Data capture and analysis Books

Book SynopsisThis bestseller can help anyone whose role is to try to find specific causes for failures.It provides detailed steps for solving problems, focusing more heavily on the analytical process involved in finding the actual causes of problems. It does this using figures, diagrams, and tools useful for helping to make our thinking visible. This increases our ability to see what is truly significant and to better identify errors in our thinking. In the sections on finding root causes, this second edition now includes more examples on the use of multi-vari charts; how thought experiments can help guide data interpretation; how to enhance the value of the data collection process; cautions for analyzing data; and what to do if one can''t find the causes. In its guidance on solution identification, biomimicry and TRIZ have been added as potential solution identification techniques. In addition, the appendices have been revised to include: an expanded breakdown of the 7 M''s, which includes more than 50 specific possible causes; forms for tracking causes and solutions, which can help maintain alignment of actions; techniques for how to enhance the interview process; and example responses to problem situations that the reader can analyze for appropriateness.

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Book SynopsisLearn to view, edit and analyse geospatial data using QGIS and Python 3Key Features Leverage the power of QGIS to add professionalism to your maps Explore and work with the newly released features like Python 3, GeoPackage, 3D views, Print layouts in QGIS 3.4 Build your own plugins and customize maps using QT designer Book DescriptionQGIS 3.4 is the first LTR (long term release) of QGIS version 3. This is a giant leap forward for the project with tons of new features and impactful changes. Learn QGIS is fully updated for QGIS 3.4, covering its processing engine update, Python 3 de-facto coding environment, and the GeoPackage format.This book will help you get started on your QGIS journey, guiding you to develop your own processing pathway. You will explore the user interface, loading your data, editing, and then creating data. QGIS often surprises new users with its mapping capabilities; you will discover how easily you can style and create your first map. But that’s not all! In the final part of the book, you’ll learn about spatial analysis and the powerful tools in QGIS, and conclude by looking at Python processing options.By the end of the book, you will have become proficient in geospatial analysis using QGIS and Python.What you will learn Explore various ways to load data into QGIS Understand how to style data and present it in a map Create maps and explore ways to expand them Get acquainted with the new processing toolbox in QGIS 3.4 Manipulate your geospatial data and gain quality insights Understand how to customize QGIS 3.4 Work with QGIS 3.4 in 3D Who this book is forIf you are a developer or consultant familiar with the basic functions and processes of GIS and want to learn how to use QGIS to analyze geospatial data and create rich mapping applications, this book is for you. You’ll also find this book useful if you’re new to QGIS and wish to grasp its fundamentalsTable of ContentsTable of Contents Where do I start? Data Creation and Editing Visualizing data Creating Great Maps Running geospatial queries on data Extending and customizing QGIS 3

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Book SynopsisA practical guide to getting the most out of Excel, using it for data preparation, applying machine learning models (including cloud services) and understanding the outcome of the data analysis.Key Features Use Microsoft's product Excel to build advanced forecasting models using varied examples Cover range of machine learning tasks such as data mining, data analytics, smart visualization, and more Derive data-driven techniques using Excel plugins and APIs without much code required Book DescriptionWe have made huge progress in teaching computers to perform difficult tasks, especially those that are repetitive and time-consuming for humans. Excel users, of all levels, can feel left behind by this innovation wave. The truth is that a large amount of the work needed to develop and use a machine learning model can be done in Excel.The book starts by giving a general introduction to machine learning, making every concept clear and understandable. Then, it shows every step of a machine learning project, from data collection, reading from different data sources, developing models, and visualizing the results using Excel features and offerings. In every chapter, there are several examples and hands-on exercises that will show the reader how to combine Excel functions, add-ins, and connections to databases and to cloud services to reach the desired goal: building a full data analysis flow. Different machine learning models are shown, tailored to the type of data to be analyzed.At the end of the book, the reader is presented with some advanced use cases using Automated Machine Learning, and artificial neural network, which simplifies the analysis task and represents the future of machine learning.What you will learn Use Excel to preview and cleanse datasets Understand correlations between variables and optimize the input to machine learning models Use and evaluate different machine learning models from Excel Understand the use of different visualizations Learn the basic concepts and calculations to understand how artificial neural networks work Learn how to connect Excel to the Microsoft Azure cloud Get beyond proof of concepts and build fully functional data analysis flows Who this book is forThis book is for data analysis, machine learning enthusiasts, project managers, and someone who doesn't want to code much for performing core tasks of machine learning. Each example will help you perform end-to-end smart analytics. Working knowledge of Excel is required.Table of ContentsTable of Contents Implementing Machine Learning Algorithms Hands-on examples of machine learning models Importing Data into Excel from Different Data Sources Data cleansing and preliminary data analysis Correlations and the Importance of Variables Data Mining Models in Excel Hands-On Examples Implementing Time Series Visualizing data in diagrams, histograms, and maps Artificial Neural Networks Azure and Excel - Machine Learning in the Cloud The future of Machine Learning

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Book SynopsisBuild a continuously learning and adapting organization that can extract increasing levels of business, customer and operational value from the amalgamation of data and advanced analytics such as AI and Machine Learning Key Features Master the Big Data Business Model Maturity Index methodology to transition to a value-driven organizational mindset Acquire implementable knowledge on digital transformation through 8 practical laws Explore the economics behind digital assets (data and analytics) that appreciate in value when constructed and deployed correctly Book DescriptionIn today’s digital era, every organization has data, but just possessing enormous amounts of data is not a sufficient market discriminator. The Economics of Data, Analytics, and Digital Transformation aims to provide actionable insights into the real market discriminators, including an organization’s data-fueled analytics products that inspire innovation, deliver insights, help make practical decisions, generate value, and produce mission success for the enterprise. The book begins by first building your mindset to be value-driven and introducing the Big Data Business Model Maturity Index, its maturity index phases, and how to navigate the index. You will explore value engineering, where you will learn how to identify key business initiatives, stakeholders, advanced analytics, data sources, and instrumentation strategies that are essential to data science success. The book will help you accelerate and optimize your company’s operations through AI and machine learning. By the end of the book, you will have the tools and techniques to drive your organization’s digital transformation. Here are a few words from Dr. Kirk Borne, Data Scientist and Executive Advisor at Booz Allen Hamilton, about the book: "Data analytics should first and foremost be about action and value. Consequently, the great value of this book is that it seeks to be actionable. It offers a dynamic progression of purpose-driven ignition points that you can act upon."What you will learn Train your organization to transition from being data-driven to being value-driven Navigate and master the big data business model maturity index Learn a methodology for determining the economic value of your data and analytics Understand how AI and machine learning can create analytics assets that appreciate in value the more that they are used Become aware of digital transformation misconceptions and pitfalls Create empowered and dynamic teams that fuel your organization’s digital transformation Who this book is forThis book is designed to benefit everyone from students who aspire to study the economic fundamentals behind data and digital transformation to established business leaders and professionals who want to learn how to leverage data and analytics to accelerate their business careers.Table of ContentsTable of Contents The CEO Mandate: Become Value-driven, Not Data-driven Value Engineering: The Secret Sauce for Data Science Success A Review of Basic Economic Concepts University of San Francisco Economic Value of Data Research Paper The Economic Value of Data Theorems The Economics of Artificial Intelligence The Schmarzo Economic Digital Asset Valuation Theorem The 8 Laws of Digital Transformation Creating a Culture of Innovation Through Empowerment Appendix A: My Most Popular Economics of Data, Analytics, and Digital Transformation Infographics Appendix B: The Economics of Data, Analytics, and Digital Transformation Cheat Sheet

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Book SynopsisTake your first steps to becoming a fully qualified data analyst by learning how to explore complex datasetsKey Features Master each concept through practical exercises and activities Discover various statistical techniques to analyze your data Implement everything you've learned on a real-world case study to uncover valuable insights Book DescriptionEvery day, businesses operate around the clock, and a huge amount of data is generated at a rapid pace. This book helps you analyze this data and identify key patterns and behaviors that can help you and your business understand your customers at a deep, fundamental level. SQL for Data Analytics, Third Edition is a great way to get started with data analysis, showing how to effectively sort and process information from raw data, even without any prior experience. You will begin by learning how to form hypotheses and generate descriptive statistics that can provide key insights into your existing data. As you progress, you will learn how to write SQL queries to aggregate, calculate, and combine SQL data from sources outside of your current dataset. You will also discover how to work with advanced data types, like JSON. By exploring advanced techniques, such as geospatial analysis and text analysis, you will be able to understand your business at a deeper level. Finally, the book lets you in on the secret to getting information faster and more effectively by using advanced techniques like profiling and automation. By the end of this book, you will be proficient in the efficient application of SQL techniques in everyday business scenarios and looking at data with the critical eye of�analytics professional.What you will learn Use SQL to clean, prepare, and combine different datasets Aggregate basic statistics using GROUP BY clauses Perform advanced statistical calculations using a WINDOW function Import data into a database to combine with other tables Export SQL query results into various sources Analyze special data types in SQL, including geospatial, date/time, and JSON data Optimize queries and automate tasks Think about data problems and find answers using SQL Who this book is forIf you're a database engineer looking to transition into analytics or a backend engineer who wants to develop a deeper understanding of production data and gain practical SQL knowledge, you will find this book useful. This book is also ideal for data scientists or business analysts who want to improve their data analytics skills using SQL.Basic familiarity with SQL (such as basic SELECT, WHERE, and GROUP BY clauses) as well as a good understanding of linear algebra, statistics, and PostgreSQL 14 are necessary to make the most of this SQL data analytics book.Table of ContentsTable of Contents Understanding and Describing Data The Basics of SQL for Analytics SQL for Data Preparation Aggregate Functions for Data Analysis Window Functions for Data Analysis Importing and Exporting Data Analytics Using Complex Data Types Performant SQL Using SQL to Uncover the Truth – a Case Study

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Book SynopsisGet a step ahead of your competitors with insights from over 30 Kaggle Masters and Grandmasters. Discover tips, tricks, and best practices for competing effectively on Kaggle and becoming a better data scientist.Purchase of the print or Kindle book includes a free eBook in the PDF format.Key Features Learn how Kaggle works and how to make the most of competitions from over 30 expert Kagglers Sharpen your modeling skills with ensembling, feature engineering, adversarial validation and AutoML A concise collection of smart data handling techniques for modeling and parameter tuning Book DescriptionMillions of data enthusiasts from around the world compete on Kaggle, the most famous data science competition platform of them all. Participating in Kaggle competitions is a surefire way to improve your data analysis skills, network with an amazing community of data scientists, and gain valuable experience to help grow your career. The first book of its kind, The Kaggle Book assembles in one place the techniques and skills you'll need for success in competitions, data science projects, and beyond. Two Kaggle Grandmasters walk you through modeling strategies you won't easily find elsewhere, and the knowledge they've accumulated along the way. As well as Kaggle-specific tips, you'll learn more general techniques for approaching tasks based on image, tabular, textual data, and reinforcement learning. You'll design better validation schemes and work more comfortably with different evaluation metrics. Whether you want to climb the ranks of Kaggle, build some more data science skills, or improve the accuracy of your existing models, this book is for you. Plus, join our Discord Community to learn along with more than 1,000 members and meet like-minded people!What you will learn Get acquainted with Kaggle as a competition platform Make the most of Kaggle Notebooks, Datasets, and Discussion forums Create a portfolio of projects and ideas to get further in your career Design k-fold and probabilistic validation schemes Get to grips with common and never-before-seen evaluation metrics Understand binary and multi-class classification and object detection Approach NLP and time series tasks more effectively Handle simulation and optimization competitions on Kaggle Who this book is forThis book is suitable for anyone new to Kaggle, veteran users, and anyone in between. Data analysts/scientists who are trying to do better in Kaggle competitions and secure jobs with tech giants will find this book useful.A basic understanding of machine learning concepts will help you make the most of this book.Table of ContentsTable of Contents Introducing Kaggle and Other Data Science Competitions Organizing Data with Datasets Working and Learning with Kaggle Notebooks Leveraging Discussion Forums Competition Tasks and Metrics Designing Good Validation Modeling for Tabular Competitions Hyperparameter Optimization Ensembling with Blending and Stacking Solutions Modeling for Computer Vision Modeling for NLP Simulation and Optimization Competitions Creating Your Portfolio of Projects and Ideas Finding New Professional Opportunities

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Book SynopsisUse modern Python libraries such as pandas, NumPy, and scikit-learn and popular machine learning and deep learning methods to solve financial modeling problemsPurchase of the print or Kindle book includes a free eBook in the PDF formatKey Features Explore unique recipes for financial data processing and analysis with Python Apply classical and machine learning approaches to financial time series analysis Calculate various technical analysis indicators and backtest trading strategies Book DescriptionPython is one of the most popular programming languages in the financial industry, with a huge collection of accompanying libraries. In this new edition of the Python for Finance Cookbook, you will explore classical quantitative finance approaches to data modeling, such as GARCH, CAPM, factor models, as well as modern machine learning and deep learning solutions.You will use popular Python libraries that, in a few lines of code, provide the means to quickly process, analyze, and draw conclusions from financial data. In this new edition, more emphasis was put on exploratory data analysis to help you visualize and better understand financial data. While doing so, you will also learn how to use Streamlit to create elegant, interactive web applications to present the results of technical analyses.Using the recipes in this book, you will become proficient in financial data analysis, be it for personal or professional projects. You will also understand which potential issues to expect with such analyses and, more importantly, how to overcome them.What you will learn Preprocess, analyze, and visualize financial data Explore time series modeling with statistical (exponential smoothing, ARIMA) and machine learning models Uncover advanced time series forecasting algorithms such as Meta's Prophet Use Monte Carlo simulations for derivatives valuation and risk assessment Explore volatility modeling using univariate and multivariate GARCH models Investigate various approaches to asset allocation Learn how to approach ML-projects using an example of default prediction Explore modern deep learning models such as Google's TabNet, Amazon's DeepAR and NeuralProphet Who this book is forThis book is intended for financial analysts, data analysts and scientists, and Python developers with a familiarity with financial concepts. You'll learn how to correctly use advanced approaches for analysis, avoid potential pitfalls and common mistakes, and reach correct conclusions for a broad range of finance problems.Working knowledge of the Python programming language (particularly libraries such as pandas and NumPy) is necessary.Table of ContentsTable of Contents Acquiring Financial Data Data Preprocessing Visualizing Financial Time Series Exploring Financial Time Series Data Technical Analysis and Building Interactive Dashboards Time Series Analysis and Forecasting Machine Learning-Based Approaches to Time Series Forecasting Multi-Factor Models Modelling Volatility with GARCH Class Models Monte Carlo Simulations in Finance Asset Allocation Backtesting Trading Strategies Applied Machine Learning: Identifying Credit Default Advanced Concepts for Machine Learning Projects Deep Learning in Finance

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Book SynopsisAccelerate your journey to smarter decision making by mastering the fundamentals of data literacy and developing the mindset to work confidently with dataKey Features Get a solid grasp of data literacy fundamentals to support your next steps in your career Learn how to work with data and extract meaningful insights to take the right actions Apply your knowledge to real-world business intelligence projects Book DescriptionData is more than a mere commodity in our digital world. It is the ebb and flow of our modern existence. Individuals, teams, and enterprises working with data can unlock a new realm of possibilities. And the resultant agility, growth, and inevitable success have one origin—data literacy.This comprehensive guide is written by two data literacy pioneers, each with a thorough footprint within the data and analytics commercial world and lectures at top universities in the US and the Netherlands. Complete with best practices, practical models, and real-world examples, Data Literacy in Practice will help you start making your data work for you by building your understanding of data literacy basics and accelerating your journey to independently uncovering insights.You’ll learn the four-pillar model that underpins all data and analytics and explore concepts such as measuring data quality, setting up a pragmatic data management environment, choosing the right graphs for your readers, and questioning your insights.By the end of the book, you'll be equipped with a combination of skills and mindset as well as with tools and frameworks that will allow you to find insights and meaning within your data for data-informed decision making.What you will learn Start your data literacy journey with simple and actionable steps Apply the four-pillar model for organizations to transform data into insights Discover which skills you need to work confidently with data Visualize data and create compelling visual data stories Measure, improve, and leverage your data to meet organizational goals Master the process of drawing insights, ask critical questions and action your insights Discover the right steps to take when you analyze insights Who this book is forThis book is for data analysts, data professionals, and data teams starting or wanting to accelerate their data literacy journey. If you’re looking to develop the skills and mindset you need to work independently with data, as well as a solid knowledge base of the tools and frameworks, you’ll find this book useful.Table of ContentsTable of Contents The Beginning- The Flow of Data Unfolding Your Data Journey Understanding the Four-Pillar Model Implementing Organizational Data Literacy Managing Your Data Environment Aligning with Organizational Goals Designing Dashboards and Reports Questioning the Data Handling Data Responsibly Turning Insights into Decisions Defining a Data Literacy Competency Framework Assessing Your Data Literacy Maturity Managing Data and Analytics Projects Appendix A – Templates Appendix B – References

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Book SynopsisLeverage machine learning to design and back-test automated trading strategies for real-world markets using pandas, TA-Lib, scikit-learn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline, backtrader, Alphalens, and pyfolio.Purchase of the print or Kindle book includes a free eBook in the PDF format.Key Features Design, train, and evaluate machine learning algorithms that underpin automated trading strategies Create a research and strategy development process to apply predictive modeling to trading decisions Leverage NLP and deep learning to extract tradeable signals from market and alternative data Book DescriptionThe explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This revised and expanded second edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models.This book introduces end-to-end machine learning for the trading workflow, from the idea and feature engineering to model optimization, strategy design, and backtesting. It illustrates this by using examples ranging from linear models and tree-based ensembles to deep-learning techniques from cutting edge research.This edition shows how to work with market, fundamental, and alternative data, such as tick data, minute and daily bars, SEC filings, earnings call transcripts, financial news, or satellite images to generate tradeable signals. It illustrates how to engineer financial features or alpha factors that enable an ML model to predict returns from price data for US and international stocks and ETFs. It also shows how to assess the signal content of new features using Alphalens and SHAP values and includes a new appendix with over one hundred alpha factor examples.By the end, you will be proficient in translating ML model predictions into a trading strategy that operates at daily or intraday horizons, and in evaluating its performance.What you will learn Leverage market, fundamental, and alternative text and image data Research and evaluate alpha factors using statistics, Alphalens, and SHAP values Implement machine learning techniques to solve investment and trading problems Backtest and evaluate trading strategies based on machine learning using Zipline and Backtrader Optimize portfolio risk and performance analysis using pandas, NumPy, and pyfolio Create a pairs trading strategy based on cointegration for US equities and ETFs Train a gradient boosting model to predict intraday returns using AlgoSeek's high-quality trades and quotes data Who this book is forIf you are a data analyst, data scientist, Python developer, investment analyst, or portfolio manager interested in getting hands-on machine learning knowledge for trading, this book is for you. This book is for you if you want to learn how to extract value from a diverse set of data sources using machine learning to design your own systematic trading strategies.Some understanding of Python and machine learning techniques is required.Table of ContentsTable of Contents Machine Learning for Trading – From Idea to Execution Market and Fundamental Data – Sources and Techniques Alternative Data for Finance – Categories and Use Cases Financial Feature Engineering – How to Research Alpha Factors Portfolio Optimization and Performance Evaluation The Machine Learning Process Linear Models – From Risk Factors to Return Forecasts The ML4T Workflow – From Model to Strategy Backtesting (N.B. Please use the Look Inside option to see further chapters)

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Book SynopsisThis open access book presents the foundations of the Big Data research and innovation ecosystem and the associated enablers that facilitate delivering value from data for business and society. It provides insights into the key elements for research and innovation, technical architectures, business models, skills, and best practices to support the creation of data-driven solutions and organizations. The book is a compilation of selected high-quality chapters covering best practices, technologies, experiences, and practical recommendations on research and innovation for big data. The contributions are grouped into four parts: · Part I: Ecosystem Elements of Big Data Value focuses on establishing the big data value ecosystem using a holistic approach to make it attractive and valuable to all stakeholders. · Part II: Research and Innovation Elements of Big Data Value details the key technical and capability challenges to be addressed for delivering big data value. · Part III: Business, Policy, and Societal Elements of Big Data Value investigates the need to make more efficient use of big data and understanding that data is an asset that has significant potential for the economy and society. · Part IV: Emerging Elements of Big Data Value explores the critical elements to maximizing the future potential of big data value. Overall, readers are provided with insights which can support them in creating data-driven solutions, organizations, and productive data ecosystems. The material represents the results of a collective effort undertaken by the European data community as part of the Big Data Value Public-Private Partnership (PPP) between the European Commission and the Big Data Value Association (BDVA) to boost data-driven digital transformation. Table of ContentsPart I: Ecosystem Elements of Big Data Value.- The European Big Data Value Ecosystem.- Stakeholder Analysis of Data Ecosystems.- A Roadmap to Drive Adoption of Data Ecosystems.- Achievements and Impact of the Big Data Value Public-Private Partnership: The Story so Far.- Part II: Research and Innovation Elements of Big Data Value.- Technical Research Priorities for Big Data.- A Reference Model for Big Data Technologies.- Data Protection in the Era of Artificial Intelligence: Trends, Existing Solutions and Recommendations for Privacy-Preserving Technologies.- A Best Practice Framework for Centres of Excellence in Big Data and Artificial Intelligence.- Data Innovation Spaces.- Part III: Business, Policy, and Societal Elements of Big Data Value.- Big Data Value Creation by Example.- Business Models and Ecosystem for Big Data.- Innovation in Times of Big Data and AI: Introducing the Data-Driven Innovation (DDI) Framework.- The Road to Big Data Standardisation.- The Role of Data Regulation in Shaping AI: An Overview of Challenges and Recommendations for SMEs.- Part IV: Emerging Elements of Big Data Value.- Data Economy 2.0: From Big Data Value to AI Value and a European Data Space.

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Book SynopsisDas Buch Arithmetik ist eine Übersetzung des vierten Kapitels der legendären Werkreihe "The Art of Computer Programming" von Donald E. Knuth in der neuesten Fassung. Es handelt sich um eine umfangreiche Einführung in die Computeralgebra, die den neuesten Stand der Forschung berücksichtigt. Donald E. Knuth versteht es, die Algorithmen didaktisch sehr geschickt und ohne Kompromisse bei der Strenge aufzubereiten. Das Buch enthält außerdem Hunderte von Aufgaben verschiedener Schwierigkeitsgrade mit Lösungen. Der Übersetzer, Prof. Dr. R. Loos, lehrt an der Universität Tübingen.Table of Contents4 — Arithmetik.- 4.1. Stellenwertsysteme.- 4.2. Gleitkomma-Aritlunetik.- 4.2.1. Einfachgenaue Rechnungen.- 4.2.2. Genauigkeit der Gleitkonuna-Arithmetik.- *4.2.3. Doppeltgenaue Rechnungen.- 4.2.4. Verteilung von Gleitkomrnazahlen.- 4.3. Mehrfachgenaue Aritlunetik.- 4.3.1. Die klassischen Algorithmen.- *4.3.2. Modulare Aritlnnetik.- *4.3.3. Wie schnell könn en wir multiplizieren?.- 4.4. Basiswechsel.- 4.5. Rationale Arithmetik.- 4.5.1. Brüche.- 4.5.2. Der größte gemeinsame Teiler.- *4.5.3. Analyse des euklidschen Algorithmus.- 4.5.4. Zerlegung in Prirnfaktoren.- 4.6. Polynornarithmetik.- 4.6.1. Division von Polynomen.- *4.6.2. Faktorisierung von Polynomen.- 4.6.3. Auswertung von Potenzen.- 4.6.4. Auswertung von Polynomen.- *4.7. Operationen an Potenzreihen.- Lösungen zu den Übungsaufgaben.- Anhang A — Tafeln numerischer Größen.- 1. Fundamentale Konstanten (dezimal).- 2. Fundamentale Konstanten (oktal).- 3. Harrnonische Zahlen , Bernoulli-Zahlen, Fibonacci-Zahlen.- Anhang B — Index der Bezeichnungen.- Index und Glossar.

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