Search results for "Data Analytics for Finance Using Python"
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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 3,826.67
People Analytics
This book addresses the topic of HR analytics focusing especially on demystifying analytics for HR managers, from both statistical and computing points of view. It presents the evolution and the importance of people analytics in today’s workforce management and highlights the motivation for studying business analytics, shows the different levels of analytic maturity and discusses real world success stories of business analytics in action through case studies. The volume provides the readers with a step-by-step guide to analyze HR data and interpret the results for strategy formulation. Each chapter of the book includes a vignette, data-driven exercises, and real-world case studies. The authors also introduce the readers to data visualization software Microsoft Power BI as well as elaborates on how to use Tableau Desktop.This book would be useful to the students of human resource management, organizational behaviour, applied psychology, social work and sociology. It would also serve as very good handbook for practicing business executives helping them to implement data analytics for effective decision making in all kinds of business situations around the world.
R 9,331.44
R 1,636.53
Advanced Data Analytics in Health
This book introduces readers to the methods, types of data, and scale of analysis used in the context of health. The challenges of working with big data are explored throughout the book, while the benefits are also emphasized through the discoveries made possible by linking large datasets. Methods include thorough case studies from statistics, as well as the newest facets of data analytics: data visualization, modeling and simulation, and machine learning. The diversity of datasets is illustrated through chapters on networked data, image processing, and text, in addition to typical structured numerical datasets. While the methods, types of data, and scale have been individually covered elsewhere, by bringing them all together under one “umbrella” the book highlights synergies, while also helping scholars fluidly switch between tools as needed. New challenges and emerging frontiers are also discussed, helping scholars grasp how methods will need to change in response to the latest challenges in health.
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R 1,099.00
R 916.00
Fundamentals of HR Analytics
The future of the Human Resource Management profession is inextricably linked to understanding data analytics. This book provides practical, hands-on approaches to connect data to HR policies and practices to help influence overall business performance. Building on traditional HR skillsets, the book makes understanding and engaging with data analytics possible for professionals at all levels. Leveraging key statistical and financial concepts, including ROI and people productivity, and commonly available tools such as Workday and Tableau, the authors explore key skills and tasks in an accessible and illuminating way, including: data-analytic thinking data management data collection clean-up and warehousing building descriptive and predictive models applying HR analytics skills and tools to workforce planning, recruitment, training, and turnover analysis. Fundamentals of HR Analytics is an essential resource for aspiring, new and experienced HR professionals across a wide range of industrial contexts, as well as data analysts working on business workforce focused projects and upper level students of HR encountering data analytics in this context for the first time.
R 1,557.00
Advances in Big Data Analytics
Today, big data affects countless aspects of our daily lives. This book provides a comprehensive and cutting-edge study on big data analytics, based on the research findings and applications developed by the author and his colleagues in related areas. It addresses the concepts of big data analytics and/or data science, multi-criteria optimization for learning, expert and rule-based data analysis, support vector machines for classification, feature selection, data stream analysis, learning analysis, sentiment analysis, link analysis, and evaluation analysis. The book also explores lessons learned in applying big data to business, engineering and healthcare. Lastly, it addresses the advanced topic of intelligence-quotient (IQ) tests for artificial intelligence.Since each aspect mentioned above concerns a specific domain of application, taken together, the algorithms, procedures, analysis and empirical studies presented here offer a general picture of big data developments. Accordingly, the book can not only serve as a textbook for graduates with a fundamental grasp of training in big data analytics, but can also show practitioners how to use the proposed techniques to deal with real-world big data problems.
R 7,326.00
Data Science and Big Data Analytics in Smart Environments
Most applications generate large datasets, like social networking and social influence programs, smart cities applications, smart house environments, Cloud applications, public web sites, scientific experiments and simulations, data warehouse, monitoring platforms, and e-government services. Data grows rapidly, since applications produce continuously increasing volumes of both unstructured and structured data. Large-scale interconnected systems aim to aggregate and efficiently exploit the power of widely distributed resources. In this context, major solutions for scalability, mobility, reliability, fault tolerance and security are required to achieve high performance and to create a smart environment. The impact on data processing, transfer and storage is the need to re-evaluate the approaches and solutions to better answer the user needs. A variety of solutions for specific applications and platforms exist so a thorough and systematic analysis of existing solutions for data science, data analytics, methods and algorithms used in Big Data processing and storage environments is significant in designing and implementing a smart environment.Fundamental issues pertaining to smart environments (smart cities, ambient assisted leaving, smart houses, green houses, cyber physical systems, etc.) are reviewed. Most of the current efforts still do not adequately address the heterogeneity of different distributed systems, the interoperability between them, and the systems resilience. This book will primarily encompass practical approaches that promote research in all aspects of data processing, data analytics, data processing in different type of systems: Cluster Computing, Grid Computing, Peer-to-Peer, Cloud/Edge/Fog Computing, all involving elements of heterogeneity, having a large variety of tools and software to manage them. The main role of resource management techniques in this domain is to create the suitable frameworks for development of applications and deployment in smart environments, with respect to high performance. The book focuses on topics covering algorithms, architectures, management models, high performance computing techniques and large-scale distributed systems.
R 10,601.02
Even You Can Learn Statistics and Analytics
THE GUIDE FOR ANYONE AFRAID TO LEARN STATISTICS & ANALYTICS UPDATED WITH NEW EXAMPLES & EXERCISES This book discusses statistics and analytics using plain language and avoiding mathematical jargon. If you thought you couldn’t learn these data analysis subjects because they were too technical or too mathematical, this book is for you! This edition delivers more everyday examples and end-of-chapter exercises and contains updated instructions for using Microsoft Excel. You’ll use downloadable data sets and spreadsheet solutions, template-based solutions you can put right to work. Using this book, you will understand the important concepts of statistics and analytics, including learning the basic vocabulary of these subjects. Create tabular and visual summaries and learn to avoid common charting errorsGain experience working with common descriptive statistics measures including the mean, median, and mode; and standard deviation and variance, among othersUnderstand the probability concepts that underlie inferential statisticsLearn how to apply hypothesis tests, using Z, t, chi-square, ANOVA, and other techniquesDevelop skills using regression analysis, the most commonly-used Inferential statistical methodExplore results produced by predictive analytics softwareChoose the right statistical or analytic techniques for any data analysis taskOptionally, read the “Equation Blackboards,” designed for readers who want to learn about the mathematical foundations of selected methods
R 798.00
Python and Matplotlib Essentials for Scientists and Engineers
This book provides an introduction to the core features of the Python programming language and Matplotlib plotting routings for scientists and engineers (or students of either discipline) who want to use Python™ to analyse data, simulate physical processes, and render publication-quality plots. No previous programming experience is needed before reading the first page.Readers will learn the core features of the Python programming language in under a day. They will be able to immediately use Python to implement codes that solve their own problems and make beautiful plots and animations. Python code is extremely fast to prototype, allowing users to achieve results quickly and accurately. Python and Matplotlib Essentials for Scientists and Engineers is accessible for motivated high-school students, but will likely be most useful for undergraduate and graduate students as well as working professionals who have some background with the basic mathematical concepts. This book is intended for technical people who want to get things done.
R 1,464.00
Big-Data Analytics for Cloud, IoT and Cognitive Computing
The definitive guide to successfully integrating social, mobile, Big-Data analytics, cloud and IoT principles and technologies The main goal of this book is to spur the development of effective big-data computing operations on smart clouds that are fully supported by IoT sensing, machine learning and analytics systems. To that end, the authors draw upon their original research and proven track record in the field to describe a practical approach integrating big-data theories, cloud design principles, Internet of Things (IoT) sensing, machine learning, data analytics and Hadoop and Spark programming. Part 1 focuses on data science, the roles of clouds and IoT devices and frameworks for big-data computing. Big data analytics and cognitive machine learning, as well as cloud architecture, IoT and cognitive systems are explored, and mobile cloud-IoT-interaction frameworks are illustrated with concrete system design examples. Part 2 is devoted to the principles of and algorithms for machine learning, data analytics and deep learning in big data applications. Part 3 concentrates on cloud programming software libraries from MapReduce to Hadoop, Spark and TensorFlow and describes business, educational, healthcare and social media applications for those tools. The first book describing a practical approach to integrating social, mobile, analytics, cloud and IoT (SMACT) principles and technologiesCovers theory and computing techniques and technologies, making it suitable for use in both computer science and electrical engineering programsOffers an extremely well-informed vision of future intelligent and cognitive computing environments integrating SMACT technologiesFully illustrated throughout with examples, figures and approximately 150 problems to support and reinforce learningFeatures a companion website with an instructor manual and PowerPoint slides www.wiley.com/go/hwangIOT Big-Data Analytics for Cloud, IoT and Cognitive Computing satisfies the demand among university faculty and students for cutting-edge information on emerging intelligent and cognitive computing systems and technologies. Professionals working in data science, cloud computing and IoT applications will also find this book to be an extremely useful working resource.
R 3,353.00
Application of Big Data and Business Analytics
Application of Big Data and Business Analytics uses advanced analytic tools to explore the solutions to problems in society, environment and industry. The chapters within bring together researchers, engineers and practitioners, encompassing a wide and diverse set of topics in almost every field. With the increase in the availability of data, analytics has now become a major element in both the top line and the bottom line of any organization. With this in mind, Application of Big Data and Business Analytics brings together researchers, engineers and practitioners, encompassing a wide and diverse set of topics in almost every field. The primary target audience of this book includes researchers, academicians and data scientists from a variety of disciplines interested in analyzing and application of big data analytics. However, this work will also be of general interest to postgraduates and undergraduates pursuing advanced study in big data.
R 4,584.83
Numerical Methods in Finance with C++
Driven by concrete computational problems in quantitative finance, this book provides aspiring quant developers with the numerical techniques and programming skills they need. The authors start from scratch, so the reader does not need any previous experience of C++. Beginning with straightforward option pricing on binomial trees, the book gradually progresses towards more advanced topics, including nonlinear solvers, Monte Carlo techniques for path-dependent derivative securities, finite difference methods for partial differential equations, and American option pricing by solving a linear complementarity problem. Further material, including solutions to all exercises and C++ code, is available online. The book is ideal preparation for work as an entry-level quant programmer and it gives readers the confidence to progress to more advanced skill sets involving C++ design patterns as applied in finance.
R 3,215.48
Statistics For Business And Financial Economics
NewsProfessor Cheng-Few Lee ranks #1 based on his publications in the 26 core finance journals, and #163 based on publications in the 7 leading finance journals (Source: Most Prolific Authors in the Finance Literature: 1959-2008 by Jean L Heck and Philip L Cooley (Saint Joseph's University and Trinity University).This is an extensively revised edition of a popular statistics textbook for business and economics students. The first edition has been adopted by universities and colleges worldwide, including New York University, Carnegie Mellon University and UCLA.Designed for upper-level undergraduates, MBA and other graduate students, this book closely integrates various statistical techniques with concepts from business, economics and finance and clearly demonstrates the power of statistical methods in the real world of business. While maintaining the essence of the first edition, the new edition places more emphasis on finance, economics and accounting concepts with updated sample data. Students will find this book very accessible with its straightforward language, ample cases, examples, illustrations and real-life applications. The book is also useful for financial analysts and portfolio managers.
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Simplifying Data Engineering and Analytics with Delta
Explore how Delta brings reliability, performance, and governance to your data lake and all the AI and BI use cases built on top of itKey FeaturesLearn Delta's core concepts and features as well as what makes it a perfect match for data engineering and analysisSolve business challenges of different industry verticals using a scenario-based approachMake optimal choices by understanding the various tradeoffs provided by DeltaBook DescriptionDelta helps you generate reliable insights at scale and simplifies architecture around data pipelines, allowing you to focus primarily on refining the use cases being worked on. This is especially important when you consider that existing architecture is frequently reused for new use cases.In this book, you'll learn about the principles of distributed computing, data modeling techniques, and big data design patterns and templates that help solve end-to-end data flow problems for common scenarios and are reusable across use cases and industry verticals. You'll also learn how to recover from errors and the best practices around handling structured, semi-structured, and unstructured data using Delta. After that, you'll get to grips with features such as ACID transactions on big data, disciplined schema evolution, time travel to help rewind a dataset to a different time or version, and unified batch and streaming capabilities that will help you build agile and robust data products.By the end of this Delta book, you'll be able to use Delta as the foundational block for creating analytics-ready data that fuels all AI/BI use cases.What you will learnExplore the key challenges of traditional data lakesAppreciate the unique features of Delta that come out of the boxAddress reliability, performance, and governance concerns using DeltaAnalyze the open data format for an extensible and pluggable architectureHandle multiple use cases to support BI, AI, streaming, and data discoveryDiscover how common data and machine learning design patterns are executed on DeltaBuild and deploy data and machine learning pipelines at scale using DeltaWho this book is forData engineers, data scientists, ML practitioners, BI analysts, or anyone in the data domain working with big data will be able to put their knowledge to work with this practical guide to executing pipelines and supporting diverse use cases using the Delta protocol. Basic knowledge of SQL, Python programming, and Spark is required to get the most out of this book.
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R 490.00
Data Analytics for Pandemics
Epidemic trend analysis, timeline progression, prediction, and recommendation are critical for initiating effective public health control strategies, and AI and data analytics play an important role in epidemiology, diagnostic, and clinical fronts. The focus of this book is data analytics for COVID-19, which includes an overview of COVID-19 in terms of epidemic/pandemic, data processing and knowledge extraction. Data sources, storage and platforms are discussed along with discussions on data models, their performance, different big data techniques, tools and technologies. This book also addresses the challenges in applying analytics to pandemic scenarios, case studies and control strategies. Aimed at Data Analysts, Epidemiologists and associated researchers, this book: discusses challenges of AI model for big data analytics in pandemic scenarios; explains how different big data analytics techniques can be implemented; provides a set of recommendations to minimize infection rate of COVID-19; summarizes various techniques of data processing and knowledge extraction; enables users to understand big data analytics techniques required for prediction purposes.
R 3,595.01
Financial Mathematics
This textbook is designed to facilitate a thorough learning for students of financial mathematics. It includes exercises and theoretical questions across seven chapters: Interest Theory, Financial Flows and Annuities, Profitability and Risk of Financial Operations, Portfolio Analysis, Bonds, Modigliani-Miller Theory, and Brusov-Filatova-Orekhova Theory. The last two chapters are dedicated to modern theories of capital structure, including problems and tasks. More than 130 detailed solutions are provided to help students solve the assignments in the textbook.This textbook is suitable for undergraduate and graduate students in all financial and economic fields, including finance and credit, accounting and auditing, taxes, insurance, and international economic relations. It is also useful for professionals in financial and economic specialties, including financial analysts, as well as anyone interested in mastering quantitative methods in finance and economics.
R 3,663.00
Marketing Analytics
Marketing Analytics provides guidelines in the application of statistics using IBM SPSS Statistics Software (SPSS) for students and professionals using quantitative methods in marketing and consumer behavior. With simple language and a practical, screenshot-led approach, the book presents 11 multivariate techniques and the steps required to perform analysis. Each chapter contains a brief description of the technique, followed by the possible marketing research applications. One of these applications is then used in detail to illustrate its applicability in a research context, including the needed SPSS commands and illustrations. Each chapter also includes practical exercises that require the readers to perform the technique and interpret the results, equipping students with the necessary skills to apply statistics by means of SPSS in marketing and consumer research. Finally, there is a list of articles employing the technique that can be used for further reading. This textbook provides introductory material for advanced undergraduate and postgraduate students studying marketing and consumer analytics, teaching methods along with practical software-applied training using SPSS. Support material includes two real data sets to illustrate the techniques’ applications and PowerPoint slides providing a step-by-step guide to the analysis and commented outcomes. Professionals are invited to use the book to select and use the appropriate analytics for their specific context.
R 6,930.45
Data-Driven Marketing Analytics
Data-Driven Marketing Analytics: Research Methods and Analytical Tools bridges the gap between traditional marketing research and modern big data analytics, offering a step-by-step guide to transforming data into actionable insights.Readers will gain practical skills to design and manage research projects, predict customer behaviour, and uncover hidden patterns in purchasing data. The book delivers these benefits through hands-on exercises, shareable datasets, and visual workflows, ensuring accessibility for users without coding experience. Key features include real-world case studies, interactive examples, and tools for segmentation, forecasting, and strategic decision-making.This book is tailored for postgraduate students, researchers, and marketing professionals seeking to master data-driven decision-making. It is ideal for advanced courses in Marketing Analytics, Management Information Systems, and Business Statistics.The book is supported by a comprehensive online repository containing over 100 ready-to-use files, including datasets, Excel solutions, SPSS files, and Orange workflows.
R 8,572.00
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
Econometric Analysis of Recurrent Events in Macroeconomics and Finance
The global financial crisis highlighted the impact on macroeconomic outcomes of recurrent events like business and financial cycles, highs and lows in volatility, and crashes and recessions. At the most basic level, such recurrent events can be summarized using binary indicators showing if the event will occur or not. These indicators are constructed either directly from data or indirectly through models. Because they are constructed, they have different properties than those arising in microeconometrics, and how one is to use them depends a lot on the method of construction. This book presents the econometric methods necessary for the successful modeling of recurrent events, providing valuable insights for policymakers, empirical researchers, and theorists. It explains why it is inherently difficult to forecast the onset of a recession in a way that provides useful guidance for active stabilization policy, with the consequence that policymakers should place more emphasis on making the economy robust to recessions.The book offers a range of econometric tools and techniques that researchers can use to measure recurrent events, summarize their properties, and evaluate how effectively economic and statistical models capture them. These methods also offer insights for developing models that are consistent with observed financial and real cycles. This book is an essential resource for students, academics, and researchers at central banks and institutions such as the International Monetary Fund.
R 2,437.00
Mastering Text Analytics
This book is a comprehensive guide to mastering Natural Language Processing (NLP), a rapidly growing field in AI-powered text and data analytics. It equips you with tools and techniques to extract valuable insights from both structured and unstructured data, enabling you to uncover insights beyond the reach of traditional data analysis methods and stay competitive in this evolving domain. The book starts with foundational concepts, such as collecting and extracting data for NLP projects, before progressing to advanced topics like applications of transfer learning in NLP and Large Language Models (LLMs). Each chapter emphasizes real-world applications and includes practical case studies to ensure the knowledge is immediately applicable. Throughout the book, readers will find Python code demonstrations, hands-on projects, and detailed explanations of key concepts. Special features include business use cases from industries like healthcare and customer service, practice exercises to reinforce learning, and explorations of emerging NLP technologies. These elements make the book not only informative but also highly engaging and interactive. By the end of the book, the reader will have a solid foundation in Generative AI techniques to apply them to complex challenges. Whether you're a budding data scientist or a seasoned professional, this guide will help you harness the power of AI-driven text and data analytics effectively. What you will learn: Understand NLP with easy-to-follow explanations, examples, and Python implementations. Explore techniques such as transformers, word embeddings, and pragmatic analysis in real-world contexts. Work with real-world datasets and apply pre-processing, tokenization, and text extraction using NLP libraries. How to build complete NLP pipelines from data collection to model implementation, including sentiment analysis and chatbots. Learn state-of-the-art methods like deep learning techniques in NLP, large language models (LLMs), and zero-shot learning in NLP. Who this book is for: This book is tailored for data scientists, machine learning engineers, AI practitioners, and software developers seeking to learn NLP techniques and apply them to solve problems.
R 1,368.00
International Finance
Understanding the globalized world economy is more important than ever before. This book provides a clear, concise, and up-to-date look at the economic foundations of international finance. The authors explain the principal concepts in an engaging and accessible manner open to students from any discipline, incorporating contemporary finance data through full-colour diagrams and graphs. Throughout, economic models are discussed in the context of recent and current international finance issues, to ensure students gain a concrete understanding and see how the field impacts the real world. Written for upper undergraduate courses, the book includes feature boxes that marry theory and economics in practice to show models applied, a featured real-world application for every chapter, and over 140 end-of-chapter questions help students fully engage with and consolidate their learning. Online resources for instructors include a solutions manual, lecture slides and the book figures as JPEGs.
R 5,414.00
Computational Business Analytics
Learn How to Properly Use the Latest Analytics Approaches in Your OrganizationComputational Business Analytics presents tools and techniques for descriptive, predictive, and prescriptive analytics applicable across multiple domains. Through many examples and challenging case studies from a variety of fields, practitioners easily see the connections to their own problems and can then formulate their own solution strategies.The book first covers core descriptive and inferential statistics for analytics. The author then enhances numerical statistical techniques with symbolic artificial intelligence (AI) and machine learning (ML) techniques for richer predictive and prescriptive analytics. With a special emphasis on methods that handle time and textual data, the text: Enriches principal component and factor analyses with subspace methods, such as latent semantic analyses Combines regression analyses with probabilistic graphical modeling, such as Bayesian networks Extends autoregression and survival analysis techniques with the Kalman filter, hidden Markov models, and dynamic Bayesian networks Embeds decision trees within influence diagramsAugments nearest-neighbor and k-means clustering techniques with support vector machines and neural networks These approaches are not replacements of traditional statistics-based analytics; rather, in most cases, a generalized technique can be reduced to the underlying traditional base technique under very restrictive conditions. The book shows how these enriched techniques offer efficient solutions in areas, including customer segmentation, churn prediction, credit risk assessment, fraud detection, and advertising campaigns.
R 9,354.79
Environmental Data Analysis with MatLab or Python
Environmental Data Analysis with MATLAB, Third Edition, is a new edition that expands fundamentally on the original with an expanded tutorial approach, more clear organization, new crib sheets, and problem sets providing a clear learning path for students and researchers working to analyze real data sets in the environmental sciences. The work teaches the basics of the underlying theory of data analysis and then reinforces that knowledge with carefully chosen, realistic scenarios, including case studies in each chapter. The new edition is expanded to include applications to Python, an open source software environment. Significant content in Environmental Data Analysis with MATLAB, Third Edition is devoted to teaching how the programs can be effectively used in an environmental data analysis setting. This new edition offers chapters that can both be used as self-contained resources or as a step-by-step guide for students, and is supplemented with data and scripts to demonstrate relevant use cases.
R 3,531.00
Learning Python Data Visualization
If you are a Python novice or an experienced developer and want to explore data visualization libraries, then this is the book for you. No prior charting or graphics experience is needed.
R 1,563.00
Introduction to Python Programming and Data Structures, Global Edition
For introductory courses in Python Programming and Data Structures. A fundamentals first approach to programming helps students create efficient, elegant code. Introduction to Python Programming and Data Structures introduces students to basic programming concepts using a fundamentals-first approach that prepares students to learn object-oriented programming and advanced Python programming. This approach presents programming concepts and techniques that include control statements, loops, functions, and arrays before designing custom classes. Students learn basic logic and programming concepts prior to moving into object-oriented and GUI programming. The content incorporates a wide variety of problems with various levels of difficulty and covers many application areas to engage and motivate students.
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Machine Learning in Finance
This book discusses the evolution of technical features in decentralized finance and focuses on machine-learning finance in emerging economies. As technological advancement evolves at an unpredictable pace, the financial industry, like every other sector, must adapt accordingly. Furthermore, the rapid expansion of diverse financial products and services is creating new applications and markets. Alongside technological progress, the exploration of complex patterns in vast amounts of data, known as big data, is facilitated by its commonly acknowledged characteristics: volume, variety, veracity, value, and velocity.Overall, machine learning has become crucial in the financial industry, allowing businesses to automate operations, gain insights from data, and make more informed decisions in real time. This edited book covers algorithmic trading, risk management, fraud detection, customer service and personalization, portfolio management, credit scoring, sentiment analysis, and algorithmic pricing. The book connects theoretical concepts with practical real-world applications, benefiting professionals looking to enhance their proficiency in using these methods efficiently. It offers insightful guidance for theorists, market participants, and policymakers by exploring financial theories and practices in light of contemporary machine-learning approaches, with a special emphasis on emerging economies.
R 8,146.22
Business Analytics, Volume I
This book deals with Business Analytics (BA) – an emerging area in modern business decision making. Business analytics is a data driven decision making approach that uses statistical and quantitative analysis along with data mining, management science, and fact-based data to measure past business performance to guide an organization in business planning and effective decision making. Business Analytics tools are also used to predict future business outcomes with the help of forecasting and predictive modeling.In this age of technology, massive amount of data are collected by companies. Successful companies use their data as an asset and use them for competitive advantage. Business Analytics is helping businesses in making informed business decisions and automating and optimizing business processes.Successful business analytics depends on the quality of data. Skilled analysts, who understand the technologies and their business, use business analytics tools as an organizational commitment to data-driven decision making.
R 707.65
Harness Oil and Gas Big Data with Analytics
Use big data analytics to efficiently drive oil and gas exploration and production Harness Oil and Gas Big Data with Analytics provides a complete view of big data and analytics techniques as they are applied to the oil and gas industry. Including a compendium of specific case studies, the book underscores the acute need for optimization in the oil and gas exploration and production stages and shows how data analytics can provide such optimization. This spans exploration, development, production and rejuvenation of oil and gas assets. The book serves as a guide for fully leveraging data, statistical, and quantitative analysis, exploratory and predictive modeling, and fact-based management to drive decision making in oil and gas operations. This comprehensive resource delves into the three major issues that face the oil and gas industry during the exploration and production stages: Data management, including storing massive quantities of data in a manner conducive to analysis and effectively retrieving, backing up, and purging dataQuantification of uncertainty, including a look at the statistical and data analytics methods for making predictions and determining the certainty of those predictionsRisk assessment, including predictive analysis of the likelihood that known risks are realized and how to properly deal with unknown risks Covering the major issues facing the oil and gas industry in the exploration and production stages, Harness Big Data with Analytics reveals how to model big data to realize efficiencies and business benefits.
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R 248.09
Lectures On The Theory And Application Of Modern Finance With R And Chatgpt
These lecture notes are thought for Master courses in Finance, Fintech and Quantitative Finance programmes. We fully subscribe to the philosophy that post-graduate students should be offered courses that are really at the cutting edge of the technologies and advances that are disrupting the financial industry and delve deep into topics such as A.I., machine learning, and their importance for Asset Management.In these notes, the illustration of the theory of Finance is paired with practical applications to real-life asset allocation problems. A hands-on approach is proposed to construct and manipulate databases to build portfolios, assess their performance and manage their risk. The course begins with a section on the fundamentals on individual choice to market valuation, covering the traditional Markowitz mean-variance approach, market-based asset pricing and Arbitrage-based pricing theory.Empirical modelling in finance is then introduced by illustrating its working and its historical evolution. The translation of financial theory into action on data is driven by building predictive models for asset prices and returns. Basic models are explored, and programming emerges as an essential prerequisite for data manipulation. Readers can acquaint themselves with the statistical software R and exhibit the application of theoretical concepts to financial data, illustrated by sample programs, exercises, and corresponding solutions.
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