Data Driven Engineering 2014Roger BargaThis document discusses the unrealized power of data and predictive analytics. It begins by highlighting how predictive analytics can be used for forecasting, targeting customers, fraud detection, risk assessment, customer churn prediction, and price elasticity analysis. It then provides examples of predictive analytics in action in various industries like healthcare, education, law enforcement, and human resources. The document emphasizes that predictive analytics must become simpler to use and be integrated into business processes. It outlines the data science process and importance of data wrangling. Finally, it discusses Microsoft's CloudML Studio and Data Lab products for building predictive models using machine learning algorithms and analyzing customer data to predict things like equipment failures and customer churn.
Killer Angelskelseyvega1) The document summarizes events leading up to the Battle of Gettysburg from multiple perspectives, including Confederate and Union officers and a British observer.
2) On July 1st, Confederate and Union forces accidentally collide as they converge on Gettysburg, starting the three-day battle.
3) On July 2nd, General Lee decides to launch attacks against the Union forces, now entrenched on Cemetery Ridge, but disputes Longstreet's suggestion to maneuver around the Union army.
Barga Data Science lecture 2Roger BargaThe document discusses machine learning and data science concepts. It begins with an introduction to machine learning and the machine learning process. It then provides an overview of select machine learning algorithms and concepts like bias/variance, generalization, underfitting and overfitting. It also discusses ensemble methods. The document then shifts to discussing time series, functions for manipulating time series, and laying the foundation for time series prediction and forecasting. It provides examples of applying techniques like median filtering to smooth time series data. Overall, the document provides a high-level introduction and overview of key machine learning and time series concepts.
Barga Data Science lecture 7Roger BargaThis document appears to be lecture slides for a course on deriving knowledge from data at scale. It covers many topics related to building machine learning models including data preparation, feature selection, classification algorithms like decision trees and support vector machines, and model evaluation. It provides examples applying these techniques to a Titanic passenger dataset to predict survival. It emphasizes the importance of data wrangling and discusses various feature selection methods.
Barga Data Science lecture 5Roger BargaThe document discusses clustering and nearest neighbor algorithms for deriving knowledge from data at scale. It provides an overview of clustering techniques like k-means clustering and discusses how they are used for applications such as recommendation systems. It also discusses challenges like class imbalance that can arise when applying these techniques to large, real-world datasets and evaluates different methods for addressing class imbalance. Additionally, it discusses performance metrics like precision, recall, and lift that can be used to evaluate models on large datasets.
Retail 2010 novMartin KoosThe retail market in Sweden is experiencing steady growth and demand for new concepts. It has a population of 9.4 million with high purchasing power. Retail growth in Sweden is among the highest in Europe, fueled by GDP growth and steady population increases. The market offers opportunities for international brands and franchisors due to demanding consumers who are early adopters of trends.
Barga Data Science lecture 10Roger BargaThis document discusses various techniques for machine learning when labeled training data is limited, including semi-supervised learning approaches that make use of unlabeled data. It describes assumptions like the clustering assumption, low density assumption, and manifold assumption that allow algorithms to learn from unlabeled data. Specific techniques covered include clustering algorithms, mixture models, self-training, and semi-supervised support vector machines.
Barga Data Science lecture 6Roger BargaThe document discusses feature extraction and selection as important steps in machine learning. It notes that better features often lead to better algorithms. It then describes five clusters identified through clustering analysis. Each cluster contains individuals (male or female) with certain average demographic characteristics like age, location, income, and whether they have accounts or loans. The document emphasizes that feature extraction and selection are underrated but important for machine learning.
Barga Galvanize Sept 2015Roger BargaRoger S. Barga discusses his experience in data science and predictive analytics projects across multiple industries. He provides examples of predictive models built for customer segmentation, predictive maintenance, customer targeting, and network intrusion prevention. Barga also outlines a sample predictive analytics project for a real estate client to predict whether they can charge above or below market rates. The presentation emphasizes best practices for building predictive models such as starting small, leveraging third-party tools, and focusing on proxy metrics that drive business outcomes.
Barga ACM DEBS 2013 KeynoteRoger BargaThis document discusses streaming data processing and the adoption of scalable frameworks and platforms for handling streaming or near real-time analysis and processing over the next few years. These platforms will be driven by the needs of large-scale location-aware mobile, social and sensor applications, similar to how Hadoop emerged from large-scale web applications. The document also references forecasts of over 50 billion intelligent devices by 2015 and 275 exabytes of data per day being sent across the internet by 2020, indicating challenges around data of extreme size and the need for rapid processing.
Data Driven Engineering 2014Roger BargaThis document discusses the unrealized power of data and predictive analytics. It begins by highlighting how predictive analytics can be used for forecasting, targeting customers, fraud detection, risk assessment, customer churn prediction, and price elasticity analysis. It then provides examples of predictive analytics in action in various industries like healthcare, education, law enforcement, and human resources. The document emphasizes that predictive analytics must become simpler to use and be integrated into business processes. It outlines the data science process and importance of data wrangling. Finally, it discusses Microsoft's CloudML Studio and Data Lab products for building predictive models using machine learning algorithms and analyzing customer data to predict things like equipment failures and customer churn.
Killer Angelskelseyvega1) The document summarizes events leading up to the Battle of Gettysburg from multiple perspectives, including Confederate and Union officers and a British observer.
2) On July 1st, Confederate and Union forces accidentally collide as they converge on Gettysburg, starting the three-day battle.
3) On July 2nd, General Lee decides to launch attacks against the Union forces, now entrenched on Cemetery Ridge, but disputes Longstreet's suggestion to maneuver around the Union army.
Barga Data Science lecture 2Roger BargaThe document discusses machine learning and data science concepts. It begins with an introduction to machine learning and the machine learning process. It then provides an overview of select machine learning algorithms and concepts like bias/variance, generalization, underfitting and overfitting. It also discusses ensemble methods. The document then shifts to discussing time series, functions for manipulating time series, and laying the foundation for time series prediction and forecasting. It provides examples of applying techniques like median filtering to smooth time series data. Overall, the document provides a high-level introduction and overview of key machine learning and time series concepts.
Barga Data Science lecture 7Roger BargaThis document appears to be lecture slides for a course on deriving knowledge from data at scale. It covers many topics related to building machine learning models including data preparation, feature selection, classification algorithms like decision trees and support vector machines, and model evaluation. It provides examples applying these techniques to a Titanic passenger dataset to predict survival. It emphasizes the importance of data wrangling and discusses various feature selection methods.
Barga Data Science lecture 5Roger BargaThe document discusses clustering and nearest neighbor algorithms for deriving knowledge from data at scale. It provides an overview of clustering techniques like k-means clustering and discusses how they are used for applications such as recommendation systems. It also discusses challenges like class imbalance that can arise when applying these techniques to large, real-world datasets and evaluates different methods for addressing class imbalance. Additionally, it discusses performance metrics like precision, recall, and lift that can be used to evaluate models on large datasets.
Retail 2010 novMartin KoosThe retail market in Sweden is experiencing steady growth and demand for new concepts. It has a population of 9.4 million with high purchasing power. Retail growth in Sweden is among the highest in Europe, fueled by GDP growth and steady population increases. The market offers opportunities for international brands and franchisors due to demanding consumers who are early adopters of trends.
Barga Data Science lecture 10Roger BargaThis document discusses various techniques for machine learning when labeled training data is limited, including semi-supervised learning approaches that make use of unlabeled data. It describes assumptions like the clustering assumption, low density assumption, and manifold assumption that allow algorithms to learn from unlabeled data. Specific techniques covered include clustering algorithms, mixture models, self-training, and semi-supervised support vector machines.
Barga Data Science lecture 6Roger BargaThe document discusses feature extraction and selection as important steps in machine learning. It notes that better features often lead to better algorithms. It then describes five clusters identified through clustering analysis. Each cluster contains individuals (male or female) with certain average demographic characteristics like age, location, income, and whether they have accounts or loans. The document emphasizes that feature extraction and selection are underrated but important for machine learning.
Barga Galvanize Sept 2015Roger BargaRoger S. Barga discusses his experience in data science and predictive analytics projects across multiple industries. He provides examples of predictive models built for customer segmentation, predictive maintenance, customer targeting, and network intrusion prevention. Barga also outlines a sample predictive analytics project for a real estate client to predict whether they can charge above or below market rates. The presentation emphasizes best practices for building predictive models such as starting small, leveraging third-party tools, and focusing on proxy metrics that drive business outcomes.
Barga ACM DEBS 2013 KeynoteRoger BargaThis document discusses streaming data processing and the adoption of scalable frameworks and platforms for handling streaming or near real-time analysis and processing over the next few years. These platforms will be driven by the needs of large-scale location-aware mobile, social and sensor applications, similar to how Hadoop emerged from large-scale web applications. The document also references forecasts of over 50 billion intelligent devices by 2015 and 275 exabytes of data per day being sent across the internet by 2020, indicating challenges around data of extreme size and the need for rapid processing.
Barga Data Science lecture 4Roger BargaThis document outlines an agenda for a data science boot camp covering various machine learning topics over several hours. The agenda includes discussions of decision trees, ensembles, random forests, data modelling, and clustering. It also provides examples of data leakage problems and discusses the importance of evaluating model performance. Homework assignments involve building models with Weka and identifying the minimum attributes needed to distinguish between red and white wines.
CURSO ENARM PREGUNTAS...............CONSULTORIO MÉDICO PRIVADOEste documento contiene preguntas y respuestas sobre diversos temas de microbiología médica. Se abordan preguntas sobre el tratamiento de enfermedades infecciosas como la meningitis, difteria, brucelosis y hongos; agentes causales de infecciones como neumonía, endocarditis y sepsis; e información sobre el sistema inmune como inmunoglobulinas, interleucinas y complemento.
Disaster Managment.2070The document discusses various types of natural disasters including floods and earthquakes. It provides details on different types of floods such as riverine floods and coastal floods. It also describes the effects of floods such as physical damage, casualties, and economic impacts. For earthquakes, it discusses the causes of earthquakes including movement along faults, and types of faults such as normal and reverse faults. It further describes shallow focus and deep focus earthquakes.
2. HERA Nom llatí: Juno Competències: Deesa del matrimoni Atributs:diadema, asseguda en un tron, paó
3. HADES Nom llatí: Plutó Competències: déu dels inferns que regna sobre els morts. Atributs: barbut, assegut en un tron o conduint un carro, sostenint un ceptre.
4. POSIDÓ Nom llatí: Neptú . Competències: déu del mar i dels terratrèmols Atributs: barbut, trident, peix, cavall i carro
5. ZEUS Nom llatí:Júpiter. Competències: déu del cel i dels fenòmens atmosfèrics. Atributs: barbut, assegut en un tron, amb un llamp.
6. AFRODITA Nom llatí: Venus Competències: Deesa de l'amor, de la sexualitat i de la bellesa Atributs: normalment nua, sobre una petxina, amb una poma, coloms, pardals
7. DEMÈTER Nom llatí: Ceres Competències: deessa de l'agricultura, donà el blat i les lleis als homes. Atributs: espigues de blat, falç, torxa, serp
8. HÉSTIA Nom llatí: Vesta Competències: deessa de la llar familiar i pàtria. Atributs: flama, ase.
9. ARES Nom llatí: Mart Competències: déu de la guerra Atributs: sense barba, cuïrassa, casc, escut, llança, gall
10. HEFEST Nom llatí: Vulcà Competències: déu del foc i dels ferrers Atributs: lleig, coix i brut, tors nu, tenalles, martell i enclusa
11. ATENA Nom llatí: Minerva Competències: deessa de la guerra i de la saviesa Atributs: ègida, casc, llança i escut
12. ÀRTEMIS Nom llatí: Diana Competències: deessa de la caça i els boscos Atributs: arc i carcaix, mitja lluna, cèrvol, gos, acompanyada de les nimfes
13. APOL·LO Nom llatí: Apol·lo Competències: déu de la bellesa, la música i les arts Atributs: jove bell i sense barba, amb lira o arc i carcaix
14. HERMES Nom llatí: Mercuri Competències: missatger dels déus, déu del comerç i dels lladres Atributs: sense barba, amb sandàlies i barret
15. EROS Nom llatí: Cupido Competències: déu de l'amor Atributs: jove o nen nu, amb arc, fletxes i ales a l'esquena
16. DIONIS Nom llatí: Bacus Competències: déu del vi, de l'irracional i del teatre Atributs: copa, vinya, pàmpols, tirs i pantera