Search results for "data analytics for finance using python"
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Data Analytics for Business
Data analytics underpin our modern data-driven economy. This textbook explains the relevance of data analytics at the firm and industry levels, tracing the evolution and key components of the field, and showing how data analytics insights can be leveraged for business results. The first section of the text covers key topics such as data analytics tools, data mining, business intelligence, customer relationship management, and cybersecurity. The chapters then take an industry focus, exploring how data analytics can be used in particular settings to strengthen business decision-making. A range of sectors are examined, including financial services, accounting, marketing, sport, health care, retail, transport, and education. With industry case studies, clear definitions of terminology, and no background knowledge required, this text supports students in gaining a solid understanding of data analytics and its practical applications. PowerPoint slides, a test bank of questions, and an instructor’s manual are also provided as online supplements.This will be a valuable text for undergraduate level courses in data analytics, data mining, business intelligence, and related areas.
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Machine Learning for Financial Risk Management with Python
Financial risk management is quickly evolving with the help of artificial intelligence. With this practical book, developers, programmers, engineers, financial analysts, and risk analysts will explore Python-based machine learning and deep learning models for assessing financial risk. You'll learn how to compare results from ML models with results obtained by traditional financial risk models.Author Abdullah Karasan helps you explore the theory behind financial risk assessment before diving into the differences between traditional and ML models.Review classical time series applications and compare them with deep learning modelsExplore volatility modeling to measure degrees of risk, using support vector regression, neural networks, and deep learningRevisit and improve market risk models (VaR and expected shortfall) using machine learning techniquesDevelop a credit risk based on a clustering technique for risk bucketing, then apply Bayesian estimation, Markov chain, and other ML modelsCapture different aspects of liquidity with a Gaussian mixture modelUse machine learning models for fraud detectionIdentify corporate risk using the stock price crash metricExplore a synthetic data generation process to employ in financial risk
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Predictive and Simulation Analytics
This book connects predictive analytics and simulation analytics, with the end goal of providing Rich Information to stakeholders in complex systems to direct data-driven decisions. Readers will explore methods for extracting information from data, work with simple and complex systems, and meld multiple forms of analytics for a more nuanced understanding of data science. The methods can be readily applied to business problems such as demand measurement and forecasting, predictive modeling, pricing analytics including elasticity estimation, customer satisfaction assessment, market research, new product development, and more. The book includes Python examples in Jupyter notebooks, available at the book's affiliated Github.This volume is intended for current and aspiring business data analysts, data scientists, and market research professionals, in both the private and public sectors.
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Data Analytics
Large data sets arriving at every increasing speeds require a new set of efficient data analysis techniques. Data analytics are becoming an essential component for every organization and technologies such as health care, financial trading, Internet of Things, Smart Cities or Cyber Physical Systems. However, these diverse application domains give rise to new research challenges. In this context, the book provides a broad picture on the concepts, techniques, applications, and open research directions in this area. In addition, it serves as a single source of reference for acquiring the knowledge on emerging Big Data Analytics technologies.
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Data Analytics for Business
Data analytics underpin our modern data-driven economy. This textbook explains the relevance of data analytics at the firm and industry levels, tracing the evolution and key components of the field, and showing how data analytics insights can be leveraged for business results. The first section of the text covers key topics such as data analytics tools, data mining, business intelligence, customer relationship management, and cybersecurity. The chapters then take an industry focus, exploring how data analytics can be used in particular settings to strengthen business decision-making. A range of sectors are examined, including financial services, accounting, marketing, sport, health care, retail, transport, and education. With industry case studies, clear definitions of terminology, and no background knowledge required, this text supports students in gaining a solid understanding of data analytics and its practical applications. PowerPoint slides, a test bank of questions, and an instructor’s manual are also provided as online supplements.This will be a valuable text for undergraduate level courses in data analytics, data mining, business intelligence, and related areas.
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Mastering Predictive Analytics with scikit-learn and TensorFlow
Learn advanced techniques to improve the performance and quality of your predictive modelsKey FeaturesUse ensemble methods to improve the performance of predictive analytics modelsImplement feature selection, dimensionality reduction, and cross-validation techniquesDevelop neural network models and master the basics of deep learningBook DescriptionPython is a programming language that provides a wide range of features that can be used in the field of data science. Mastering Predictive Analytics with scikit-learn and TensorFlow covers various implementations of ensemble methods, how they are used with real-world datasets, and how they improve prediction accuracy in classification and regression problems.This book starts with ensemble methods and their features. You will see that scikit-learn provides tools for choosing hyperparameters for models. As you make your way through the book, you will cover the nitty-gritty of predictive analytics and explore its features and characteristics. You will also be introduced to artificial neural networks and TensorFlow, and how it is used to create neural networks. In the final chapter, you will explore factors such as computational power, along with improvement methods and software enhancements for efficient predictive analytics.By the end of this book, you will be well-versed in using deep neural networks to solve common problems in big data analysis.What you will learnUse ensemble algorithms to obtain accurate predictionsApply dimensionality reduction techniques to combine features and build better modelsChoose the optimal hyperparameters using cross-validationImplement different techniques to solve current challenges in the predictive analytics domainUnderstand various elements of deep neural network (DNN) modelsImplement neural networks to solve both classification and regression problemsWho this book is forMastering Predictive Analytics with scikit-learn and TensorFlow is for data analysts, software engineers, and machine learning developers who are interested in implementing advanced predictive analytics using Python. Business intelligence experts will also find this book indispensable as it will teach them how to progress from basic predictive models to building advanced models and producing more accurate predictions. Prior knowledge of Python and familiarity with predictive analytics concepts are assumed.
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Advanced Analytics in Power BI with R and Python
This easy-to-follow guide provides R and Python recipes to help you learn and apply the top languages in the field of data analytics to your work in Microsoft Power BI. Data analytics expert and author Ryan Wade shows you how to use R and Python to perform tasks that are extremely hard, if not impossible, to do using native Power BI tools. For example, you will learn to score Power BI data using custom data science models and powerful models from Microsoft Cognitive Services. The R and Python languages are powerful complements to Power BI. They enable advanced data transformation techniques that are difficult to perform in Power BI in its default configuration but become easier by leveraging the capabilities of R and Python. If you are a business analyst, data analyst, or a data scientist who wants to push Power BI and transform it from being just a business intelligence tool into an advanced data analytics tool, then this is the book to help you do that. What You Will LearnCreate advanced data visualizations via R using the ggplot2 packageIngest data using R and Python to overcome some limitations of Power QueryApply machine learning models to your data using R and Python without the need of Power BI premium capacityIncorporate advanced AI in Power BI without the need of Power BI premium capacity via Microsoft Cognitive Services, IBM Watson Natural Language Understanding, and pre-trained models in SQL Server Machine Learning Services Perform advanced string manipulations not otherwise possible in Power BI using R and Python Who This Book Is ForPower users, data analysts, and data scientists who want to go beyond Power BI’s built-in functionality to create advanced visualizations, transform data in ways not otherwise supported, and automate data ingestion from sources such as SQL Server and Excel in a more concise way
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Large-Scale Data Analytics with Python and Spark
Based on the authors' extensive teaching experience, this hands-on graduate-level textbook teaches how to carry out large-scale data analytics and design machine learning solutions for big data. With a focus on fundamentals, this extensively class-tested textbook walks students through key principles and paradigms for working with large-scale data, frameworks for large-scale data analytics (Hadoop, Spark), and explains how to implement machine learning to exploit big data. It is unique in covering the principles that aspiring data scientists need to know, without detail that can overwhelm. Real-world examples, hands-on coding exercises and labs combine with exceptionally clear explanations to maximize student engagement. Well-defined learning objectives, exercises with online solutions for instructors, lecture slides, and an accompanying suite of lab exercises of increasing difficulty in Jupyter Notebooks offer a coherent and convenient teaching package. An ideal teaching resource for courses on large-scale data analytics with machine learning in computer/data science departments.
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Python for Marketing Research and Analytics
This book provides an introduction to quantitative marketing with Python. The book presents a hands-on approach to using Python for real marketing questions, organized by key topic areas. Following the Python scientific computing movement toward reproducible research, the book presents all analyses in Colab notebooks, which integrate code, figures, tables, and annotation in a single file. The code notebooks for each chapter may be copied, adapted, and reused in one's own analyses. The book also introduces the usage of machine learning predictive models using the Python sklearn package in the context of marketing research. This book is designed for three groups of readers: experienced marketing researchers who wish to learn to program in Python, coming from tools and languages such as R, SAS, or SPSS; analysts or students who already program in Python and wish to learn about marketing applications; and undergraduate or graduate marketing students with little or no programming background. It presumes only an introductory level of familiarity with formal statistics and contains a minimum of mathematics.
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Python for Excel Users
Excel is a mainstay of the modern workplace, but it isn't up to every task. Python for Excel Users offers a better way, showing how relatively simple programming can greatly boost productivity by streamlining workflows and adding new tools to the reader's toolbox - no programming experience required. Uses include: Generating beautiful data visualizations, Creating interactive reports and dashboards, Tracking collaborative projects. This book leverages what readers already know about Excel to introduce useful coding applications. Practical examples illustrate how programming skills can be immediately applied to the reader's day-to-day work.
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Big Data Analysis with Python
Get to grips with processing large volumes of data and presenting it as engaging, interactive insights using Spark and Python.Key FeaturesGet a hands-on, fast-paced introduction to the Python data science stackExplore ways to create useful metrics and statistics from large datasetsCreate detailed analysis reports with real-world dataBook DescriptionProcessing big data in real time is challenging due to scalability, information inconsistency, and fault tolerance. Big Data Analysis with Python teaches you how to use tools that can control this data avalanche for you. With this book, you'll learn practical techniques to aggregate data into useful dimensions for posterior analysis, extract statistical measurements, and transform datasets into features for other systems.The book begins with an introduction to data manipulation in Python using pandas. You'll then get familiar with statistical analysis and plotting techniques. With multiple hands-on activities in store, you'll be able to analyze data that is distributed on several computers by using Dask. As you progress, you'll study how to aggregate data for plots when the entire data cannot be accommodated in memory. You'll also explore Hadoop (HDFS and YARN), which will help you tackle larger datasets. The book also covers Spark and explains how it interacts with other tools.By the end of this book, you'll be able to bootstrap your own Python environment, process large files, and manipulate data to generate statistics, metrics, and graphs.What you will learnUse Python to read and transform data into different formatsGenerate basic statistics and metrics using data on diskWork with computing tasks distributed over a clusterConvert data from various sources into storage or querying formatsPrepare data for statistical analysis, visualization, and machine learningPresent data in the form of effective visualsWho this book is forBig Data Analysis with Python is designed for Python developers, data analysts, and data scientists who want to get hands-on with methods to control data and transform it into impactful insights. Basic knowledge of statistical measurements and relational databases will help you to understand various concepts explained in this book.
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Data-Driven Modelling and Predictive Analytics in Business and Finance
Data- driven and AI- aided applications are next- generation technologies that can be used to visualize and realize intelligent transactions in finance, banking, and business. These transactions will be enabled by powerful data- driven solutions, IoT technologies, AI- aided techniques, data analytics, and visualization tools. To implement these solutions, frameworks will be needed to support human control of intelligent computing and modern business systems. The power and consistency of data- driven competencies are a critical challenge, and so is developing explainable AI (XAI) to make data- driven transactions transparent.Data- Driven Modelling and Predictive Analytics in Business and Finance covers the need for intelligent business solutions and applications. Explaining how business applications use algorithms and models to bring out the desired results, the book covers:Data-driven modellingPredictive analyticsData analytics and visualization toolsAI-aided applicationsCybersecurity techniquesCloud computingIoT-enabled systems for developing smart financial systemsThis book was written for business analysts, financial analysts, scholars, researchers, academics, professionals, and students so they may be able to share and contribute new ideas, methodologies, technologies, approaches, models, frameworks, theories, and practices.
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Analyzing Financial Data and Implementing Financial Models Using R
This advanced undergraduate/graduate textbook teaches students in finance and economics how to use R to analyse financial data and implement financial models. It demonstrates how to take publically available data and manipulate, implement models and generate outputs typical for particular analyses. A wide spectrum of timely and practical issues in financial modelling are covered including return and risk measurement, portfolio management, option pricing and fixed income analysis. This new edition updates and expands upon the existing material providing updated examples and new chapters on equities, simulation and trading strategies, including machine learnings techniques. Select data sets are available online.
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Mathematical Modeling And Computation In Finance: With Exercises And Python And Matlab Computer Codes
This book discusses the interplay of stochastics (applied probability theory) and numerical analysis in the field of quantitative finance. The stochastic models, numerical valuation techniques, computational aspects, financial products, and risk management applications presented will enable readers to progress in the challenging field of computational finance.When the behavior of financial market participants changes, the corresponding stochastic mathematical models describing the prices may also change. Financial regulation may play a role in such changes too. The book thus presents several models for stock prices, interest rates as well as foreign-exchange rates, with increasing complexity across the chapters. As is said in the industry, 'do not fall in love with your favorite model.' The book covers equity models before moving to short-rate and other interest rate models. We cast these models for interest rate into the Heath-Jarrow-Morton framework, show relations between the different models, and explain a few interest rate products and their pricing.The chapters are accompanied by exercises. Students can access solutions to selected exercises, while complete solutions are made available to instructors. The MATLAB and Python computer codes used for most tables and figures in the book are made available for both print and e-book users. This book will be useful for people working in the financial industry, for those aiming to work there one day, and for anyone interested in quantitative finance. The topics that are discussed are relevant for MSc and PhD students, academic researchers, and for quants in the financial industry.Supplementary Material:Solutions Manual is available to instructors who adopt this textbook for their courses. Please contact sales@wspc.com.
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Python Programming for Data Analysis
This textbook grew out of notes for the ECE143 Programming for Data Analysis class that the author has been teaching at University of California, San Diego, which is a requirement for both graduate and undergraduate degrees in Machine Learning and Data Science. This book is ideal for readers with some Python programming experience. The book covers key language concepts that must be understood to program effectively, especially for data analysis applications. Certain low-level language features are discussed in detail, especially Python memory management and data structures. Using Python effectively means taking advantage of its vast ecosystem. The book discusses Python package management and how to use third-party modules as well as how to structure your own Python modules. The section on object-oriented programming explains features of the language that facilitate common programming patterns.After developing the key Python language features, the book moves on to third-party modules that are foundational for effective data analysis, starting with Numpy. The book develops key Numpy concepts and discusses internal Numpy array data structures and memory usage. Then, the author moves onto Pandas and details its many features for data processing and alignment. Because strong visualizations are important for communicating data analysis, key modules such as Matplotlib are developed in detail, along with web-based options such as Bokeh, Holoviews, Altair, and Plotly.The text is sprinkled with many tricks-of-the-trade that help avoid common pitfalls. The author explains the internal logic embodied in the Python language so that readers can get into the Python mindset and make better design choices in their codes, which is especially helpful for newcomers to both Python and data analysis. To get the most out of this book, open a Python interpreter and type along with the many code samples.
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Data Mining and Predictive Analytics for Business Decisions
With many recent advances in data science, we have many more tools and techniques available for data analysts to extract information from data sets. This book will assist data analysts to move up from simple tools such as Excel for descriptive analytics to answer more sophisticated questions using machine learning. Most of the exercises use R and Python, but rather than focus on coding algorithms, the book employs interactive interfaces to these tools to perform the analysis. Using the CRISP-DM data mining standard, the early chapters cover conducting the preparatory steps in data mining: translating business information needs into framed analytical questions and data preparation. The Jamovi and the JASP interfaces are used with R and the Orange3 data mining interface with Python. Where appropriate, Voyant and other open-source programs are used for text analytics. The techniques covered in this book range from basic descriptive statistics, such as summarization and tabulation, to more sophisticated predictivetechniques, such as linear and logistic regression, clustering, classification, and text analytics. Includes companion files with case study files, solution spreadsheets, data sets and charts, etc. from the book. FEATURES:Covers basic descriptive statistics, such as summarization and tabulation, to more sophisticated predictive techniques, such as linear and logistic regression, clustering, classification, and text analyticsUses R, Python, Jamovi and JASP interfaces, and the Orange3 data mining interfaceIncludes companion files with the case study files from the book, solution spreadsheets, data sets, etc.
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Essentials of Pricing Analytics
This second edition of Essentials of Pricing Analytics provides a comprehensive introduction to pricing as a tactical business function and is a practical toolbox for implementing solutions to diverse pricing challenges.Beyond theoretical perspectives, this second edition of Essentials of Pricing Analytics demonstrates practical implementations of analytical tools using global case studies. Covering fundamental pricing theory, break-even analysis, price sensitivity, optimization techniques, revenue management, and ethical considerations, this updated edition features new content on subscription models, two-sided markets, and AI applications in pricing strategy. It also expands on implementation examples to include R and Python alongside Excel.This accessible text is ideal for advanced undergraduate, MBA, and postgraduate students of pricing strategy, entrepreneurship, marketing strategy, and operations, as well as practitioners seeking effective methods to implement pricing strategies and maximize profits.Online resources include Excel templates, R and Python scripts for most examples covered in the book, a comprehensive test bank, and PowerPoint slides for each chapter.
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Business Analytics
This new textbook focuses on how data and analytics can be used to help inform organisational decision-making across the business by complementing human judgement.Taking a highly practical approach, it covers major use cases for analytics across different business areas, including marketing analytics, HR analytics, operational analytics and financial analytics. This concise and readable book grounds discussion in the fundamentals of data, analytics and data visualisation, and in an understanding of the legal and ethical responsibilities that come with working with data.Key features include:• Analytics in Practice vignettes show how data and analytics have been applied in real organisations• Video interviews with industry professionals bring examples to life• A running case study and accompanying dataset allow you to apply what you have learntSuitable for undergraduate and postgraduate students studying business analytics.Mary Ellen Gordon is Senior Professional Teaching Fellow/Senior Lecturer in the School of Information Systems at the Victoria University of Wellington, New Zealand.
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Learning IPython for Interactive Computing and Data Visualization -
Get started with Python for data analysis and numerical computing in the Jupyter notebookAbout This Book• Learn the basics of Python in the Jupyter Notebook• Analyze and visualize data with pandas, NumPy, matplotlib, and seaborn• Perform highly-efficient numerical computations with Numba, Cython, and ipyparallelWho This Book Is ForThis book targets students, teachers, researchers, engineers, analysts, journalists, hobbyists, and all data enthusiasts who are interested in analyzing and visualizing real-world datasets. If you are new to programming and data analysis, this book is exactly for you. If you're already familiar with another language or analysis software, you will also appreciate this introduction to the Python data analysis platform. Finally, there are more technical topics for advanced readers. No prior experience is required; this book contains everything you need to know.What You Will Learn• Install Anaconda and code in Python in the Jupyter Notebook• Load and explore datasets interactively• Perform complex data manipulations effectively with pandas• Create engaging data visualizations with matplotlib and seaborn• Simulate mathematical models with NumPy• Visualize and process images interactively in the Jupyter Notebook with scikit-image• Accelerate your code with Numba, Cython, and IPython.parallel• Extend the Notebook interface with HTML, JavaScript, and D3In DetailPython is a user-friendly and powerful programming language. IPython offers a convenient interface to the language and its analysis libraries, while the Jupyter Notebook is a rich environment well-adapted to data science and visualization. Together, these open source tools are widely used by beginners and experts around the world, and in a huge variety of fields and endeavors.This book is a beginner-friendly guide to the Python data analysis platform. After an introduction to the Python language, IPython, and the Jupyter Notebook, you will learn how to analyze and visualize data on real-world examples, how to create graphical user interfaces for image processing in the Notebook, and how to perform fast numerical computations for scientific simulations with NumPy, Numba, Cython, and ipyparallel. By the end of this book, you will be able to perform in-depth analyses of all sorts of data.Style and approachThis is a hands-on beginner-friendly guide to analyze and visualize data on real-world examples with Python and the Jupyter Notebook.
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Data Mining and Business Analytics with R
Collecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools. Data Mining and Business Analytics with R utilizes the open source software R for the analysis, exploration, and simplification of large high-dimensional data sets. As a result, readers are provided with the needed guidance to model and interpret complicated data and become adept at building powerful models for prediction and classification. Highlighting both underlying concepts and practical computational skills, Data Mining and Business Analytics with R begins with coverage of standard linear regression and the importance of parsimony in statistical modeling. The book includes important topics such as penalty-based variable selection (LASSO); logistic regression; regression and classification trees; clustering; principal components and partial least squares; and the analysis of text and network data. In addition, the book presents: A thorough discussion and extensive demonstration of the theory behind the most useful data mining toolsIllustrations of how to use the outlined concepts in real-world situationsReadily available additional data sets and related R code allowing readers to apply their own analyses to the discussed materialsNumerous exercises to help readers with computing skills and deepen their understanding of the material Data Mining and Business Analytics with R is an excellent graduate-level textbook for courses on data mining and business analytics. The book is also a valuable reference for practitioners who collect and analyze data in the fields of finance, operations management, marketing, and the information sciences.
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AI FINANCE: FINANCIAL MODELING - AI DRIVER® SERIES
This book focuses on bridging the gap between the widespread use of artificial intelligence tools and the absence of systematic training in finance education. It introduces the DRIVER® methodology, a structured six stage framework — Define&Discover, Represent, Implement, Validate, Evolve, and Reflect — designed to guide readers in professional AI collaboration while maintaining rigorous foundations in financial theory.Validated in classroom settings at Purdue University, where students described it as their most useful course, the DRIVER® approach equips learners with both conceptual depth and practical skills. Readers progressively build a portfolio of AI powered financial models, ranging from portfolio optimization to derivatives pricing, while cultivating the systematic thinking that distinguishes professionals from casual experimenters.The text is designed for a broad audience: finance students seeking to strengthen their analytical toolkit, professionals aiming to advance their modeling expertise, and institutions implementing AI transformation strategies. Importantly, no prior programming experience is required. The curriculum introduces Python fundamentals before advancing to sophisticated financial applications, ensuring accessibility while delivering immediate real world value.By combining financial theory with structured AI methodology, this book provides a transformative resource for modern finance education and practice. It reshapes how readers approach modeling, risk analysis, and decision making in an AI driven era, offering a comprehensive foundation for the next generation of financial professionals.
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Machine Learning in Finance
This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance.Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher's perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance.
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Data Analytics and AI for Quantitative Risk Assessment and Financial Computation
In today's fast-paced financial landscape, professionals face an uphill battle in effectively integrating data analytics and artificial intelligence (AI) into quantitative risk assessment and financial computation. The constantly increasing volume, velocity, and variety of data generated by digital transactions, market exchanges, and social media platforms offer unparalleled financial analysis and decision-making opportunities. However, professionals need sophisticated AI technologies and data analytics methodologies to harness this data for predictive modeling, risk assessment, and algorithmic trading. Navigating this complex terrain can be daunting, and a comprehensive guide that bridges theory and practice is necessary. Data Analytics and AI for Quantitative Risk Assessment and Financial Computation is an all-encompassing reference for finance professionals, risk managers, data scientists, and students seeking to leverage the transformative power of AI and data analytics in finance. The book encapsulates this integration's theoretical underpinnings, practical applications, challenges, and future directions, empowering readers to enhance their analytical capabilities, make informed decisions, and stay ahead in the competitive financial landscape. The book provides a structured approach that covers foundational topics in quantitative risk assessment, data analytics, and AI, providing a roadmap for professionals to navigate the complexities of integrating AI and data analytics into financial practices. The book explores specific applications and methodologies, including machine learning algorithms for economic modeling and AI-driven strategies for risk management, providing readers with practical insights and strategies for success in the AI-driven financial future. With this book as a guide, professionals can confidently embrace the power of AI and data analytics to stay ahead in the ever-evolving economic landscape.
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Python Tools for Data Scientists
As part of the best-selling Pocket Primer series, this book is designed to provide an introduction to Python tools which are used by data scientists. It includes coverage of fundamental aspects of NumPy and Pandas, how to write regular expressions, and how to perform data cleaning tasks. The first chapter contains a quick tour of basic Python, followed by a chapter introducing NumPy, and a chapter on Pandas. Chapter 4 provides a high-level view of Sklearn and SciPy. Chapter 5 contains an assortment of data cleaning tasks that are solved via Python and the awk programming language. Chapter 6 delves into data visualization with Matplotlib, Seaborn, and Bokeh. Next, one appendix explores issues that can arise with data, followed by an appendix on awk. Numerous code samples are used to illustrate concepts. Companion files with source code are available for downloading from the publisher. FEATURES:Introduces Python, NumPy, Sklearn, SciPy, and awk.Covers data cleaning tasks and data visualization.Features numerous code samples throughout.Includes companion files with source code .
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Python Projects for Beginners
Immerse yourself in learning Python and introductory data analytics with this book’s project-based approach. Through the structure of a ten-week coding bootcamp course, you’ll learn key concepts and gain hands-on experience through weekly projects.Each chapter in this book is presented as a full week of topics, with Monday through Thursday covering specific concepts, leading up to Friday, when you are challenged to create a project using the skills learned throughout the week. Topics include Python basics and essential intermediate concepts such as list comprehension, generators and iterators, understanding algorithmic complexity, and data analysis with pandas. From beginning to end, this book builds up your abilities through exercises and challenges, culminating in your solid understanding of Python.Challenge yourself with the intensity of a coding bootcamp experience or learn at your own pace. With this hands-on learning approach, you will gain the skills you need to jumpstart a new career in programming or further your current one as a software developer. What You Will LearnUnderstand beginning and more advanced concepts of the Python languageBe introduced to data analysis using pandas, the Python Data Analysis libraryWalk through the process of interviewing and answering technical questionsCreate real-world applications with the Python languageLearn how to use Anaconda, Jupyter Notebooks, and the Python ShellWho This Book Is ForThose trying to jumpstart a new career into programming, and those already in the software development industry and would like to learn Python programming.
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Data Analytics and AI for Quantitative Risk Assessment and Financial Computation
In today's fast-paced financial landscape, professionals face an uphill battle in effectively integrating data analytics and artificial intelligence (AI) into quantitative risk assessment and financial computation. The constantly increasing volume, velocity, and variety of data generated by digital transactions, market exchanges, and social media platforms offer unparalleled financial analysis and decision-making opportunities. However, professionals need sophisticated AI technologies and data analytics methodologies to harness this data for predictive modeling, risk assessment, and algorithmic trading. Navigating this complex terrain can be daunting, and a comprehensive guide that bridges theory and practice is necessary. Data Analytics and AI for Quantitative Risk Assessment and Financial Computation is an all-encompassing reference for finance professionals, risk managers, data scientists, and students seeking to leverage the transformative power of AI and data analytics in finance. The book encapsulates this integration's theoretical underpinnings, practical applications, challenges, and future directions, empowering readers to enhance their analytical capabilities, make informed decisions, and stay ahead in the competitive financial landscape. The book provides a structured approach that covers foundational topics in quantitative risk assessment, data analytics, and AI, providing a roadmap for professionals to navigate the complexities of integrating AI and data analytics into financial practices. The book explores specific applications and methodologies, including machine learning algorithms for economic modeling and AI-driven strategies for risk management, providing readers with practical insights and strategies for success in the AI-driven financial future. With this book as a guide, professionals can confidently embrace the power of AI and data analytics to stay ahead in the ever-evolving economic landscape.
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Exploratory Data Analysis with Python Cookbook
Extract valuable insights from data by leveraging various analysis and visualization techniques with this comprehensive guide Purchase of the print or Kindle book includes a free PDF eBookKey FeaturesGain practical experience in conducting EDA on a single variable of interest in PythonLearn the different techniques for analyzing and exploring tabular, time series, and textual data in PythonGet well versed in data visualization using leading Python libraries like Matplotlib and seabornBook DescriptionIn today's data-centric world, the ability to extract meaningful insights from vast amounts of data has become a valuable skill across industries. Exploratory Data Analysis (EDA) lies at the heart of this process, enabling us to comprehend, visualize, and derive valuable insights from various forms of data.This book is a comprehensive guide to Exploratory Data Analysis using the Python programming language. It provides practical steps needed to effectively explore, analyze, and visualize structured and unstructured data. It offers hands-on guidance and code for concepts such as generating summary statistics, analyzing single and multiple variables, visualizing data, analyzing text data, handling outliers, handling missing values and automating the EDA process. It is suited for data scientists, data analysts, researchers or curious learners looking to gain essential knowledge and practical steps for analyzing vast amounts of data to uncover insights.Python is an open-source general purpose programming language which is used widely for data science and data analysis given its simplicity and versatility. It offers several libraries which can be used to clean, analyze, and visualize data. In this book, we will explore popular Python libraries such as Pandas, Matplotlib, and Seaborn and provide workable code for analyzing data in Python using these libraries.By the end of this book, you will have gained comprehensive knowledge about EDA and mastered the powerful set of EDA techniques and tools required for analyzing both structured and unstructured data to derive valuable insights.What you will learnPerform EDA with leading python data visualization librariesExecute univariate, bivariate and multivariate analysis on tabular dataUncover patterns and relationships within time series dataIdentify hidden patterns within textual dataLearn different techniques to prepare data for analysisOvercome challenge of outliers and missing values during data analysisLeverage automated EDA for fast and efficient analysisWho this book is forWhether you are a data analyst, data scientist, researcher or a curious learner looking to analyze structured and unstructured data, this book will appeal to you. It aims to empower you with essential knowledge and practical skills for analyzing and visualizing data to uncover insights.It covers several EDA concepts and provides hands-on instructions on how these can be applied using various Python libraries. Familiarity with basic stati
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R 531.00
Python Programming for Data Analysis
This textbook grew out of notes for the ECE143 Programming for Data Analysis class that the author has been teaching at University of California, San Diego, which is a requirement for both graduate and undergraduate degrees in Machine Learning and Data Science. This book is ideal for readers with some Python programming experience. The book covers key language concepts that must be understood to program effectively, especially for data analysis applications. Certain low-level language features are discussed in detail, especially Python memory management and data structures. Using Python effectively means taking advantage of its vast ecosystem. The book discusses Python package management and how to use third-party modules as well as how to structure your own Python modules. The section on object-oriented programming explains features of the language that facilitate common programming patterns.After developing the key Python language features, the book moves on to third-party modules that are foundational for effective data analysis, starting with Numpy. The book develops key Numpy concepts and discusses internal Numpy array data structures and memory usage. Then, the author moves onto Pandas and details its many features for data processing and alignment. Because strong visualizations are important for communicating data analysis, key modules such as Matplotlib are developed in detail, along with web-based options such as Bokeh, Holoviews, Altair, and Plotly.The text is sprinkled with many tricks-of-the-trade that help avoid common pitfalls. The author explains the internal logic embodied in the Python language so that readers can get into the Python mindset and make better design choices in their codes, which is especially helpful for newcomers to both Python and data analysis. To get the most out of this book, open a Python interpreter and type along with the many code samples.
R 3,079.26
