Search results for "data analytics for finance using python"
Items we need to order for you
7 Financial Models for Analysts, Investors and Finance Professionals
Financial models in Excel allow investment analysts and other finance professionals to take the laborious number crunching out of financial analysis and forecasting. Models help them to gain meaningful insights into the way that a business is working and focus attention on areas to improve bottom-line results. They can also be used as powerful tools to test the potential impact of various risks on business performance. In this brand new guide, financial modelling expert Paul Lower presents step-by-step instructions for seven spreadsheet models that will help the user to gain a better understanding of the financial data coming out of a business. These seven models can be used to:1. Assess how a business is performing on key financial indicators.2. Produce sales and cost forecasts.3. Create a cash flow forecast.4. Understand the impact of product price changes on profitability.5. Assess potential investment decisions.6. Check the sensitivity of key financial measures to risk events.7. Produce a business valuation.The book also includes downloadable spreadsheets of the author’s original Excel models and introductory chapters about best practice when modelling in Excel. With this suite of seven tools, a financial analyst will be equipped to use Excel to achieve a deep understanding of a business and its financial data.
R 1,482.00
Big Data Analytics
Big Data Analytics: Applications in Business and Marketing explores the concepts and applications related to marketing and business as well as future research directions. It also examines how this emerging field could be extended to performance management and decision-making. Investment in business and marketing analytics can create value through proper allocation of resources and resource orchestration process. The use of data analytics tools can be used to diagnose and improve performance. The book is divided into five parts. The first part introduces data science, big data, and data analytics. The second part focuses on applications of business analytics including:Big data analytics and algorithmMarket basket analysisAnticipating consumer purchase behaviorVariation in shopping patternsBig data analytics for market intelligenceThe third part looks at business intelligence and features an evaluation study of churn prediction models for business Intelligence. The fourth part of the book examines analytics for marketing decision-making and the roles of big data analytics for market intelligence and of consumer behavior. The book concludes with digital marketing, marketing by consumer analytics, web analytics for digital marketing, and smart retailing. This book covers the concepts, applications and research trends of marketing and business analytics with the aim of helping organizations increase profitability by improving decision-making through data analytics.
R 5,640.81
R 650.30
Data Analytics
This book constitutes the refereed conference proceedings of the 31st British International Conference on Databases, BICOD 2017 - formerly known as BNCOD (British National Conference on Databases) - held in London, UK, in July 2017.The 17 revised full papers were carefully reviewed and selected from numerous submissions. The papers cover a wide range of topics such as data cleansing, data integration, data wrangling, data mining and knowledge discovery, graph data and knowledge graphs, intelligent data analysis, approximate and flexible querying, data provenance and ontology-based data access. They are organized in the following topical sections: data wrangling and data integration; data analysis and data mining; graph data querying and analysis; multidimensional data and data quality; and distributed and multimedia data management.
R 2,155.68
Data Analytics Applications in Emerging Markets
This book analyzes the impact of technology in emerging markets by considering conditions and the history of how it has changed the way of working and market development in such contexts. The book delves into key areas such as fintech enterprises, artificial intelligence, pension funds, stock markets, and energy markets though applied studies and research. This book is a useful read for practitioners and scholars interested in how technology has and continues to change the way in which development is defined and achieved, particularly in emerging markets.
R 5,366.98
Python for Data Mining Quick Syntax Reference
Learn how to use Python and its structures, how to install Python, and which tools are best suited for data analyst work. This book provides you with a handy reference and tutorial on topics ranging from basic Python concepts through to data mining, manipulating and importing datasets, and data analysis.Python for Data Mining Quick Syntax Reference covers each concept concisely, with many illustrative examples. You'll be introduced to several data mining packages, with examples of how to use each of them. The first part covers core Python including objects, lists, functions, modules, and error handling. The second part covers Python's most important data mining packages: NumPy and SciPy for mathematical functions and random data generation, pandas for dataframe management and data import, Matplotlib for drawing charts, and scikitlearn for machine learning. What You'll LearnInstall Python and choose a development environmentUnderstand the basic concepts of object-oriented programmingImport, open, and edit filesReview the differences between Python 2.x and 3.xWho This Book Is ForProgrammers new to Python's data mining packages or with experience in other languages, who want a quick guide to Pythonic tools and techniques.
R 752.00
Python Data Science Essentials
Gain useful insights from your data using popular data science toolsKey FeaturesA one-stop guide to Python libraries such as pandas and NumPyComprehensive coverage of data science operations such as data cleaning and data manipulationChoose scalable learning algorithms for your data science tasksBook DescriptionFully expanded and upgraded, the latest edition of Python Data Science Essentials will help you succeed in data science operations using the most common Python libraries. This book offers up-to-date insight into the core of Python, including the latest versions of the Jupyter Notebook, NumPy, pandas, and scikit-learn.The book covers detailed examples and large hybrid datasets to help you grasp essential statistical techniques for data collection, data munging and analysis, visualization, and reporting activities. You will also gain an understanding of advanced data science topics such as machine learning algorithms, distributed computing, tuning predictive models, and natural language processing. Furthermore, You’ll also be introduced to deep learning and gradient boosting solutions such as XGBoost, LightGBM, and CatBoost.By the end of the book, you will have gained a complete overview of the principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business usersWhat you will learnSet up your data science toolbox on Windows, Mac, and LinuxUse the core machine learning methods offered by the scikit-learn libraryManipulate, fix, and explore data to solve data science problemsLearn advanced explorative and manipulative techniques to solve data operationsOptimize your machine learning models for optimized performanceExplore and cluster graphs, taking advantage of interconnections and links in your dataWho this book is forIf you’re a data science entrant, data analyst, or data engineer, this book will help you get ready to tackle real-world data science problems without wasting any time. Basic knowledge of probability/statistics and Python coding experience will assist you in understanding the concepts covered in this book.
R 1,661.00
Pricing Analytics
This updated and expanded second edition has the same theme as the first: the price, the number, someone puts on a product to help consumers decide to buy that product, comes from statistically modeling data.Five new chapters provide a wider perspective on pricing analytics to more effectively develop elasticities and pricing strategies. This book gives readers the statistical modeling tools needed to get the number to put on a product, based on economic and statistical principles and theory. It covers elasticities, methodologies for analyzing customer choices including conjoint analysis, pricing segmentation, big data and econometric models—now with improved explanations and developments. The second edition adds discussion of three important and advanced topics: simulations for testing strategies under different conditions such as scenario analysis, AI applications for elasticity estimation and dynamic pricing, and the impacts of tariffs.A comprehensive and essential resource for analysts—economists, statisticians, or market researchers—who must estimate the optimal price for a product or service, this book is also a helpful guide for upper-level students in analytics disciplines.
R 8,572.00
Data and Analytics Strategy for Business
Struggling to convert your data, analytics and AI strategy into business value? Data and Analytics Strategy for Business by Simon Asplen-Taylor is an essential guide for CDOs, CAIOs, CIOs and CTOs seeking to deliver measurable results from data investments. Focused on aligning data, analytics and AI with strategic goals, this book helps leaders move from initial implementation to transformation. Fully updated with the latest AI developments, it provides practical tools and real-world insights to accelerate implementation, drive adoption and optimize performance across the organization. You'll learn how to: - Align data, analytics and AI to strategic business outcomes - Build trust through data quality, governance and transparency - Integrate AI and ML Secure executive and organizational buy-in for long-term success - Learn from real-world examples including Tesco and Facebook With guidance grounded in real results, Data and Analytics Strategy for Business helps senior leaders lead smarter, execute faster and unlock enterprise-wide impact. Themes include: data strategy, AI adoption, analytics leadership, digital transformation, data governance, performance optimization
R 1,626.30
Applying Analytics
Newcomers to quantitative analysis need practical guidance on how to analyze data in the real world yet most introductory books focus on lengthy derivations and justifications instead of practical techniques. Covering the technical and professional skills needed by analysts in the academic, private, and public sectors, Applying Analytics: A Practical Introduction systematically teaches novices how to apply algorithms to real data and how to recognize potential pitfalls. It offers one of the first textbooks for the emerging first course in analytics.The text concentrates on the interpretation, strengths, and weaknesses of analytical techniques, along with challenges encountered by analysts in their daily work. The author shares various lessons learned from applying analytics in the real world. He supplements the technical material with coverage of professional skills traditionally learned through experience, such as project management, analytic communication, and using analysis to inform decisions. Example data sets used in the text are available for download online so that readers can test their own analytic routines. Suitable for beginning analysts in the sciences, business, engineering, and government, this book provides an accessible, example-driven introduction to the emerging field of analytics. It shows how to interpret data and identify trends across a range of fields.
R 11,135.88
Analytics: Data Science, Data Analysis and Predictive Analytics for Business
SO MANY PEOPLE DREAM OF BECOMING THEIR OWN BOSS OR SUCCEEDING IN THEIR CHOSEN PROFESSION, AND WITH THE RESOURCES AVAILABLE TODAY, MORE ENTREPRENEURS AND PROFESSIONALS ARE ACHIEVING GREAT SUCCESS! HOWEVER, SUCCESS SHOULD BE DEFINED FOR THE LONG TERM, AND AS OPPORTUNITIES START TO GROW, SO DOES THE COMPETITION. Getting your business up and running or starting on your career path is one thing, but have a sustainable business or career is completely another. Many people make the mistake of making plans but having no follow-through. This is where analytics comes in. Don't you wish to have the power to know what your target consumers are thinking? Won't you want to have a preview of what future trends to expect in the market you are in? Well, this book is just the one you need. This book will teach you, in simple and easy-to-understand terms, how to take advantage of data from your daily operations and make such data a powerful tool that can influence how well your business does over time.
R 1,985.51
Programming with Python for Social Scientists
As data become ′big′, fast and complex, the software and computing tools needed to manage and analyse them are rapidly developing. Social scientists need new tools to meet these challenges, tackle big datasets, while also developing a more nuanced understanding of - and control over - how these computing tools and algorithms are implemented. Programming with Python for Social Scientists offers a vital foundation to one of the most popular programming tools in computer science, specifically for social science researchers, assuming no prior coding knowledge. It guides you through the full research process, from question to publication, including:the fundamentals of why and how to do your own programming in social scientific research,questions of ethics and research design,a clear, easy to follow ′how-to′ guide to using Python, with a wide array of applications such as data visualisation, social media data research, social network analysis, and more.Accompanied by numerous code examples, screenshots, sample data sources, this is the textbook for social scientists looking for a complete introduction to programming with Python and incorporating it into their research design and analysis.
R 5,706.92
Handbook of Empirical Economics and Finance
Handbook of Empirical Economics and Finance explores the latest developments in the analysis and modeling of economic and financial data. Well-recognized econometric experts discuss the rapidly growing research in economics and finance and offer insight on the future direction of these fields.Focusing on micro models, the first group of chapters describes the statistical issues involved in the analysis of econometric models with cross-sectional data often arising in microeconomics. The book then illustrates time series models that are extensively used in empirical macroeconomics and finance. The last set of chapters explores the types of panel data and spatial models that are becoming increasingly significant in analyzing complex economic behavior and policy evaluations.This handbook brings together both background material and new methodological and applied results that are extremely important to the current and future frontiers in empirical economics and finance. It emphasizes inferential issues that transpire in the analysis of cross-sectional, time series, and panel data-based empirical models in economics, finance, and related disciplines.
R 4,498.23
Luminescence Signal Analysis Using Python
This book compiles and presents a complete package of open-access Python software code for luminescence signal analysis in the areas of radiation dosimetry, luminescence dosimetry, and luminescence dating. Featuring more than 90 detailed worked examples of Python code, fully integrated into the text, 16 chapters summarize the theory and equations behind the subject matter, while presenting the practical Python codes used to analyze experimental data and extract the various parameters that mathematically describe the luminescence signals. Several examples are provided of how researchers can use and modify the available codes for different practical situations. Types of luminescence signals analyzed in the book are thermoluminescence (TL), isothermal luminescence (ITL), optically stimulated luminescence (OSL), infrared stimulated luminescence (IRSL), timeresolved luminescence (TR) and dose response of dosimetric materials. The open-access Python codes are available at GitHub.The book is well suited to the broader scientific audience using the tools of luminescence dosimetry: physicists, geologists, archaeologists, solid-state physicists, medical physicists, and all scientists using luminescence dosimetry in their research. The detailed code provided allows both students and researchers to be trained quickly and efficiently on the practical aspects of their work, while also providing an overview of the theory behind the analytical equations.
R 7,848.20
Python for ArcGIS Pro
Extend your ArcGIS expertise by unlocking the world of Python programming. A fully hands-on guide that takes you through exercise after exercise using real data and real problems. NOTE: This book is compatible with ArcGIS Pro 2.9.Key FeaturesLearn the core components of the two Python modules for ArcGIS: ArcPy and ArcGIS API for PythonUse ArcPy, pandas, NumPy, and ArcGIS in ArcGIS Pro Notebooks to manage and analyze geospatial data at scaleIntegrate with ArcGIS Online using Python to publish and manage dataBook DescriptionIntegrating Python into your day-to-day ArcGIS work is highly recommended when dealing with large amounts of geospatial data. Python for ArcGIS Pro aims to help you get your work done faster, with greater repeatability and higher confidence in your results. Starting from programming basics and building in complexity, two experienced ArcGIS professionals-turned-Python programmers teach you how to incorporate scripting at each step: automating the production of maps for print, managing data between ArcGIS Pro and ArcGIS Online, creating custom script tools for sharing, and then running data analysis and visualization on top of the ArcGIS geospatial library, all using Python. You'll use ArcGIS Pro Notebooks to explore and analyze geospatial data, and write data engineering scripts to manage ongoing data processing and data transfers. This exercise-based book also includes three rich real-world case studies, giving you an opportunity to apply and extend the concepts you studied earlier. Irrespective of your expertise level with Esri software or the Python language, you'll benefit from this book's hands-on approach, which takes you through the major uses of Python for ArcGIS Pro to boost your ArcGIS productivity.What you will learnAutomate map production to make and edit maps at scale, cutting down on repetitive tasksPublish map layer data to ArcGIS OnlineAutomate data updates using the ArcPy Data Access module and cursorsTurn your scripts into script tools for ArcGIS ProLearn how to manage data on ArcGIS OnlineQuery, edit, and append to feature layers and create symbology with renderers and colorizersApply pandas and NumPy to raster and vector analysisLearn new tricks to manage data for entire cities or large companiesWho this book is forThis book is ideal for anyone looking to add Python to their ArcGIS Pro workflows, even if you have no prior experience with programming. This includes ArcGIS professionals, intermediate ArcGIS Pro users, ArcGIS Pro power users, students, and people who want to move from being a GIS Technician to GIS Analyst; GIS Analyst to GIS Programmer; or GIS Developer/Programmer to a GIS Architect. Basic familiarity with geospatial/GIS syntax, ArcGIS, and data science (pandas) is helpful, though not necessary.
R 2,247.00
High Performance Python
Your Python code may run correctly, but what if you need it to run faster? This practical book shows you how to locate performance bottlenecks and significantly speed up your code in high-data-volume programs. By explaining the fundamental theory behind design choices, this expanded edition of High Performance Python helps experienced Python programmers gain a deeper understanding of Python's implementation.How do you take advantage of multicore architectures or compilation? Or build a system that scales up beyond RAM limits or with a GPU? Authors Micha Gorelick and Ian Ozsvald reveal concrete solutions to many issues and include war stories from companies that use high-performance Python for GenAI data extraction, productionized machine learning, and more.Get a better grasp of NumPy, Cython, and profilersLearn how Python abstracts the underlying computer architectureUse profiling to find bottlenecks in CPU time and memory usageWrite efficient programs by choosing appropriate data structuresSpeed up matrix and vector computationsProcess DataFrames quickly with Pandas, Dask, and PolarsSpeed up your neural networks and GPU computationsUse tools to compile Python down to machine codeManage multiple I/O and computational operations concurrentlyConvert multiprocessing code to run on local or remote clusters
R 1,505.00
R 168.00
Handbook of Graphs and Networks in People Analytics
Handbook of Graphs and Networks in People Analytics: With Examples in R and Python covers the theory and practical implementation of graph methods in R and Python for the analysis of people and organizational networks. Starting with an overview of the origins of graph theory and its current applications in the social sciences, the book proceeds to give in-depth technical instruction on how to construct and store graphs from data, how to visualize those graphs compellingly and how to convert common data structures into graph-friendly form.The book explores critical elements of network analysis in detail, including the measurement of distance and centrality, the detection of communities and cliques, and the analysis of assortativity and similarity. An extension chapter offers an introduction to graph database technologies. Real data sets from various research contexts are used for both instruction and for end of chapter practice exercises and a final chapter contains data sets and exercises ideal for larger personal or group projects of varying difficulty level.Key features:Immediately implementable code, with extensive and varied illustrations of graph variants and layoutsExamples and exercises across a variety of real-life contexts including business, politics, education, social media and crime investigationDedicated chapter on graph visualization methodsPractical walkthroughs of common methodological uses: finding influential actors in groups, discovering hidden community structures, facilitating diverse interaction in organizations, detecting political alignment, determining what influences connection and attachmentVarious downloadable data sets for use both in class and individual learning projectsFinal chapter dedicated to individual or group project examples
R 3,920.00
R 640.00
R 611.00
Practical Data Science with Python 3
Gain insight into essential data science skills in a holistic manner using data engineering and associated scalable computational methods. This book covers the most popular Python 3 frameworks for both local and distributed (in premise and cloud based) processing. Along the way, you will be introduced to many popular open-source frameworks, like, SciPy, scikitlearn, Numba, Apache Spark, etc. The book is structured around examples, so you will grasp core concepts via case studies and Python 3 code.As data science projects gets continuously larger and more complex, software engineering knowledge and experience is crucial to produce evolvable solutions. You'll see how to create maintainable software for data science and how to document data engineering practices.This book is a good starting point for people who want to gain practical skills to perform data science. All the code will be available in the form of IPython notebooks and Python 3 programs, which allow you to reproduce all analyses from the book and customize them for your own purpose. You'll also benefit from advanced topics like Machine Learning, Recommender Systems, and Security in Data Science.Practical Data Science with Python will empower you analyze data, formulate proper questions, and produce actionable insights, three core stages in most data science endeavors.What You'll LearnPlay the role of a data scientist when completing increasingly challenging exercises using Python 3Work work with proven data science techniques/technologies Review scalable software engineering practices to ramp up data analysis abilities in the realm of Big DataApply theory of probability, statistical inference, and algebra to understand the data science practicesWho This Book Is ForAnyone who would like to embark into the realm of data science using Python 3.
R 1,450.76
Recent Trends and Future Direction for Data Analytics
In an increasingly data-centric world, scholars and practitioners grapple with the complexities of harnessing data analytics effectively across various industries. The challenge lies in navigating the rapid evolution of methodologies, identifying emerging trends, and understanding the nuanced applications of data analytics in real-world scenarios. This gap between theory and practice inhibits academic progress. It hampers industry innovation, leaving stakeholders needing help to leverage data to its full potential. Recent Trends and Future Direction for Data Analytics presents a compelling solution. By delving into real-world case studies spanning supply chain management, marketing, healthcare, and finance, this book bridges the gap between theory and practice, offering invaluable insights into the practical applications of data analytics. A systematic exploration of fundamental concepts, advanced techniques, and specialized topics equips scholars, researchers, and industry professionals with the knowledge and tools needed to navigate the complexities of data analytics with confidence. This book is designed as a comprehensive resource for academia and industry alike. It serves as a roadmap for understanding the current state of data analytics and anticipating its future trajectories. By offering a blend of theoretical foundations and practical applications, it empowers readers to unlock the full potential of data analytics, driving innovation, informed decision-making, and transformative change across diverse sectors.
R 11,039.06
Big Data Analytics
The proposed book will discuss various aspects of big data Analytics. It will deliberate upon the tools, technology, applications, use cases and research directions in the field. Chapters would be contributed by researchers, scientist and practitioners from various reputed universities and organizations for the benefit of readers.
R 9,384.57
In-Memory Analytics with Apache Arrow
Harness the power of Apache Arrow to optimize tabular data processing and develop robust, high-performance data systems with its standardized, language-independent columnar memory formatKey FeaturesExplore Apache Arrow's data types and integration with pandas, Polars, and ParquetWork with Arrow libraries such as Flight SQL, Acero compute engine, and Dataset APIs for tabular dataEnhance and accelerate machine learning data pipelines using Apache Arrow and its subprojectsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionApache Arrow is an open source, columnar in-memory data format designed for efficient data processing and analytics. This book harnesses the author’s 15 years of experience to show you a standardized way to work with tabular data across various programming languages and environments, enabling high-performance data processing and exchange.This updated second edition gives you an overview of the Arrow format, highlighting its versatility and benefits through real-world use cases. It guides you through enhancing data science workflows, optimizing performance with Apache Parquet and Spark, and ensuring seamless data translation. You’ll explore data interchange and storage formats, and Arrow's relationships with Parquet, Protocol Buffers, FlatBuffers, JSON, and CSV. You’ll also discover Apache Arrow subprojects, including Flight, SQL, Database Connectivity, and nanoarrow. You’ll learn to streamline machine learning workflows, use Arrow Dataset APIs, and integrate with popular analytical data systems such as Snowflake, Dremio, and DuckDB. The latter chapters provide real-world examples and case studies of products powered by Apache Arrow, providing practical insights into its applications.By the end of this book, you’ll have all the building blocks to create efficient and powerful analytical services and utilities with Apache Arrow.What you will learnUse Apache Arrow libraries to access data files, both locally and in the cloudUnderstand the zero-copy elements of the Apache Arrow formatImprove the read performance of data pipelines by memory-mapping Arrow filesProduce and consume Apache Arrow data efficiently by sharing memory with the C APILeverage the Arrow compute engine, Acero, to perform complex operationsCreate Arrow Flight servers and clients for transferring data quicklyBuild the Arrow libraries locally and contribute to the communityWho this book is forThis book is for developers, data engineers, and data scientists looking to explore the capabilities of Apache Arrow from the ground up. Whether you’re building utilities for data analytics and query engines, or building full pipelines with tabular data, this book can help you out regardless of your preferred programming language. A basic understanding of data analysis concepts is needed, but not necessary. Code examples are provided using C++, Python, and Go throughout the book.
R 1,693.00
R 305.00
Transforming Healthcare Analytics
Real-life examples of how to apply intelligence in the healthcare industry through innovative analytics Healthcare analytics offers intelligence for making better healthcare decisions. Identifying patterns and correlations contained in complex health data, analytics has applications in hospital management, patient records, diagnosis, operating and treatment costs, and more. Helping healthcare managers operate more efficiently and effectively. Transforming Healthcare Analytics: The Quest for Healthy Intelligence shares real-world use cases of a healthcare company that leverages people, process, and advanced analytics technology to deliver exemplary results. This book illustrates how healthcare professionals can transform the healthcare industry through analytics. Practical examples of modern techniques and technology show how unified analytics with data management can deliver insight-driven decisions. The authors—a data management and analytics specialist and a healthcare finance executive—share their unique perspectives on modernizing data and analytics platforms to alleviate the complexity of the healthcare, distributing capabilities and analytics to key stakeholders, equipping healthcare organizations with intelligence to prepare for the future, and more. This book: Explores innovative technologies to overcome data complexity in healthcareHighlights how analytics can help with healthcare market analysis to gain competitive advantageProvides strategies for building a strong foundation for healthcare intelligenceExamines managing data and analytics from end-to-end, from diagnosis, to treatment, to provider paymentDiscusses the future of technology and focus areas in the healthcare industry Transforming Healthcare Analytics: The Quest for Healthy Intelligence is an important source of information for CFO’s, CIO, CTO, healthcare managers, data scientists, statisticians, and financial analysts at healthcare institutions.
R 1,139.00
R 305.00
Handbook of High-Frequency Trading and Modeling in Finance
Reflecting the fast pace and ever-evolving nature of the financial industry, the Handbook of High-Frequency Trading and Modeling in Finance details how high-frequency analysis presents new systematic approaches to implementing quantitative activities with high-frequency financial data. Introducing new and established mathematical foundations necessary to analyze realistic market models and scenarios, the handbook begins with a presentation of the dynamics and complexity of futures and derivatives markets as well as a portfolio optimization problem using quantum computers. Subsequently, the handbook addresses estimating complex model parameters using high-frequency data. Finally, the handbook focuses on the links between models used in financial markets and models used in other research areas such as geophysics, fossil records, and earthquake studies. The Handbook of High-Frequency Trading and Modeling in Finance also features: • Contributions by well-known experts within the academic, industrial, and regulatory fields • A well-structured outline on the various data analysis methodologies used to identify new trading opportunities • Newly emerging quantitative tools that address growing concerns relating to high-frequency data such as stochastic volatility and volatility tracking; stochastic jump processes for limit-order books and broader market indicators; and options markets • Practical applications using real-world data to help readers better understand the presented material The Handbook of High-Frequency Trading and Modeling in Finance is an excellent reference for professionals in the fields of business, applied statistics, econometrics, and financial engineering. The handbook is also a good supplement for graduate and MBA-level courses on quantitative finance, volatility, and financial econometrics. Ionut Florescu, PhD, is Research Associate Professor in Financial Engineering and Director of the Hanlon Financial Systems Laboratory at Stevens Institute of Technology. His research interests include stochastic volatility, stochastic partial differential equations, Monte Carlo Methods, and numerical methods for stochastic processes. Dr. Florescu is the author of Probability and Stochastic Processes, the coauthor of Handbook of Probability, and the coeditor of Handbook of Modeling High-Frequency Data in Finance, all published by Wiley. Maria C. Mariani, PhD, is Shigeko K. Chan Distinguished Professor in Mathematical Sciences and Chair of the Department of Mathematical Sciences at The University of Texas at El Paso. Her research interests include mathematical finance, applied mathematics, geophysics, nonlinear and stochastic partial differential equations and numerical methods. Dr. Mariani is the coeditor of Handbook of Modeling High-Frequency Data in Finance, also published by Wiley. H. Eugene Stanley, PhD, is William Fairfield Warren Distinguished Professor at Boston University. Stanley is one of the key founders of the new interdisciplinary f
R 5,469.00
Big Data Analytics
Big Data Analytics: Digital Marketing and Decision-Making covers the advances related to marketing and business analytics. Investment marketing analytics can create value through proper allocation of resources and resource orchestration processes. The use of data analytics tools can be used to improve and speed decision-making processes.Chapters examining analytics for decision-making cover such topics as: Big data analytics for gathering business intelligenceData analytics and consumer behaviorThe role of big data analytics in organizational decision-makingThis book also looks at digital marketing and focuses on such areas as: The prediction of marketing by consumer analyticsWeb analytics for digital marketingSmart retailingLeveraging web analytics for optimizing digital marketing strategiesBig Data Analytics: Digital Marketing and Decision-Making aims to help organizations increase their profits by making better decisions on time through the use of data analytics. It is written for students, practitioners, industry professionals, researchers, and faculty working in the field of commerce and marketing, big data analytics, and organizational decision-making.
R 4,453.70
Digital Economy, Business Analytics, and Big Data Analytics Applications
This book is about turning data into smart decisions, knowledge into wisdom and business into business intelligence and insight. It explores diverse paradigms, methodologies, models, tools and techniques of the emerging knowledge domain of digitalized business analytics applications.The book covers almost every crucial aspect of applied artificial intelligence in business, smart mobile and digital services in business administration, marketing, accounting, logistics, finance and IT management.This book aids researchers, practitioners and decisions makers to gain enough knowledge and insight on how to effectively leverage data into competitive intelligence.
R 7,667.14
AI-Powered Cybersecurity for Banking and Finance
In the digital finance world, cybersecurity and cloud tools can be used to prevent attacks and secure sensitive information such as the data of business transactions, internal payments, interbank payments, bank accounts, loan and deposit accounts, stocks, and other financial transactions.The book explores the importance of artificial intelligence (AI)-based predictive cybersecurity tools in the financial industry and their role in protecting financial data. As AI continues to revolutionize the landscape of global transactions, it also brings forth new challenges, including sophisticated theft activities. This book addresses the problem of enhancing security and prevention of attacks through the application of AI-powered cybersecurity tools.This book contributes to a deeper understanding of the importance of virus detection tools, unauthorized access detection tools, cloud services, and real-time predictive analytics in the finance industry as well as their pivotal role in cybersecurity for preventing data theft. The book provides valuable insights for cybersecurity and cloud services in the financial services industry, aiding administrators, engineers, and managers in fortifying the security and resilience of financial data systems in the face of evolving challenges of financial data theft.This book is not merely a compilation of technical knowledge – through its engaging and thought-provoking content, the book leaves an indelible impression, urging readers to embrace the transformative power of AI-based technologies and embark on a collective mission to unlock the full potential of cybersecurity as well as cloud services for the improved safety of the global financial ecosystem.
R 3,564.26
Applied Data Analytics
The emergence of huge amounts of data which require analysis and in some cases real-time processing has forced exploration into fast algorithms for handling very lage data sizes. Analysis of x-ray images in medical applications, cyber security data, crime data, telecommunications and stock market data, health records and business analytics data are but a few areas of interest. Applications and platforms including R, RapidMiner and Weka provide the basis for analysis, often used by practitioners who pay little to no attention to the underlying mathematics and processes impacting the data. This often leads to an inability to explain results or correct mistakes, or to spot errors. Applied Data Analytics - Principles and Applications seeks to bridge this missing gap by providing some of the most sought after techniques in big data analytics. Establishing strong foundations in these topics provides practical ease when big data analyses are undertaken using the widely available open source and commercially orientated computation platforms, languages and visualisation systems. The book, when combined with such platforms, provides a complete set of tools required to handle big data and can lead to fast implementations and applications. The book contains a mixture of machine learning foundations, deep learning, artificial intelligence, statistics and evolutionary learning mathematics written from the usage point of view with rich explanations on what the concepts mean. The author has thus avoided the complexities often associated with these concepts when found in research papers. The tutorial nature of the book and the applications provided are some of the reasons why the book is suitable for undergraduate, postgraduate and big data analytics enthusiasts. This text should ease the fear of mathematics often associated with practical data analytics and support rapid applications in artificial intelligence, environmental sensor data modelling and analysis, health informatics, business data analytics, data from Internet of Things and deep learning applications.
R 4,763.00
R 916.00
Handbook of Regression Modeling in People Analytics
Despite the recent rapid growth in machine learning and predictive analytics, many of the statistical questions that are faced by researchers and practitioners still involve explaining why something is happening. Regression analysis is the best ‘swiss army knife’ we have for answering these kinds of questions.This book is a learning resource on inferential statistics and regression analysis. It teaches how to do a wide range of statistical analyses in both R and in Python, ranging from simple hypothesis testing to advanced multivariate modelling. Although it is primarily focused on examples related to the analysis of people and talent, the methods easily transfer to any discipline. The book hits a ‘sweet spot’ where there is just enough mathematical theory to support a strong understanding of the methods, but with a step-by-step guide and easily reproducible examples and code, so that the methods can be put into practice immediately. This makes the book accessible to a wide readership, from public and private sector analysts and practitioners to students and researchers.Key Features:16 accompanying datasets across a wide range of contexts (e.g. academic, corporate, sports, marketing)Clear step-by-step instructions on executing the analysesClear guidance on how to interpret resultsPrimary instruction in R but added sections for Python codersDiscussion exercises and data exercises for each of the main chaptersFinal chapter of practice material and datasets ideal for class homework or project work.
R 4,066.00
R In Finance And Economics: A Beginner's Guide
This book provides an introduction to the statistical software R and its application with an empirical approach in finance and economics. It is specifically targeted towards undergraduate and graduate students. It provides beginner-level introduction to R using RStudio and reproducible research examples. It will enable students to use R for data cleaning, data visualization and quantitative model building using statistical methods like linear regression, econometrics (GARCH etc), Copulas, etc. Moreover, the book demonstrates latest research methods with applications featuring linear regression, quantile regression, panel regression, econometrics, dependence modelling, etc. using a range of data sets and examples.
R 2,205.58
Handbook of Graphs and Networks in People Analytics
Handbook of Graphs and Networks in People Analytics: With Examples in R and Python covers the theory and practical implementation of graph methods in R and Python for the analysis of people and organizational networks. Starting with an overview of the origins of graph theory and its current applications in the social sciences, the book proceeds to give in-depth technical instruction on how to construct and store graphs from data, how to visualize those graphs compellingly and how to convert common data structures into graph-friendly form.The book explores critical elements of network analysis in detail, including the measurement of distance and centrality, the detection of communities and cliques, and the analysis of assortativity and similarity. An extension chapter offers an introduction to graph database technologies. Real data sets from various research contexts are used for both instruction and for end of chapter practice exercises and a final chapter contains data sets and exercises ideal for larger personal or group projects of varying difficulty level.Key features:Immediately implementable code, with extensive and varied illustrations of graph variants and layoutsExamples and exercises across a variety of real-life contexts including business, politics, education, social media and crime investigationDedicated chapter on graph visualization methodsPractical walkthroughs of common methodological uses: finding influential actors in groups, discovering hidden community structures, facilitating diverse interaction in organizations, detecting political alignment, determining what influences connection and attachmentVarious downloadable data sets for use both in class and individual learning projectsFinal chapter dedicated to individual or group project examples
R 11,704.73
Python and R for the Modern Data Scientist
Success in data science depends on the flexible and appropriate use of tools. That includes Python and R, two of the foundational programming languages in the field. With this book, data scientists from the Python and R communities will learn how to speak the dialects of each language. By recognizing the strengths of working with both, you'll discover new ways to accomplish data science tasks and expand your skill set.Authors Boyan Angelov and Rick Scavetta explain the parallel structures of these languages and highlight where each one excels, whether it's their linguistic features or the powers of their open source ecosystems. Not only will you learn how to use Python and R together in real-world settings, but you'll also broaden your knowledge and job opportunities by working as a bilingual data scientist.Learn Python and R from the perspective of your current languageUnderstand the strengths and weaknesses of each languageIdentify use cases where one language is better suited than the otherUnderstand the modern open source ecosystem available for both, including packages, frameworks, and workflowsLearn how to integrate R and Python in a single workflowFollow a real-world case study that demonstrates ways to use these languages together
R 1,505.00
Python for Information Professionals
Python for Information Professionals: How to Design Practical Applications to Capitalize on the Data Explosion is an introduction to the Python programming language for library and information professionals with little or no prior experience. As opposed to the many Python books available today that focus on the language only from a general sense, this book is designed specifically for information professionals who are seeking to advance their career prospects or challenge themselves in new ways by acquiring skills within the rapidly expanding field of data science. Readers of Python for Information Professionals will learn to:Develop Python applications for the retrieval, cleaning, and analysis of large datasets. Design applications to support traditional library functions and create new opportunities to maximize library value. Consider data security and privacy relevant to data analysis when using the Python language.
R 1,464.00
Computational Financial Mathematics using MATHEMATICA®
Given the explosion of interest in mathematical methods for solving problems in finance and trading, a great deal of research and development is taking place in universities, large brokerage firms, and in the supporting trading software industry. Mathematical advances have been made both analytically and numerically in finding practical solutions. This book provides a comprehensive overview of existing and original material, about what mathematics when allied with Mathematica can do for finance. Sophisticated theories are presented systematically in a user-friendly style, and a powerful combination of mathematical rigor and Mathematica programming. Three kinds of solution methods are emphasized: symbolic, numerical, and Monte-- Carlo. Nowadays, only good personal computers are required to handle the symbolic and numerical methods that are developed in this book. Key features: * No previous knowledge of Mathematica programming is required * The symbolic, numeric, data management and graphic capabilities of Mathematica are fully utilized * Monte--Carlo solutions of scalar and multivariable SDEs are developed and utilized heavily in discussing trading issues such as Black--Scholes hedging * Black--Scholes and Dupire PDEs are solved symbolically and numerically * Fast numerical solutions to free boundary problems with details of their Mathematica realizations are provided * Comprehensive study of optimal portfolio diversification, including an original theory of optimal portfolio hedging under non-Log-Normal asset price dynamics is presented The book is designed for the academic community of instructors and students, and most importantly, will meet the everyday trading needs of quantitatively inclined professional and individual investors.
R 2,396.60
