The Wizard's Workshop

The Wizard's Workshop

Author: Jennifer K. Clark

Publisher: Plain Sight Publishing

Published: 2018

Total Pages: 0

ISBN-13: 9781462121670

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An imaginative science activity book for children.


Leonardo's Science Workshop

Leonardo's Science Workshop

Author: Heidi Olinger

Publisher: Rockport Publishers

Published: 2019-01-01

Total Pages: 147

ISBN-13: 1631595245

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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.


Science Workshop

Science Workshop

Author: Wendy Saul

Publisher: Heinemann Educational Books

Published: 2002

Total Pages: 0

ISBN-13: 9780325005102

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This second edition, chock-full of new information and ideas, leaves teachers even more eager to implement an inquiry-based science curriculum.


Leonardo's Art Workshop

Leonardo's Art Workshop

Author: Amy Leidtke

Publisher: Rockport Publishers

Published: 2018-11-20

Total Pages: 147

ISBN-13: 1631595229

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Leonardo’s Art Workshop leads children on an interactive adventure through key art 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. Following Leonardo’s example, this fascinating book harnesses children’s innate curiosity to explore the foundational elements of art—color, shadow and light, lines and patterns, forms and structures, and optics and special effects—and the science behind them. After each concept is explained using science, history, and real-world examples, kids can experience the principles first-hand with step-by-step STEAM projects, including: Create paints and dyes from food Harness a rainbow with a prism Build a camera obscura Make your own sundial Practice blind contour drawing Create a one-point perspective drawing Make an infinity scope Insight from other great artists and scientists—such as Sir Isaac Newton, Sandro Botticelli, Paul Klee, and Leonardo Pisano Fibonacci—are woven into the lessons throughout. Introduce vital STEAM skills through visually rich, hands-on learning with Leonardo’s Art Workshop.


The New Art and Science of Teaching

The New Art and Science of Teaching

Author: Robert J. Marzano

Publisher:

Published: 2018-02-14

Total Pages: 146

ISBN-13: 9781947604032

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This title is a greatly expanded volume of the original Art and Science of Teaching, offering a competency-based education framework for substantive change based on Dr. Robert Marzano's 50 years of education research. While the previous model focused on teacher outcomes, the new version places focus on student learning outcomes, with research-based instructional strategies teachers can use to help students grasp the information and skills transferred through their instruction. Throughout the book, Marzano details the elements of three overarching categories of teaching, which define what must happen to optimize student learning: students must receive feedback, get meaningful content instruction, and have their basic psychological needs met. Gain research-based instructional strategies and teaching methods that drive student success: Explore instructional strategies that correspond to each of the 43 elements of The New Art and Science of Teaching, which have been carefully designed to maximize student engagement and achievement. Use ten design questions and a general framework to help determine which classroom strategies you should use to foster student learning. Analyze the behavioral evidence that proves the strategies of an element are helping learners reach their peak academic success. Study the state of the modern standards movement and what changes must be made in K-12 education to ensure high levels of learning for all. Download free reproducible scales specific to the elements in The New Art and Science of Teaching. Contents: Chapter 1: Providing and Communicating Clear Learning Goals Chapter 2: Conducting Assessment Chapter 3: Conducting Direct Instruction Lessons Chapter 4: Practicing and Deepening Lessons Chapter 5: Implementing Knowledge Application Lessons Chapter 6: Using Strategies That Appear in All Types of Lessons Chapter 7: Using Engagement Strategies Chapter 8: Implementing Rules and Procedures Chapter 9: Building Relationships Chapter 10: Communicating High Expectations Chapter 11: Making System Changes


That Workshop Book

That Workshop Book

Author: Samantha Bennett

Publisher: Heinemann Educational Books

Published: 2007

Total Pages: 0

ISBN-13: 9780325011929

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Shows a new generation of teachers how the systems, structures, routines, and rituals that support successful workshops combine with thinking, planning, and conferring to drive students' growth, inform assessment and instruction, and increase teachers' professional satisfaction. And it shows those already using the workshop how to increase its instructional power by seeing its big ideas and its component parts in fresh, dynamic ways.


DATA SCIENCE WORKSHOP: Heart Failure Analysis and Prediction Using Scikit-Learn, Keras, and TensorFlow with Python GUI

DATA SCIENCE WORKSHOP: Heart Failure Analysis and Prediction Using Scikit-Learn, Keras, and TensorFlow with Python GUI

Author: Vivian Siahaan

Publisher: BALIGE PUBLISHING

Published: 2023-08-18

Total Pages: 398

ISBN-13:

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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.


DATA SCIENCE WORKSHOP: Liver Disease Classification and Prediction Using Machine Learning and Deep Learning with Python GUI

DATA SCIENCE WORKSHOP: Liver Disease Classification and Prediction Using Machine Learning and Deep Learning with Python GUI

Author: Vivian Siahaan

Publisher: BALIGE PUBLISHING

Published: 2023-08-09

Total Pages: 353

ISBN-13:

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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.