| data science day 2023: Ace the Data Science Interview Kevin Huo, Nick Singh, 2021 |
| data science day 2023: R for Data Science Hadley Wickham, Garrett Grolemund, 2016-12-12 Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true signals in your dataset Communicate—learn R Markdown for integrating prose, code, and results |
| data science day 2023: Encyclopedia of Data Science and Machine Learning Wang, John, 2023-01-20 Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed. The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more. It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities. Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians. |
| data science day 2023: Data Science Zhiwen Yu, Qilong Han, Hongzhi Wang, Bin Guo, Xiaokang Zhou, Xianhua Song, Zeguang Lu, 2023-09-14 This two-volume set (CCIS 1879 and 1880) constitutes the refereed proceedings of the 9th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2023 held in Harbin, China, during September 22–24, 2023. The 52 full papers and 14 short papers presented in these two volumes were carefully reviewed and selected from 244 submissions. The papers are organized in the following topical sections: Part I: Applications of Data Science, Big Data Management and Applications, Big Data Mining and Knowledge Management, Data Visualization, Data-driven Security, Infrastructure for Data Science, Machine Learning for Data Science and Multimedia Data Management and Analysis. Part II: Data-driven Healthcare, Data-driven Smart City/Planet, Social Media and Recommendation Systems and Education using big data, intelligent computing or data mining, etc. |
| data science day 2023: Modern Data Science with R Benjamin S. Baumer, Daniel T. Kaplan, Nicholas J. Horton, 2021-03-31 From a review of the first edition: Modern Data Science with R... is rich with examples and is guided by a strong narrative voice. What’s more, it presents an organizing framework that makes a convincing argument that data science is a course distinct from applied statistics (The American Statistician). Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world data problems. Rather than focus exclusively on case studies or programming syntax, this book illustrates how statistical programming in the state-of-the-art R/RStudio computing environment can be leveraged to extract meaningful information from a variety of data in the service of addressing compelling questions. The second edition is updated to reflect the growing influence of the tidyverse set of packages. All code in the book has been revised and styled to be more readable and easier to understand. New functionality from packages like sf, purrr, tidymodels, and tidytext is now integrated into the text. All chapters have been revised, and several have been split, re-organized, or re-imagined to meet the shifting landscape of best practice. |
| data science day 2023: Python for Data Science Erick Thompson, 2020-10-30 |
| data science day 2023: Data Science and Machine Learning Diana Benavides-Prado, Sarah Erfani, Philippe Fournier-Viger, Yee Ling Boo, Yun Sing Koh, 2023-12-04 This book constitutes the proceedings of the 21st Australasian Conference on Data Science and Machine Learning, AusDM 2023, held in Auckland, New Zealand, during December 11–13, 2023. The 20 full papers presented in this book were carefully reviewed and selected from 50 submissions. The papers are organized in the following topical sections: research track and application track. They deal with topics around data science and machine learning in everyday life. |
| data science day 2023: Text as Data Justin Grimmer, Margaret E. Roberts, Brandon M. Stewart, 2022-03-29 A guide for using computational text analysis to learn about the social world From social media posts and text messages to digital government documents and archives, researchers are bombarded with a deluge of text reflecting the social world. This textual data gives unprecedented insights into fundamental questions in the social sciences, humanities, and industry. Meanwhile new machine learning tools are rapidly transforming the way science and business are conducted. Text as Data shows how to combine new sources of data, machine learning tools, and social science research design to develop and evaluate new insights. Text as Data is organized around the core tasks in research projects using text—representation, discovery, measurement, prediction, and causal inference. The authors offer a sequential, iterative, and inductive approach to research design. Each research task is presented complete with real-world applications, example methods, and a distinct style of task-focused research. Bridging many divides—computer science and social science, the qualitative and the quantitative, and industry and academia—Text as Data is an ideal resource for anyone wanting to analyze large collections of text in an era when data is abundant and computation is cheap, but the enduring challenges of social science remain. Overview of how to use text as data Research design for a world of data deluge Examples from across the social sciences and industry |
| data science day 2023: Proceedings of the 5th International Conference on Data Science, Machine Learning and Applications; Volume 1 Amit Kumar, |
| data science day 2023: Data Science and Machine Learning Dirk P. Kroese, Zdravko Botev, Thomas Taimre, Radislav Vaisman, 2019-11-20 Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code |
| data science day 2023: Data-Driven Science and Engineering Steven L. Brunton, J. Nathan Kutz, 2022-05-05 A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®. |
| data science day 2023: Proceedings of the 5th International Conference on Data Science, Machine Learning and Applications; Volume 2 Amit Kumar, |
| data science day 2023: Data Science on AWS Chris Fregly, Antje Barth, 2021-04-07 With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level upyour skills. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance. Apply the Amazon AI and ML stack to real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and more Use automated machine learning to implement a specific subset of use cases with SageMaker Autopilot Dive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, model training, and deployment Tie everything together into a repeatable machine learning operations pipeline Explore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis and Managed Streaming for Apache Kafka Learn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more |
| data science day 2023: Data Science Tiffany Timbers, Trevor Campbell, Melissa Lee, Joel Ostblom, Lindsey Heagy, 2024-08-23 Data Science: A First Introduction with Python focuses on using the Python programming language in Jupyter notebooks to perform data manipulation and cleaning, create effective visualizations, and extract insights from data using classification, regression, clustering, and inference. It emphasizes workflows that are clear, reproducible, and shareable, and includes coverage of the basics of version control. Based on educational research and active learning principles, the book uses a modern approach to Python and includes accompanying autograded Jupyter worksheets for interactive, self-directed learning. The text will leave readers well-prepared for data science projects. It is designed for learners from all disciplines with minimal prior knowledge of mathematics and programming. The authors have honed the material through years of experience teaching thousands of undergraduates at the University of British Columbia. Key Features: Includes autograded worksheets for interactive, self-directed learning. Introduces readers to modern data analysis and workflow tools such as Jupyter notebooks and GitHub, and covers cutting-edge data analysis and manipulation Python libraries such as pandas, scikit-learn, and altair. Is designed for a broad audience of learners from all backgrounds and disciplines. |
| data science day 2023: Doing Data Science Cathy O'Neil, Rachel Schutt, 2013-10-09 Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know. In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science. Topics include: Statistical inference, exploratory data analysis, and the data science process Algorithms Spam filters, Naive Bayes, and data wrangling Logistic regression Financial modeling Recommendation engines and causality Data visualization Social networks and data journalism Data engineering, MapReduce, Pregel, and Hadoop Doing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course. |
| data science day 2023: STEP BY STEP PROJECT-BASED TUTORIALS DATA SCIENCE WITH PYTHON GUI: TRAFFIC AND HEART ATTACK ANALYSIS AND PREDICTION Vivian Siahaan, Rismon Hasiholan Sianipar, 2023-06-21 In this book, you will implement two data science projects using Scikit-Learn, Scipy, and other libraries with Python GUI. In chapter 1, you will learn how to use Scikit-Learn, Scipy, and other libraries to perform how to predict traffic (number of vehicles) in four different junctions using Traffic Prediction Dataset (https://viviansiahaan.blogspot.com/2023/06/step-by-step-project-based-tutorials.html). This dataset contains 48.1k (48120) observations of the number of vehicles each hour in four different junctions: 1) DateTime; 2) Juction; 3) Vehicles; and 4) ID. Here's the outline of the steps involved in predicting traffic: Dataset Preparation: Extract the dataset files to a local folder. Import the necessary libraries, such as pandas and numpy. Load the dataset into a pandas DataFrame. Exploratory Data Analysis (EDA). Explore the dataset to understand its structure and characteristics. Check for missing values or anomalies in the data. Examine the distribution of the target variable (number of vehicles). Visualize the data using plots or graphs to gain insights into the patterns and trends.; Data Preprocessing: Convert the DateTime column to a datetime data type for easier manipulation. Extract additional features from the DateTime column, such as hour, day of the week, month, etc., which might be relevant for traffic prediction. Encode categorical variables, such as Junction, using one-hot encoding or label encoding. Split the dataset into training and testing sets for model evaluation.; Feature Selection/Engineering: Perform feature selection techniques, such as correlation analysis or feature importance, to identify the most relevant features for traffic prediction. Engineer new features that might capture underlying patterns or relationships in the data, such as lagged variables or rolling averages.; Model Selection and Training: Choose an appropriate machine learning model for traffic prediction, such as linear regression, decision trees, random forests, or gradient boosting. Split the data into input features (X) and target variable (y). Split the data further into training and testing sets. Fit the chosen model to the training data. Evaluate the model's performance using appropriate evaluation metrics (e.g., mean squared error, R-squared). Model Evaluation and Hyperparameter Tuning. Assess the model's performance on the testing set. Tune the hyperparameters of the chosen model to improve its performance. Use techniques like grid search or randomized search to find the optimal hyperparameters.; Model Deployment and Prediction: Once satisfied with the model's performance, retrain it on the entire dataset (including the testing set). Save the trained model for future use. Utilize the model to make predictions on new, unseen data for traffic prediction. In chapter 2, you will learn how to use Scikit-Learn, NumPy, Pandas, and other libraries to perform how to analyze and predict heart attack using Heart Attack Analysis & Prediction Dataset (https://viviansiahaan.blogspot.com/2023/06/step-by-step-project-based-tutorials.html). Following are the outline steps for analyzing and predicting heart attacks using the Heart Attack Analysis & Prediction Dataset. Introduction and Dataset Description: Provide an introduction to the topic of heart attack analysis and prediction. Briefly explain the dataset's source and its features, such as age, sex, blood pressure, cholesterol levels, etc.; Data Loading: Explain how to load the Heart Attack Analysis & Prediction Dataset into your Python environment using libraries like Pandas. You can mention that the dataset should be in a CSV format and demonstrate how to load it.; Data Exploration: Describe the importance of exploring the dataset before analysis. Show how to examine the dataset's structure, check for missing values, understand the statistical summary, and visualize the data using plots or charts.; Data Preprocessing: Explain the steps required to preprocess the dataset before feeding it into a machine learning model. This may include handling missing values, encoding categorical variables, scaling numerical features, and dealing with any other necessary data transformations.; Data Splitting: Describe how to split the preprocessed data into training and testing sets. Emphasize the importance of having separate data for training and evaluation to assess the model's performance accurately.; Model Building and Training: Explain how to choose an appropriate machine learning algorithm for heart attack prediction and how to build a model using libraries like Scikit-Learn. Outline the steps involved in training the model on the training dataset.; Model Evaluation: Describe how to evaluate the trained model's performance using appropriate evaluation metrics, such as accuracy, precision, recall, and F1 score. Demonstrate how to interpret the evaluation results and assess the model's predictive capabilities.; Predictions on New Data: Explain how to use the trained model to make predictions on new, unseen data. Demonstrate the process of feeding new data to the model and obtaining predictions for heart attack risk. |
| data science day 2023: Advances on Intelligent Computing and Data Science Faisal Saeed, Fathey Mohammed, Errais Mohammed, Tawfik Al-Hadhrami, Mohammed Al-Sarem, 2023-08-16 This book presents the papers included in the proceedings of the 3rd International Conference of Advanced Computing and Informatics (ICACin’22) that was held in Casablanca, Morocco, on October 15–16, 2022. A total of 98 papers were submitted to the conference, but only 60 papers were accepted and published in this book with an acceptance rate of 61%. The book presents several hot research topics which include artificial intelligence and data science, big data analytics, Internet of Things (IoT) and smart cities, information security, cloud computing and networking, and computational informatics. |
| data science day 2023: Mathematical Methods in Data Science Jingli Ren, Haiyan Wang, 2023-01-06 Mathematical Methods in Data Science covers a broad range of mathematical tools used in data science, including calculus, linear algebra, optimization, network analysis, probability and differential equations. Based on the authors' recently published and previously unpublished results, this book introduces a new approach based on network analysis to integrate big data into the framework of ordinary and partial differential equations for dataanalysis and prediction. With data science being used in virtually every aspect of our society, the book includes examples and problems arising in data science and the clear explanation of advanced mathematical concepts, especially data-driven differential equations, making it accessible to researchers and graduate students in mathematics and data science. - Combines a broad spectrum of mathematics, including linear algebra, optimization, network analysis and ordinary and partial differential equations for data science - Written by two researchers who are actively applying mathematical and statistical methods as well as ODE and PDE for data analysis and prediction - Highly interdisciplinary, with content spanning mathematics, data science, social media analysis, network science, financial markets, and more - Presents a wide spectrum of topics in a logical order, including probability, linear algebra, calculus and optimization, networks, ordinary differential and partial differential equations |
| data science day 2023: Computational Intelligence in Data Science Mieczyslaw Lech Owoc, |
| data science day 2023: Emerging Innovations and Applications in Computer Science, Statistics and Data Science V. Prakash, Manimannan G, P. Arumugam, 2024-10-14 The rapid advancement of technology and the rise of data-driven innovations have profoundly shaped research across a variety of fields. This edited book consolidates pioneering studies and analyses that utilize cutting-edge approaches such as machine learning, statistical techniques, and data-centric methodologies. From predictive analytics in healthcare to breakthroughs in cyber security and Internet of Things (IoT) applications, the content presents a wealth of insights aimed at tackling challenges in today’s fast-paced, digitally transformed world. It underscores the transformative role of artificial intelligence, big data analytics, and block chain technologies in revolutionizing sectors like healthcare, finance, e-commerce, and climate research. This collection of chapters spans a diverse range of interdisciplinary subjects. It features healthcare studies that explore predictive models for conditions such as cervical and lung cancers, as well as thyroid disorders, showcasing the revolutionary impact of artificial intelligence in improving diagnostic precision and treatment strategies. Concurrently, research on IoT, cloud computing, and block chain highlights the growing necessity of secure and interconnected infrastructures in paving the way for smart living and decentralized systems. Statistical methodologies, including time series analysis, Bayesian models, and survival analysis, are explored in real-world contexts, offering valuable insights into climatic trends, consumer behavior, and industrial advancements. This book is the result of a collaborative effort by esteemed researchers and practitioners, whose expertise provides innovative solutions to real-world challenges. By bridging theoretical advancements with practical implementations, the volume serves as a comprehensive resource for scholars, industry experts, and students. We trust that this work will inspire further research and catalyze meaningful progress in the domains of technology, healthcare, and beyond. |
| data science day 2023: The Ethical Algorithm Michael Kearns, Aaron Roth, 2020 Algorithms have made our lives more efficient and entertaining--but not without a significant cost. Can we design a better future, one in which societial gains brought about by technology are balanced with the rights of citizens? The Ethical Algorithm offers a set of principled solutions based on the emerging and exciting science of socially aware algorithm design. |
| data science day 2023: Enhancing Business Communications and Collaboration Through Data Science Applications Geada, Nuno, Leal Jamil, George, 2023-03-21 Digital evolution has become increasingly present in our lives, whether on cellphones, computers, watches, or other appliances. As a result of the wide access we have to the digital world, the amount of data generated daily is vast. This density of information generated at every moment can be the insight needed for the success of an organization. Much is said about data-based decision-making to generate the best results. The new capabilities of data intelligence unleashed by the emergence of cloud computing and artificial intelligence make it one of the most promising areas of digital transformation change management. Enhancing Business Communications and Collaboration Through Data Science Applications provides relevant theoretical frameworks and the latest empirical research findings in the area. It is written for professionals who wish to improve their understanding of the strategic role of trust at different levels of the information and knowledge society. Covering topics such as data science, online business communication, and user-centered design, this premier reference source is an ideal resource for business managers and leaders, entrepreneurs, data scientists, data analysts, sociologists, students and educators of higher education, librarians, researchers, and academicians. |
| data science day 2023: Data Science and Applications for Modern Power Systems Le Xie, Yang Weng, Ram Rajagopal, 2023-06-20 This book offers a comprehensive collection of research articles that utilize data—in particular large data sets—in modern power systems operation and planning. As the power industry moves towards actively utilizing distributed resources with advanced technologies and incentives, it is becoming increasingly important to benefit from the available heterogeneous data sets for improved decision-making. The authors present a first-of-its-kind comprehensive review of big data opportunities and challenges in the smart grid industry. This book provides succinct and useful theory, practical algorithms, and case studies to improve power grid operations and planning utilizing big data, making it a useful graduate-level reference for students, faculty, and practitioners on the future grid. |
| data science day 2023: Recent Advancement in Geoinformatics and Data Science Xiaogang Ma, Matty Mookerjee, Leslie Hsu, Denise Hills, 2023-04-11 |
| data science day 2023: Large-Scale Data Analytics with Python and Spark Isaac Triguero, Mikel Galar, 2023-11-30 A hands-on textbook for courses on large-scale data analytics and designing machine learning solutions. |
| data science day 2023: AI and Big Data’s Potential for Disruptive Innovation Strydom, Moses, Buckley, Sheryl, 2019-09-27 Big data and artificial intelligence (AI) are at the forefront of technological advances that represent a potential transformational mega-trend—a new multipolar and innovative disruption. These technologies, and their associated management paradigm, are already rapidly impacting many industries and occupations, but in some sectors, the change is just beginning. Innovating ahead of emerging technologies is the new imperative for any organization that aspires to succeed in the next decade. Faced with the power of this AI movement, it is imperative to understand the dynamics and new codes required by the disruption and to adapt accordingly. AI and Big Data’s Potential for Disruptive Innovation provides emerging research exploring the theoretical and practical aspects of successfully implementing new and innovative technologies in a variety of sectors including business, transportation, and healthcare. Featuring coverage on a broad range of topics such as semantic mapping, ethics in AI, and big data governance, this book is ideally designed for IT specialists, industry professionals, managers, executives, researchers, scientists, and engineers seeking current research on the production of new and innovative mechanization and its disruptions. |
| data science day 2023: Mastering Marketing Data Science Iain Brown, 2024-04-29 Unlock the Power of Data: Transform Your Marketing Strategies with Data Science In the digital age, understanding the symbiosis between marketing and data science is not just an advantage; it's a necessity. In Mastering Marketing Data Science: A Comprehensive Guide for Today's Marketers, Dr. Iain Brown, a leading expert in data science and marketing analytics, offers a comprehensive journey through the cutting-edge methodologies and applications that are defining the future of marketing. This book bridges the gap between theoretical data science concepts and their practical applications in marketing, providing readers with the tools and insights needed to elevate their strategies in a data-driven world. Whether you're a master's student, a marketing professional, or a data scientist keen on applying your skills in a marketing context, this guide will empower you with a deep understanding of marketing data science principles and the competence to apply these principles effectively. Comprehensive Coverage: From data collection to predictive analytics, NLP, and beyond, explore every facet of marketing data science. Practical Applications: Engage with real-world examples, hands-on exercises in both Python & SAS, and actionable insights to apply in your marketing campaigns. Expert Guidance: Benefit from Dr. Iain Brown's decade of experience as he shares cutting-edge techniques and ethical considerations in marketing data science. Future-Ready Skills: Learn about the latest advancements, including generative AI, to stay ahead in the rapidly evolving marketing landscape. Accessible Learning: Tailored for both beginners and seasoned professionals, this book ensures a smooth learning curve with a clear, engaging narrative. Mastering Marketing Data Science is designed as a comprehensive how-to guide, weaving together theory and practice to offer a dynamic, workbook-style learning experience. Dr. Brown's voice and expertise guide you through the complexities of marketing data science, making sophisticated concepts accessible and actionable. |
| data science day 2023: Data Science and Human-Environment Systems Steven M. Manson, 2023-01-31 Transformation of the Earth's social and ecological systems is occurring at a rate and magnitude unparalleled in human experience. Data science is a revolutionary new way to understand human-environment relationships at the heart of pressing challenges like climate change and sustainable development. However, data science faces serious shortcomings when it comes to human-environment research. There are challenges with social and environmental data, the methods that manipulate and analyze the information, and the theory underlying the data science itself; as well as significant legal, ethical and policy concerns. This timely book offers a comprehensive, balanced, and accessible account of the promise and problems of this work in terms of data, methods, theory, and policy. It demonstrates the need for data scientists to work with human-environment scholars to tackle pressing real-world problems, making it ideal for researchers and graduate students in Earth and environmental science, data science and the environmental social sciences. |
| data science day 2023: Data Science for the Geosciences Lijing Wang, David Zhen Yin, Jef Caers, 2023-07-31 An accessible text providing data science foundations to address earth science questions using real-world case studies. |
| data science day 2023: Data Engineering and Data Science Kukatlapalli Pradeep Kumar, Aynur Unal, Vinay Jha Pillai, Hari Murthy, M. Niranjanamurthy, 2023-10-03 DATA ENGINEERING and DATA SCIENCE Written and edited by one of the most prolific and well-known experts in the field and his team, this exciting new volume is the “one-stop shop” for the concepts and applications of data science and engineering for data scientists across many industries. The field of data science is incredibly broad, encompassing everything from cleaning data to deploying predictive models. However, it is rare for any single data scientist to be working across the spectrum day to day. Data scientists usually focus on a few areas and are complemented by a team of other scientists and analysts. Data engineering is also a broad field, but any individual data engineer doesn’t need to know the whole spectrum of skills. Data engineering is the aspect of data science that focuses on practical applications of data collection and analysis. For all the work that data scientists do to answer questions using large sets of information, there have to be mechanisms for collecting and validating that information. In this exciting new volume, the team of editors and contributors sketch the broad outlines of data engineering, then walk through more specific descriptions that illustrate specific data engineering roles. Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems. This book brings together machine learning, engineering mathematics, and mathematical physics to integrate modeling and control of dynamical systems with modern methods in data science. It highlights many of the recent advances in scientific computing that enable data-driven methods to be applied to a diverse range of complex systems, such as turbulence, the brain, climate, epidemiology, finance, robotics, and autonomy. Whether for the veteran engineer or scientist working in the field or laboratory, or the student or academic, this is a must-have for any library. |
| data science day 2023: Mastering Data Science Cybellium Ltd, Unleash the Power of Insights from Data Are you ready to embark on a transformative journey into the world of data science? Mastering Data Science is your comprehensive guide to unlocking the full potential of data for extracting valuable insights and driving informed decisions. Whether you're an aspiring data scientist looking to enhance your skills or a business leader seeking to leverage data-driven strategies, this book equips you with the knowledge and tools to master the art of data science. Key Features: 1. Dive into Data Science: Immerse yourself in the realm of data science, understanding its core principles, methodologies, and applications. Build a solid foundation that empowers you to extract meaningful insights from complex datasets. 2. Data Exploration and Visualization: Master the art of data exploration and visualization. Learn how to analyze datasets, uncover patterns, and create compelling visualizations that reveal hidden trends. 3. Statistical Analysis and Hypothesis Testing: Uncover the power of statistical analysis and hypothesis testing. Explore techniques for making data-driven inferences, validating assumptions, and drawing meaningful conclusions. 4. Machine Learning Fundamentals: Delve into machine learning concepts and techniques. Learn about supervised and unsupervised learning, feature engineering, model selection, and evaluation. 5. Predictive Analytics: Discover the realm of predictive analytics. Learn how to build predictive models that forecast future outcomes, enabling proactive decision-making. 6. Natural Language Processing (NLP) and Text Mining: Explore NLP and text mining techniques. Learn how to process and analyze textual data, extract sentiments, and uncover insights from unstructured content. 7. Time Series Analysis: Master time series analysis for modeling sequential data. Learn how to forecast trends, identify seasonality, and make predictions based on temporal patterns. 8. Big Data and Data Wrangling: Dive into big data analytics and data wrangling. Learn how to handle and preprocess large datasets, ensuring data quality and usability. 9. Deep Learning and Neural Networks: Uncover the world of deep learning and neural networks. Learn how to build and train deep learning models for tasks like image recognition and natural language understanding. 10. Real-World Applications: Gain insights into real-world applications of data science across industries. From healthcare to finance, explore how organizations harness data science for strategic decision-making. Who This Book Is For: Mastering Data Science is an indispensable resource for aspiring data scientists, analysts, and business professionals who want to excel in extracting insights from data. Whether you're new to data science or seeking advanced techniques, this book will guide you through the intricacies and empower you to harness the full potential of data for innovation. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com |
| data science day 2023: People and Data Thomas C. Redman, 2023-07-03 People and Data is an innovative exploration of the relationship between non-data professionals and data in an organization's success, and why it is only when they work together that a business can unlock its full potential. This book explains how most companies are yet to take advantage of the value that data offers. Their structures and processes are unfit for data and their biggest mistake is that regular employees are not included in the data-driven effort. People and Data illustrates how to change this. It shows how and why improving data quality should be an organization's first priority, how to tackle the tough organizational issues, such as departmental silos, that get in the way and how to upskill the whole workforce to get the best out of the organization's data. It is a practical guide written by a global expert which explains how companies can put their data to work by building it into all aspects of the business including their structure, culture and workforce design. By infusing the whole organization with data in this way employees at any level can use insights from the data to improve business performance. Full of practical tips and advice, People and Data includes a Resource Centre featuring a curriculum for training employees, and eight tools that will help companies to leverage their data to meet their business goals and upskill their employees so that everyone can benefit from the power of data. With important takeaways and real-world examples from organizations including AT&T and Morgan Stanley, this book is essential reading for all those wanting to allow their people and data to reach their full potential but are not sure where to start. |
| data science day 2023: Data Science for Genomics Amit Kumar Tyagi, Ajith Abraham, 2022-11-27 Data Science for Genomics presents the foundational concepts of data science as they pertain to genomics, encompassing the process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions and supporting decision-making. Sections cover Data Science, Machine Learning, Deep Learning, data analysis, and visualization techniques. The authors then present the fundamentals of Genomics, Genetics, Transcriptomes and Proteomes as basic concepts of molecular biology, along with DNA and key features of the human genome, as well as the genomes of eukaryotes and prokaryotes. Techniques that are more specifically used for studying genomes are then described in the order in which they are used in a genome project, including methods for constructing genetic and physical maps. DNA sequencing methodology and the strategies used to assemble a contiguous genome sequence and methods for identifying genes in a genome sequence and determining the functions of those genes in the cell. Readers will learn how the information contained in the genome is released and made available to the cell, as well as methods centered on cloning and PCR. - Provides a detailed explanation of data science concepts, methods and algorithms, all reinforced by practical examples that are applied to genomics - Presents a roadmap of future trends suitable for innovative Data Science research and practice - Includes topics such as Blockchain technology for securing data at end user/server side - Presents real world case studies, open issues and challenges faced in Genomics, including future research directions and a separate chapter for Ethical Concerns |
| data science day 2023: Mastering the Data Paradox Nitin Seth, 2024-03-18 There are two remarkable phenomena that are unfolding almost simultaneously. The first is the emergence of a data-first world, where data has become a central driving force, shaping industries and fueling innovation. The second is the dawn of the AI age, propelled by the advent of Generative AI, that has created the possibility to leverage the data of the world for the first time. The convergence of these two, with data as the common denominator, holds immense promise and the opportunities are boundless. This book provides us with opportunities to push our thinking, to innovate, to transform and to create a better future at all levels—individual, enterprise and the world. |
| data science day 2023: COVID-19: Integrating Artificial Intelligence, Data Science, Mathematics, Medicine and Public Health, Epidemiology, Neuroscience, Neurorobotics, and Biomedical Science in Pandemic Management, volume II Atefeh Abedini, Reza Lashgari, 2024-02-29 |
| data science day 2023: Data Science, Analytics and Machine Learning with R Luiz Paulo Favero, Patricia Belfiore, Rafael de Freitas Souza, 2023-01-23 Data Science, Analytics and Machine Learning with R explains the principles of data mining and machine learning techniques and accentuates the importance of applied and multivariate modeling. The book emphasizes the fundamentals of each technique, with step-by-step codes and real-world examples with data from areas such as medicine and health, biology, engineering, technology and related sciences. Examples use the most recent R language syntax, with recognized robust, widespread and current packages. Code scripts are exhaustively commented, making it clear to readers what happens in each command. For data collection, readers are instructed how to build their own robots from the very beginning. In addition, an entire chapter focuses on the concept of spatial analysis, allowing readers to build their own maps through geo-referenced data (such as in epidemiologic research) and some basic statistical techniques. Other chapters cover ensemble and uplift modeling and GLMM (Generalized Linear Mixed Models) estimations, both linear and nonlinear. - Presents a comprehensive and practical overview of machine learning, data mining and AI techniques for a broad multidisciplinary audience - Serves readers who are interested in statistics, analytics and modeling, and those who wish to deepen their knowledge in programming through the use of R - Teaches readers how to apply machine learning techniques to a wide range of data and subject areas - Presents data in a graphically appealing way, promoting greater information transparency and interactive learning |
| data science day 2023: Class 10 CBSE Data Science Previous Years Unsolved Questions Paper Book Manish Soni, 2024-11-10 Prepare for success in data science with Data Science Class 10 Previous Years Unsolved Questions Paper Book! This essential resource compiles unsolved questions from previous years' exams, tailored for Class 10 students to strengthen their understanding and problem-solving skills in data science. Each question is designed to challenge students and enhance their analytical thinking, covering key topics in data handling, statistics, probability, and more. Ideal for self-assessment and exam practice, this book is perfect for students aiming to build confidence and excel in their data science studies. |
| data science day 2023: Fuzzy Computing in Data Science Sachi Nandan Mohanty, Prasenjit Chatterjee, Bui Thanh Hung, 2022-11-03 FUZZY COMPUTING IN DATA SCIENCE This book comprehensively explains how to use various fuzzy-based models to solve real-time industrial challenges. The book provides information about fundamental aspects of the field and explores the myriad applications of fuzzy logic techniques and methods. It presents basic conceptual considerations and case studies of applications of fuzzy computation. It covers the fundamental concepts and techniques for system modeling, information processing, intelligent system design, decision analysis, statistical analysis, pattern recognition, automated learning, system control, and identification. The book also discusses the combination of fuzzy computation techniques with other computational intelligence approaches such as neural and evolutionary computation. Audience Researchers and students in computer science, artificial intelligence, machine learning, big data analytics, and information and communication technology. |
| data science day 2023: Data Science and Analytics Strategy Kailash Awati, Alexander Scriven, 2023-04-05 This book describes how to establish data science and analytics capabilities in organisations using Emergent Design, an evolutionary approach that increases the chances of successful outcomes while minimising upfront investment. Based on their experiences and those of a number of data leaders, the authors provide actionable advice on data technologies, processes, and governance structures so that readers can make choices that are appropriate to their organisational contexts and requirements. The book blends academic research on organisational change and data science processes with real-world stories from experienced data analytics leaders, focusing on the practical aspects of setting up a data capability. In addition to a detailed coverage of capability, culture, and technology choices, a unique feature of the book is its treatment of emerging issues such as data ethics and algorithmic fairness. Data Science and Analytics Strategy: An Emergent Design Approach has been written for professionals who are looking to build data science and analytics capabilities within their organisations as well as those who wish to expand their knowledge and advance their careers in the data space. Providing deep insights into the intersection between data science and business, this guide will help professionals understand how to help their organisations reap the benefits offered by data. Most importantly, readers will learn how to build a fit-for-purpose data science capability in a manner that avoids the most common pitfalls. |
| data science day 2023: Data Feminism Catherine D'Ignazio, Lauren F. Klein, 2020-03-31 A new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism. Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom? Data science for whom? Data science with whose interests in mind? The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. In Data Feminism, Catherine D'Ignazio and Lauren Klein present a new way of thinking about data science and data ethics—one that is informed by intersectional feminist thought. Illustrating data feminism in action, D'Ignazio and Klein show how challenges to the male/female binary can help challenge other hierarchical (and empirically wrong) classification systems. They explain how, for example, an understanding of emotion can expand our ideas about effective data visualization, and how the concept of invisible labor can expose the significant human efforts required by our automated systems. And they show why the data never, ever “speak for themselves.” Data Feminism offers strategies for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science. But Data Feminism is about much more than gender. It is about power, about who has it and who doesn't, and about how those differentials of power can be challenged and changed. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will enable a …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels to …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
