamic interplay between true positive rate and false positive rate, vital when tackling imbalanced datasets. For regression tasks, MSE and its counterpart RMSE quantified the average squared differences between predictions and actual values, facilitating an insightful assessment of model fit. Further enhancing our toolkit, the R-squared (R2) score unveiled the extent to which the model explained variance in the dependent variable, offering a valuable gauge of overall performance. Collectively, this ensemble of metrics enabled us to make astute model decisions, optimize hyperparameters, and gauge the models' fitness for accurate disease prognosis in a clinical context. Amidst this whirlwind of data exploration and model construction, our GUI using PyQt emerged as a beacon of user-friendly interaction. Through its intuitive interface, users navigated seamlessly between model selection, training, and prediction. Our GUI encapsulated the intricacies of our journey, bridging the gap between data science and user experience. In the end, our odyssey illuminated the intricate landscape of Chronic Kidney Disease classification and prediction. We harnessed the power of both machine learning and deep learning, uncovering hidden insights and propelling our predictive capabilities to new heights. Our journey transcended the realms of data, algorithms, and interfaces, leaving an indelible mark on the crossroads of science and innovation.
