Search results for "data analytics for finance using python"
Items we need to order for you
Data Science and Risk Analytics in Finance and Insurance
This book presents statistics and data science methods for risk analytics in quantitative finance and insurance. Part I covers the background, financial models, and data analytical methods for market risk, credit risk, and operational risk in financial instruments, as well as models of risk premium and insolvency in insurance contracts. Part II provides an overview of machine learning (including supervised, unsupervised, and reinforcement learning), Monte Carlo simulation, and sequential analysis techniques for risk analytics. In Part III, the book offers a non-technical introduction to four key areas in financial technology: artificial intelligence, blockchain, cloud computing, and big data analytics.Key Features:Provides a comprehensive and in-depth overview of data science methods for financial and insurance risks.Unravels bandits, Markov decision processes, reinforcement learning, and their interconnections.Promotes sequential surveillance and predictive analytics for abrupt changes in risk factors.Introduces the ABCDs of FinTech: Artificial intelligence, blockchain, cloud computing, and big data analytics.Includes supplements and exercises to facilitate deeper comprehension.
R 4,486.04
Data Analytics
Data analytics is creeping into the lexicon of our daily language. This book gives the reader a perspective as to the overall data analytics skill set, startingwith a primer on statistics, and works toward the application of those methods.There are a variety of formulas and algorithms used in the data analyticsprocess. These formulas can be plugged into whatever software application thereader uses to obtain the answer they need. There are several demonstrations ofthis process to provide straightforward instruction as to how to bring data analytics skills to your critical thinking. This bookpresents a variety of methods and techniques, as well as case studies, toenrich the knowledge of data analytics for project managers, systems engineers,and cybersecurity professionals. It separates the case studies so that eachprofession can practice some straightforward data analytics specific to their fields. The main purpose of this text is to refresh the statistical knowledgenecessary to build models for data analytics. Along with that, this bookencompasses the analytics thinking that is essential to all three professions.FEATURES:Provides straightforward instruction on data analytics methodsIncludes methods, techniques, and case studies for project managers, systems engineers, and cybersecurity professionalsRefreshes the statistical knowledgeneeded to bring data analytics into your skillset and decision-makingFocuses on getting readers up to speed quickly and efficiently to be able to see the impact of data analytics and analytical thinking
R 1,185.00
Practical Data Analysis Using Jupyter Notebook
Understand data analysis concepts to make accurate decisions based on data using Python programming and Jupyter NotebookKey FeaturesFind out how to use Python code to extract insights from data using real-world examplesWork with structured data and free text sources to answer questions and add value using dataPerform data analysis from scratch with the help of clear explanations for cleaning, transforming, and visualizing dataBook DescriptionData literacy is the ability to read, analyze, work with, and argue using data. Data analysis is the process of cleaning and modeling your data to discover useful information. This book combines these two concepts by sharing proven techniques and hands-on examples so that you can learn how to communicate effectively using data.After introducing you to the basics of data analysis using Jupyter Notebook and Python, the book will take you through the fundamentals of data. Packed with practical examples, this guide will teach you how to clean, wrangle, analyze, and visualize data to gain useful insights, and you'll discover how to answer questions using data with easy-to-follow steps.Later chapters teach you about storytelling with data using charts, such as histograms and scatter plots. As you advance, you'll understand how to work with unstructured data using natural language processing (NLP) techniques to perform sentiment analysis. All the knowledge you gain will help you discover key patterns and trends in data using real-world examples. In addition to this, you will learn how to handle data of varying complexity to perform efficient data analysis using modern Python libraries.By the end of this book, you'll have gained the practical skills you need to analyze data with confidence.What you will learnUnderstand the importance of data literacy and how to communicate effectively using dataFind out how to use Python packages such as NumPy, pandas, Matplotlib, and the Natural Language Toolkit (NLTK) for data analysisWrangle data and create DataFrames using pandasProduce charts and data visualizations using time-series datasetsDiscover relationships and how to join data together using SQLUse NLP techniques to work with unstructured data to create sentiment analysis modelsDiscover patterns in real-world datasets that provide accurate insightsWho this book is forThis book is for aspiring data analysts and data scientists looking for hands-on tutorials and real-world examples to understand data analysis concepts using SQL, Python, and Jupyter Notebook. Anyone looking to evolve their skills to become data-driven personally and professionally will also find this book useful. No prior knowledge of data analysis or programming is required to get started with this book.
R 1,335.00
R 1,099.00
R 763.00
R 730.10
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.
R 2,125.00
Data Analysis for Business, Economics, and Policy
This textbook provides future data analysts with the tools, methods, and skills needed to answer data-focused, real-life questions; to carry out data analysis; and to visualize and interpret results to support better decisions in business, economics, and public policy. Data wrangling and exploration, regression analysis, machine learning, and causal analysis are comprehensively covered, as well as when, why, and how the methods work, and how they relate to each other. As the most effective way to communicate data analysis, running case studies play a central role in this textbook. Each case starts with an industry-relevant question and answers it by using real-world data and applying the tools and methods covered in the textbook. Learning is then consolidated by 360 practice questions and 120 data exercises. Extensive online resources, including raw and cleaned data and codes for all analysis in Stata, R, and Python, can be found at www.gabors-data-analysis.com.
R 7,541.00
Ai & Quantum Computing For Finance & Insurance: Fortunes And Challenges For China And America
This book offers a framework and analysis for the current technological landscape between the United States and China across the financial and insurance sectors as well as emerging technologies such as AI, Blockchain, Cloud and Data Analytics and Quantum Computing (ABCDQ). Based on original lecture slides used by the authors, the book presents contemporary and critical views of emergent technologies for a wide spectrum of readers from CEOs to university lecturers to students. The narrative aims to help readers upgrade their technology literacy and to overcome the fear of AI posed by our lizard brain.
R 1,664.74
R 1,139.00
Financial Valuation And Econometrics (Third Edition)
This book provides a comprehensive introduction to financial valuation and financial data analysis using econometric methods. It is intended for advanced finance undergraduates and graduate students, offering detailed guidance on applying econometric techniques to real-world financial problems. Most chapters in the book would contain one or more finance application examples where finance concepts and theory are taught. This book weaves together the three important domains of financial valuation theory, econometrics modelling, and the empirical analyses of financial data using common programming languages and statistical software. The pedagogical approach in this book employs various suitable econometric methods such as multiple linear and panel regressions, time series analyses, cointegration, maximum likelihood, the generalized method of moments to perform estimation and testing of popular financial models using appropriate financial data. It provides for very effective learning by a finance professional or student who wants to be well equipped in both theory and the ability to investigate the models using appropriate econometrics and statistical techniques. Learning these econometric methods is also important in complementing prediction and classification techniques in AI and machine learning.
R 5,788.00
Hands-On Prescriptive Analytics
Business decisions in any context—operational, tactical, or strategic—can have considerable consequences. Whether the outcome is positive and rewarding or negative and damaging to the business, its employees, and stakeholders is unknown when action is approved. These decisions are usually made under the proverbial cloud of uncertainty.With this practical guide, data analysts, data scientists, and business analysts will learn why and how maximizing positive consequences and minimizing negative ones requires three forms of rich information: Descriptive analytics explores the results from an action—what has already happened. Predictive analytics focuses on what could happen. The third, prescriptive analytics, informs us what should happen in the future.While all three are important for decision-makers, the primary focus of this book is on the third: prescriptive analytics. Author Walter R. Paczkowski, Ph.D. shows you:The distinction among descriptive, predictive, and prescriptive analyticsHow predictive analytics produces a menu of action optionsHow prescriptive analytics narrows the menu of action optionsThe forms of prescriptive analytics: eight prescriptive methodsTwo broad classes of these methods: non-stochastic and stochasticHow to develop prescriptive analyses for action recommendationsWays to use an appropriate tool-set in Python
R 1,484.00
Data Science for Economics and Finance
This open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models.The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis. This book is relevant to all stakeholders involved in digital and data-intensive research in economics and finance, helping them to understand the main opportunities and challenges, become familiar with the latest methodological findings, and learn how to use and evaluate the performances of novel tools and frameworks. It primarily targets data scientists and business analysts exploiting data science technologies, and it will also be a useful resource to research students in disciplines and courses related to these topics. Overall, readers will learn modern and effective data science solutions to create tangible innovations for economic and financial applications.
R 1,832.00
Data Science for Economics and Finance
This open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models.The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis. This book is relevant to all stakeholders involved in digital and data-intensive research in economics and finance, helping them to understand the main opportunities and challenges, become familiar with the latest methodological findings, and learn how to use and evaluate the performances of novel tools and frameworks. It primarily targets data scientists and business analysts exploiting data science technologies, and it will also be a useful resource to research students in disciplines and courses related to these topics. Overall, readers will learn modern and effective data science solutions to create tangible innovations for economic and financial applications.
R 2,198.00
Big Data in Finance: Transforming the Financial Landscape
This book uncovers the potential of machine learning, artificial intelligence, and advanced analytics to empower financial institutions to stay competitive in an ever-evolving market. In the age of digital transformation, big data is revolutionizing the financial industry, reshaping how businesses understand markets, manage risks, and make strategic decisions. "Big Data in Finance: Transforming the Financial Landscape" offers a comprehensive exploration of the cutting-edge technologies and analytics driving this change, making it an essential resource for professionals, scholars, business leaders, and enthusiasts alike.From enhancing operational efficiency to enabling more accurate forecasting, this book delves deep into how data-driven insights are transforming traditional financial models.With practical insights, real-world applications, and contributions from industry leaders, this book provides a balanced perspective on both the opportunities and challenges of data-driven finance. It addresses key ethical considerations, regulatory concerns, and future trends, offering readers a holistic understanding of the dynamic intersection of technology and finance.Discover how big data is shaping the future of finance, and gain the knowledge you need to harness its full potential. Whether you're looking to innovate, optimize, or simply stay informed, "Big Data in Finance: Transforming the Financial Landscape" is your roadmap to success in the new era of digital finance.
R 9,767.11
Data Wrangling with Python
Simplify your ETL processes with these hands-on data hygiene tips, tricks, and best practices.Key FeaturesFocus on the basics of data wranglingStudy various ways to extract the most out of your data in less timeBoost your learning curve with bonus topics like random data generation and data integrity checksBook DescriptionFor data to be useful and meaningful, it must be curated and refined. Data Wrangling with Python teaches you the core ideas behind these processes and equips you with knowledge of the most popular tools and techniques in the domain. The book starts with the absolute basics of Python, focusing mainly on data structures. It then delves into the fundamental tools of data wrangling like NumPy and Pandas libraries. You’ll explore useful insights into why you should stay away from traditional ways of data cleaning, as done in other languages, and take advantage of the specialized pre-built routines in Python. This combination of Python tips and tricks will also demonstrate how to use the same Python backend and extract/transform data from an array of sources including the Internet, large database vaults, and Excel financial tables. To help you prepare for more challenging scenarios, you’ll cover how to handle missing or wrong data, and reformat it based on the requirements from the downstream analytics tool. The book will further help you grasp concepts through real-world examples and datasets. By the end of this book, you will be confident in using a diverse array of sources to extract, clean, transform, and format your data efficiently. What you will learnUse and manipulate complex and simple data structuresHarness the full potential of DataFrames and numpy.array at run timePerform web scraping with BeautifulSoup4 and html5libExecute advanced string search and manipulation with RegEXHandle outliers and perform data imputation with PandasUse descriptive statistics and plotting techniquesPractice data wrangling and modeling using data generation techniquesWho this book is forData Wrangling with Python is designed for developers, data analysts, and business analysts who are keen to pursue a career as a full-fledged data scientist or analytics expert. Although, this book is for beginners, prior working knowledge of Python is necessary to easily grasp the concepts covered here. It will also help to have rudimentary knowledge of relational database and SQL.
R 1,498.00
Big Data Analytics
A handy reference guide for data analysts and data scientists to help to obtain value from big data analytics using Spark on Hadoop clustersAbout This Book• This book is based on the latest 2.0 version of Apache Spark and 2.7 version of Hadoop integrated with most commonly used tools.• Learn all Spark stack components including latest topics such as DataFrames, DataSets, GraphFrames, Structured Streaming, DataFrame based ML Pipelines and SparkR.• Integrations with frameworks such as HDFS, YARN and tools such as Jupyter, Zeppelin, NiFi, Mahout, HBase Spark Connector, GraphFrames, H2O and Hivemall.Who This Book Is ForThough this book is primarily aimed at data analysts and data scientists, it will also help architects, programmers, and practitioners. Knowledge of either Spark or Hadoop would be beneficial. It is assumed that you have basic programming background in Scala, Python, SQL, or R programming with basic Linux experience. Working experience within big data environments is not mandatory.What You Will Learn• Find out and implement the tools and techniques of big data analytics using Spark on Hadoop clusters with wide variety of tools used with Spark and Hadoop• Understand all the Hadoop and Spark ecosystem components• Get to know all the Spark components: Spark Core, Spark SQL, DataFrames, DataSets, Conventional and Structured Streaming, MLLib, ML Pipelines and Graphx• See batch and real-time data analytics using Spark Core, Spark SQL, and Conventional and Structured Streaming• Get to grips with data science and machine learning using MLLib, ML Pipelines, H2O, Hivemall, Graphx, SparkR and Hivemall.In DetailBig Data Analytics book aims at providing the fundamentals of Apache Spark and Hadoop. All Spark components – Spark Core, Spark SQL, DataFrames, Data sets, Conventional Streaming, Structured Streaming, MLlib, Graphx and Hadoop core components – HDFS, MapReduce and Yarn are explored in greater depth with implementation examples on Spark + Hadoop clusters.It is moving away from MapReduce to Spark. So, advantages of Spark over MapReduce are explained at great depth to reap benefits of in-memory speeds. DataFrames API, Data Sources API and new Data set API are explained for building Big Data analytical applications. Real-time data analytics using Spark Streaming with Apache Kafka and HBase is covered to help building streaming applications. New Structured streaming concept is explained with an IOT (Internet of Things) use case. Machine learning techniques are covered using MLLib, ML Pipelines and SparkR and Graph Analytics are covered with GraphX and GraphFrames components of Spark.Readers will also get an opportunity to get started with web based notebooks such as Jupyter, Apache Zeppelin and data flow tool Apache NiFi to analyze and visualize data.Style and approachThis step-by-step pragmatic guide will make life easy no matter what your level of experience. You will deep dive into Apache Spark on Hadoop clusters through ample exciting real-l
R 1,889.00
R 519.00
Recent Advancements in Computational Finance and Business Analytics
Recent Advancements of Computational Finance and Business Analytics provide a comprehensive overview of the cutting-edge advancements in this dynamic field. By embracing computational finance and business analytics, organizations can gain a competitive edge in an increasingly data-driven and complex business environment.This book has explored the latest developments and breakthroughs in this rapidly evolving domain, providing a comprehensive overview of the current state of computational finance and business analytics. It covers the following dimensions of this domains:Business AnalyticsFinancial AnalyticsHuman Resource AnalyticsMarketing Analytics
R 9,158.00
R 580.00
R 428.00
Python Data Science Handbook
Python is a first-class tool for many researchers, primarily because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the new edition of Python Data Science Handbook do you get them all—IPython, NumPy, pandas, Matplotlib, Scikit-Learn, and other related tools.Working scientists and data crunchers familiar with reading and writing Python code will find the second edition of this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.With this handbook, you'll learn how:IPython and Jupyter provide computational environments for scientists using PythonNumPy includes the ndarray for efficient storage and manipulation of dense data arraysPandas contains the DataFrame for efficient storage and manipulation of labeled/columnar dataMatplotlib includes capabilities for a flexible range of data visualizationsScikit-learn helps you build efficient and clean Python implementations of the most important and established machine learning algorithms
R 1,978.00
Analytics in Finance and Risk Management
This book presents contemporary issues and challenges in finance and risk management in a time of rapid transformation due to technological advancements. It includes research articles based on financial and economic data and intends to cover the emerging role of analytics in financial management, asset management, and risk management.Analytics in Finance and Risk Management covers statistical techniques for data analysis in finance. It explores applications in finance and risk management, covering empirical properties of financial systems. It addresses data science involving the study of statistical and computational models and includes basic and advanced concepts. The chapters incorporate the latest methodologies and challenges facing financial and risk management and illustrate related issues and their implications in the real world. The primary users of this book will include researchers, academicians, postgraduate students, professionals in engineering and business analytics, managers, consultants, and advisors in IT firms, financial markets, and services domains.
R 9,967.58
R 416.00
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.
R 4,361.87
R 1,282.00
Big Data and Business Analytics
"The chapters in this volume offer useful case studies, technical roadmaps, lessons learned, and a few prescriptions to ‘do this, avoid that.’" —From the Foreword by Joe LaCugna, Ph.D., Enterprise Analytics and Business Intelligence, Starbucks Coffee CompanyWith the growing barrage of "big data," it becomes vitally important for organizations to make sense of this data and information in a timely and effective way. That’s where analytics come into play. Research shows that organizations that use business analytics to guide their decision making are more productive and experience higher returns on equity. Big Data and Business Analytics helps you quickly grasp the trends and techniques of big data and business analytics to make your organization more competitive.Packed with case studies, this book assembles insights from some of the leading experts and organizations worldwide. Spanning industry, government, not-for-profit organizations, and academia, they share valuable perspectives on big data domains such as cybersecurity, marketing, emergency management, healthcare, finance, and transportation.Understand the trends, potential, and challenges associated with big data and business analyticsGet an overview of machine learning, advanced statistical techniques, and other predictive analytics that can help you solve big data issuesLearn from VPs of Big Data/Insights & Analytics via case studies of Fortune 100 companies, government agencies, universities, and not-for-profitsBig data problems are complex. This book shows you how to go from being data-rich to insight-rich, improving your decision making and creating competitive advantage.Author Jay Liebowitz recently had an article published in The World Financial Review. www.worldfinancialreview.com/?p=1904
R 4,460.32
R 1,679.00
R 519.00
Recent Developments in Computational Finance and Business Analytics
Recent Advancements in Computational Finance and Business Analytics captures the transformation reshaping finance, business, and decision-making. Structured around Financial Analytics, Business Analytics, and HR & Marketing Analytics, this volume presents cutting-edge research and real-world applications. Topics include machine learning in financial forecasting, AI-driven customer segmentation, and blockchain-enabled supply chains. Blending rigorous methods with actionable insights, it offers value to researchers, analysts, students, and policymakers. As industries embrace data-driven innovation, this book serves as both a reference and a roadmap for navigating the digital economy through strategic and intelligent analytics.
R 9,158.00
Machine Learning for Time-Series with Python
Get better insights from time-series data and become proficient in model performance analysisKey FeaturesExplore popular and modern machine learning methods including the latest online and deep learning algorithmsLearn to increase the accuracy of your predictions by matching the right model with the right problemMaster time series via real-world case studies on operations management, digital marketing, finance, and healthcareBook DescriptionThe Python time-series ecosystem is huge and often quite hard to get a good grasp on, especially for time-series since there are so many new libraries and new models. This book aims to deepen your understanding of time series by providing a comprehensive overview of popular Python time-series packages and help you build better predictive systems. Machine Learning for Time-Series with Python starts by re-introducing the basics of time series and then builds your understanding of traditional autoregressive models as well as modern non-parametric models. By observing practical examples and the theory behind them, you will become confident with loading time-series datasets from any source, deep learning models like recurrent neural networks and causal convolutional network models, and gradient boosting with feature engineering. This book will also guide you in matching the right model to the right problem by explaining the theory behind several useful models. You’ll also have a look at real-world case studies covering weather, traffic, biking, and stock market data. By the end of this book, you should feel at home with effectively analyzing and applying machine learning methods to time-series.What you will learnUnderstand the main classes of time series and learn how to detect outliers and patternsChoose the right method to solve time-series problemsCharacterize seasonal and correlation patterns through autocorrelation and statistical techniquesGet to grips with time-series data visualizationUnderstand classical time-series models like ARMA and ARIMAImplement deep learning models, like Gaussian processes, transformers, and state-of-the-art machine learning modelsBecome familiar with many libraries like Prophet, XGboost, and TensorFlowWho this book is forThis book is ideal for data analysts, data scientists, and Python developers who want instantly useful and practical recipes to implement today, and a comprehensive reference book for tomorrow. Basic knowledge of the Python Programming language is a must, while familiarity with statistics will help you get the most out of this book.
R 1,889.00
Data Science for Marketing Analytics
Turbocharge your marketing plans by making the leap from simple descriptive statistics in Excel to sophisticated predictive analytics with the Python programming languageKey FeaturesUse data analytics and machine learning in a sales and marketing contextGain insights from data to make better business decisionsBuild your experience and confidence with realistic hands-on practiceBook DescriptionUnleash the power of data to reach your marketing goals with this practical guide to data science for business.This book will help you get started on your journey to becoming a master of marketing analytics with Python. You'll work with relevant datasets and build your practical skills by tackling engaging exercises and activities that simulate real-world market analysis projects.You'll learn to think like a data scientist, build your problem-solving skills, and discover how to look at data in new ways to deliver business insights and make intelligent data-driven decisions.As well as learning how to clean, explore, and visualize data, you'll implement machine learning algorithms and build models to make predictions. As you work through the book, you'll use Python tools to analyze sales, visualize advertising data, predict revenue, address customer churn, and implement customer segmentation to understand behavior.By the end of this book, you'll have the knowledge, skills, and confidence to implement data science and machine learning techniques to better understand your marketing data and improve your decision-making.What you will learnLoad, clean, and explore sales and marketing data using pandasForm and test hypotheses using real data sets and analytics toolsVisualize patterns in customer behavior using MatplotlibUse advanced machine learning models like random forest and SVMUse various unsupervised learning algorithms for customer segmentationUse supervised learning techniques for sales predictionEvaluate and compare different models to get the best outcomesOptimize models with hyperparameter tuning and SMOTEWho this book is forThis marketing book is for anyone who wants to learn how to use Python for cutting-edge marketing analytics. Whether you're a developer who wants to move into marketing, or a marketing analyst who wants to learn more sophisticated tools and techniques, this book will get you on the right path.Basic prior knowledge of Python and experience working with data will help you access this book more easily.
R 1,335.00
Learn Data Analysis with Python
Get started using Python in data analysis with this compact practical guide. This book includes three exercises and a case study on getting data in and out of Python code in the right format. Learn Data Analysis with Python also helps you discover meaning in the data using analysis and shows you how to visualize it. Each lesson is, as much as possible, self-contained to allow you to dip in and out of the examples as your needs dictate. If you are already using Python for data analysis, you will find a number of things that you wish you knew how to do in Python. You can then take these techniques and apply them directly to your own projects.If you aren’t using Python for data analysis, this book takes you through the basics at the beginning to give you a solid foundation in the topic. As you work your way through the book you will have a better of idea of how to use Python for data analysis when you are finished.What You Will LearnGet data into and out of Python codePrepare the data and its formatFind the meaning of the dataVisualize the data using iPythonWho This Book Is For Those who want to learn data analysis using Python. Some experience with Python is recommended but not required, as is some prior experience with data analysis or data science.
R 1,067.67
R 671.00
Practical Python Data Wrangling and Data Quality
The world around us is full of data that holds unique insights and valuable stories, and this book will help you uncover them. Whether you already work with data or want to learn more about its possibilities, the examples and techniques in this practical book will help you more easily clean, evaluate, and analyze data so that you can generate meaningful insights and compelling visualizations.Complementing foundational concepts with expert advice, author Susan E. McGregor provides the resources you need to extract, evaluate, and analyze a wide variety of data sources and formats, along with the tools to communicate your findings effectively. This book delivers a methodical, jargon-free way for data practitioners at any level, from true novices to seasoned professionals, to harness the power of data.Use Python 3.8+ to read, write, and transform data from a variety of sourcesUnderstand and use programming basics in Python to wrangle data at scaleOrganize, document, and structure your code using best practicesCollect data from structured data files, web pages, and APIsPerform basic statistical analyses to make meaning from datasetsVisualize and present data in clear and compelling ways
R 1,824.00
Basic Computational Techniques for Data Analysis
This book is a multi-stage learning resource that takes readers from basic data handling and software orientation to statistical analysis and financial applications using MS Excel and R. Through a gradual, step-by-step approach, it equips readers with the essential skills needed to understand, analyse, and interpret data in today’s data-driven world.Using clear illustrations, the book enables readers to organise and manage data, perform analytical computations, create meaningful visualisations, and draw conclusions. It introduces core concepts in research, data analysis, and economic and financial decision-making, demonstrating their relevance in academic, professional, and everyday contexts. This book enables readers to:• Work confidently with MS Excel and R for efficient data handling and analysis • Present and visualise data using charts, tables, and Pivot Tables • Apply statistical techniques such as descriptive statistics, correlation, regression, and hypothesis testing • Perform financial analysis using concepts such as time value of money, compounding, NPV, IRR, and EMI calculations By combining concepts with practical implementation, the book provides a strong foundation in computational techniques essential for modern data analysis.This revised third edition introduces two new chapters on R for data analysis, complementing the book’s Excel-based approach, making it an invaluable resource for beginners and students of economics, commerce, management, and the social sciences. This book will be useful for courses in econometrics, financial technology, and applied economics, as it equips readers with the analytical and computational skills required for academic excellence and professional success.
R 7,327.00
Data Science Essentials in Python
Go from messy, unstructured artifacts stored in SQL and NoSQL databases to a neat, well-organized dataset with this quick reference for the busy data scientist. Understand text mining, machine learning, and network analysis; process numeric data with the NumPy and Pandas modules; describe and analyze data using statistical and network-theoretical methods; and see actual examples of data analysis at work. This one-stop solution covers the essential data science you need in Python. Data science is one of the fastest-growing disciplines in terms of academic research, student enrollment, and employment. Python, with its flexibility and scalability, is quickly overtaking the R language for data-scientific projects. Keep Python data-science concepts at your fingertips with this modular, quick reference to the tools used to acquire, clean, analyze, and store data. This one-stop solution covers essential Python, databases, network analysis, natural language processing, elements of machine learning, and visualization. Access structured and unstructured text and numeric data from local files, databases, and the Internet. Arrange, rearrange, and clean the data.Work with relational and non-relational databases, data visualization, and simple predictive analysis (regressions, clustering, and decision trees). See how typical data analysis problems are handled. And try your hand at your own solutions to a variety of medium-scale projects that are fun to work on and look good on your resume. Keep this handy quick guide at your side whether you're a student, an entry-level data science professional converting from R to Python, or a seasoned Python developer who doesn't want to memorize every function and option. What You Need: You need a decent distribution of Python 3.3 or above that includes at least NLTK, Pandas, NumPy, Matplotlib, Networkx, SciKit-Learn, and BeautifulSoup. A great distribution that meets the requirements is Anaconda, available for free from www.continuum.io. If you plan to set up your own database servers, you also need MySQL (www.mysql.com) and MongoDB (www.mongodb.com). Both packages are free and run on Windows, Linux, and Mac OS.
R 1,004.00
Essential Data Analytics, Data Science, and AI
In today’s world, understanding data analytics, data science, and artificial intelligence is not just an advantage but a necessity. This book is your thorough guide to learning these innovative fields, designed to make the learning practical and engaging.The book starts by introducing data analytics, data science, and artificial intelligence. It illustrates real-world applications, and, it addresses the ethical considerations tied to AI. It also explores ways to gain data for practice and real-world scenarios, including the concept of synthetic data. Next, it uncovers Extract, Transform, Load (ETL) processes and explains how to implement them using Python. Further, it covers artificial intelligence and the pivotal role played by machine learning models. It explains feature engineering, the distinction between algorithms and models, and how to harness their power to make predictions. Moving forward, it discusses how to assess machine learning models after their creation, with insights into various evaluation techniques. It emphasizes the crucial aspects of model deployment, including the pros and cons of on-device versus cloud-based solutions. It concludes with real-world examples and encourages embracing AI while dispelling fears, and fostering an appreciation for the transformative potential of these technologies.Whether you’re a beginner or an experienced professional, this book offers valuable insights that will expand your horizons in the world of data and AI.What you will learn:What are Synthetic data and Telemetry dataHow to analyze data using programming languages like Python and Tableau.What is feature engineeringWhat are the practical Implications of Artificial IntelligenceWho this book is for:Data analysts, scientists, and engineers seeking to enhance their skills, explore advanced concepts, and stay up-to-date with ethics. Business leaders and decision-makers across industries are interested in understanding the transformative potential and ethical implications of data analytics and AI in their organizations.
R 1,254.00
Introduction to Python in Earth Science Data Analysis
This textbook introduces the use of Python programming for exploring and modelling data in the field of Earth Sciences. It drives the reader from his very first steps with Python, like setting up the environment and starting writing the first lines of codes, to proficient use in visualizing, analyzing, and modelling data in the field of Earth Science. Each chapter contains explicative examples of code, and each script is commented in detail. The book is minded for very beginners in Python programming, and it can be used in teaching courses at master or PhD levels. Also, Early careers and experienced researchers who would like to start learning Python programming for the solution of geological problems will benefit the reading of the book.
R 2,624.51
Learn Data Science Using Python
Harness the capabilities of Python and gain the expertise need to master data science techniques. This step-by-step book guides you through using Python to achieve tasks related to data cleaning, statistics, and visualization.You’ll start by reviewing the foundational aspects of the data science process. This includes an extensive overview of research points and practical applications, such as the insightful analysis of presidential elections. The journey continues by navigating through installation procedures and providing valuable insights into Python, data types, typecasting, and essential libraries like Pandas and NumPy. You’ll then delve into the captivating world of data visualization. Concepts such as scatter plots, histograms, and bubble charts come alive through detailed discussions and practical code examples, unraveling the complexities of creating compelling visualizations for enhanced data understanding.Statistical analysis, linear models, and advanced data preprocessing techniques are also discussed before moving on to preparing data for analysis, including renaming variables, variable rearrangement, and conditional statements. Finally, you’ll be introduced to regression techniques, demystifying the intricacies of simple and multiple linear regression, as well as logistic regression.What You’ll LearnUnderstand installation procedures and valuable insights into Python, data types, typecastingExamine the fundamental statistical analysis required in most data science and analytics reportsClean the most common data set problemsUse linear progression for data predictionWho This Book Is ForData Analysts, data scientists, Python programmers, and software developers new to data science.
R 2,156.71
Business Analytics
Analytical skills are in high demand but short supply. Through a unique combination of data visualisation and analytics (both theoretical and applied), this ground-breaking textbook provides you with the expertise to analyse, interpret and communicate data with confidence, to inform real-world decision-making.Key features include:• Difficulty indicators for each chapter signalling introductory, intermediate and advanced content• An entire chapter dedicated to data visualisation, introducing Tableau for storytelling with data• Motivational cases throughout showing how the theory relates to real life• A wide variety of end-of-chapter exercises to test your understanding of the topics covered• Online examples and datasets to help you master your analytics skills through hands-on learningLecturers also have access a range of online resources developed to support teaching, including a Teaching Guide, solutions to end-of-chapter exercises, PowerPoints and a testbank.Business Analytics: Applied Modelling and Prediction is suitable for undergraduate and postgraduate students of Business Analytics.Dr James Abdey is an Associate Professor (Education) in Statistics at The London School of Economics and Political Science (LSE).
R 3,767.00
Data Visualization with Python and JavaScript 2e
How do you turn raw, unprocessed, or malformed data into dynamic, interactive web visualizations? In this practical book, author Kyran Dale shows data scientists and analysts--as well as Python and JavaScript developers--how to create the ideal toolchain for the job. By providing engaging examples and stressing hard-earned best practices, this guide teaches you how to leverage the power of best-of-breed Python and JavaScript libraries. Python provides accessible, powerful, and mature libraries for scraping, cleaning, and processing data. And while JavaScript is the best language when it comes to programming web visualizations, its data processing abilities can't compare with Python's. Together, these two languages are a perfect complement for creating a modern web-visualization toolchain. This book gets you started. You'll learn how to: Obtain data you need programmatically, using scraping tools or web APIs: Requests, Scrapy, Beautiful Soup Clean and process data using Python's heavyweight data processing libraries within the NumPy ecosystem: Jupyter notebooks with pandas+Matplotlib+Seaborn Deliver the data to a browser with static files or by using Flask, the lightweight Python server, and a RESTful API Pick up enough web development skills (HTML, CSS, JS) to get your visualized data on the web Use the data you've mined and refined to create web charts and visualizations with Plotly, D3, Leaflet, and other libraries
R 1,505.00
Analytics Stories
Inform your own analyses by seeing how one of the best data analysts in the world approaches analytics problems Analytics Stories: How to Make Good Things Happen is a thoughtful, incisive, and entertaining exploration of the application of analytics to real-world problems and situations. Covering fields as diverse as sports, finance, politics, healthcare, and business, Analytics Stories bridges the gap between the oft inscrutable world of data analytics and the concrete problems it solves. Distinguished professor and author Wayne L. Winston answers questions like: Was Liverpool over Barcelona the greatest upset in sports history? Was Derek Jeter a great infielder What's wrong with the NFL QB rating? How did Madoff keep his fund going? Does a mutual fund’s past performance predict future performance? What caused the Crash of 2008? Can we predict where crimes are likely to occur? Is the lot of the American worker improving? How can analytics save the US Republic? The birth of evidence-based medicine: How did James Lind know citrus fruits cured scurvy? How can I objectively compare hospitals? How can we predict heart attacks in real time? How does a retail store know if you're pregnant? How can I use A/B testing to improve sales from my website? How can analytics help me write a hit song? Perfect for anyone with the word “analyst” in their job title, Analytics Stories illuminates the process of applying analytic principles to practical problems and highlights the potential pitfalls that await careless analysts.
R 844.00
R 1,160.00
Primer on Business Analytics
This book will provide a comprehensive overview of business analytics, for those who have either a technical background (quantitative methods) or a practitioner business background. Business analytics, in the context of the 4th Industrial Revolution, is the "new normal" for businesses that operate in this digital age. This book provides a comprehensive primer and overview of the field (and related fields such as Business Intelligence and Data Science). It will discuss the field as it applies to financial institutions, with some minor departures to other industries. Readers will gain understanding and insight into the field of data science, including traditional as well as emerging techniques. Further, many chapters are dedicated to the establishment of a data-driven team – from executive buy-in and corporate governance to managing and quantifying the return of data-driven projects.
R 4,091.69
