“Life on earth is filled with many mysteries, but perhaps the most challenging of these is the nature of Intelligence.” – Prof. Terrence J. Sejnowski, Computational Neurobiologist The main objective of this book is to create awareness about both the promises and the formidable challenges that the era of Data-Driven Decision-Making and Machine Learning are confronted with, and especially about how these new developments may influence the future of the financial industry. The subject of Financial Machine Learning has attracted a lot of interest recently, specifically because it represents one of the most challenging problem spaces for the applicability of Machine Learning. The author has used a novel approach to introduce the reader to this topic: The first half of the book is a readable and coherent introduction to two modern topics that are not generally considered together: the data-driven paradigm and Computational Intelligence. The second half of the book illustrates a set of Case Studies that are contemporarily relevant to quantitative trading practitioners who are dealing with problems such as trade execution optimization, price dynamics forecast, portfolio management, market making, derivatives valuation, risk, and compliance. The main purpose of this book is pedagogical in nature, and it is specifically aimed at defining an adequate level of engineering and scientific clarity when it comes to the usage of the term “ Artificial Intelligence,” especially as it relates to the financial industry. The message conveyed by this book is one of confidence in the possibilities offered by this new era of Data-Intensive Computation. This message is not grounded on the current hype surrounding the latest technologies, but on a deep analysis of their effectiveness and also on the author’s two decades of professional experience as a technologist, quant and academic.

Apply MATLAB programming to the mathematical modeling of real-life problems from a wide range of topics. This pragmatic book shows you how to solve your programming problems, starting with a brief primer on MATLAB and the fundamentals of the MATLAB programming language. Then, you’ll build fully working examples and computational models found in the financial, engineering, and scientific sectors. As part of this section, you’ll cover signal and image processing, as well as GUIs. After reading and using Practical MATLAB and its accompanying source code, you’ll have the practical know-how and code to apply to your own MATLAB programming projects. What You Will Learn Discover the fundamentals of MATLAB and how to get started with it for problem solving Apply MATLAB to a variety of problems and case studies Carry out economic and financial modeling with MATLAB, including option pricing and compound interest Use MATLAB for simulation problems such as coin flips, dice rolling, random walks, and traffic flows Solve computational biology problems with MATLAB Implement signal processing with MATLAB, including currents, Fast Fourier Transforms (FFTs), and harmonic analysis Process images with filters and edge detection Build applications with GUIs Who This Book Is For People with some prior experience with programming and MATLAB.

Mastering pandas is the ultimate guide to harnessing the power of the pandas library for data analysis. Covering everything from installation to advanced techniques, this book provides comprehensive instructions and examples to help you perform efficient data manipulation and visualization. Explore key features of pandas, such as multi-indexing and time series analysis, and become proficient in actionable analytics. What this Book will help me do Master importing and managing datasets of various formats using pandas. Expertly handle missing data and clean datasets for robust analysis. Create powerful visualizations and reports using pandas and Jupyter notebooks. Leverage advanced indexing and grouping techniques to derive insights. Utilize pandas for time series analysis to analyze trends and patterns. Author(s) None Kumar is an experienced data scientist specializing in data analysis and visualization using Python. With a deep understanding of the pandas library, None has been helping professionals and enthusiasts alike to make data-driven decisions. Known for an example-driven teaching style, None bridges complex theoretical concepts with practical applications in data science. Who is it for? If you're a data scientist, analyst, or Python developer seeking to enhance your data analysis capabilities, this book is for you. Prior knowledge of Python is beneficial but not mandatory, as foundational concepts are explained. This guide spans beginner to advanced topics, accommodating users looking to deepen their skills and those aiming to start with pandas.

As your business tries to make sense of today’s staggering amount of structured and unstructured data, traditional analytics will take you only so far. The key to success over the next few years will depend on augmented analytics, a method that embeds machine learning and natural language processing (NLP) in the process. This report explains how augmented analytics can help you uncover hidden insights, predict results, and even prescribe solutions. Author Alice LaPlante provides best practices for deploying augmented analytics, along with real-world case studies that show you how to take full advantage of this method. IT professionals, business managers, and CFOs will learn ways to democratize data use among business users and executives, using a self-service model. The future belongs to those who can get more from their data. This report shows you how. Get a primer on the key components and learn how they work together Delve into the benefits of—and roadblocks to—adopting augmented analytics Learn how companies use this method in marketing, sales, finance, and human resources Examine case studies of companies including Accenture and Riverbed

Influence action through data! This is not a book. It is a one-of-a-kind immersive learning experience through which you can become—or teach others to be—a powerful data storyteller. Let’s practice! helps you build confidence and credibility to create graphs and visualizations that make sense and weave them into action-inspiring stories. Expanding upon best seller storytelling with data’s foundational lessons, Let’s practice! delivers fresh content, a plethora of new examples, and over 100 hands-on exercises. Author and data storytelling maven Cole Nussbaumer Knaflic guides you along the path to hone core skills and become a well-practiced data communicator. Each chapter includes: ● Practice with Cole: exercises based on real-world examples first posed for you to consider and solve, followed by detailed step-by-step illustration and explanation ● Practice on your own: thought-provoking questions and even more exercises to be assigned or worked through individually, without prescribed solutions ● Practice at work: practical guidance and hands-on exercises for applying storytelling with data lessons on the job, including instruction on when and how to solicit useful feedback and refine for greater impact The lessons and exercises found within this comprehensive guide will empower you to master—or develop in others—data storytelling skills and transition your work from acceptable to exceptional. By investing in these skills for ourselves and our teams, we can all tell inspiring and influential data stories!

Business Statistics with Solutions in R covers a wide range of applications of statistics in solving business related problems. It will introduce readers to quantitative tools that are necessary for daily business needs and help them to make evidence-based decisions. The book provides an insight on how to summarize data, analyze it, and draw meaningful inferences that can be used to improve decisions. It will enable readers to develop computational skills and problem-solving competence using the open source language, R. Mustapha Abiodun Akinkunmi uses real life business data for illustrative examples while discussing the basic statistical measures, probability, regression analysis, significance testing, correlation, the Poisson distribution, process control for manufacturing, time series analysis, forecasting techniques, exponential smoothing, univariate and multivariate analysis including ANOVA and MANOVA and more in this valuable reference for policy makers, professionals, academics and individuals interested in the areas of business statistics, applied statistics, statistical computing, finance, management and econometrics.

Make Better Decisions, Leverage New Opportunities, and Automate Decisioning at Scale Prescriptive analytics is more directly linked to successful decision-making than any other form of business analytics. It can help you systematically sort through your choices to optimize decisions, respond to new opportunities and risks with precision, and continually reflect new information into your decisioning process. In Prescriptive Analytics, analytics expert Dr. Dursun Delen illuminates the field’s state-of-the-art methods, offering holistic insight for both professionals and students. Delen’s end-to-end, all-inclusive approach covers optimization, simulation, multi-criteria decision-making methods, inference- and heuristic-based decisioning, and more. Balancing theory and practice, he presents intuitive conceptual illustrations, realistic example problems, and real-world case studies–all designed to deliver knowledge you can use. Discover where prescriptive analytics fits and how it improves decision-making Identify optimal solutions for achieving an objective within real-world constraints Analyze complex systems via Monte-Carlo, discrete, and continuous simulations Apply powerful multi-criteria decision-making and mature expert systems and case-based reasoning Preview emerging techniques based on deep learning and cognitive computing

This book outlines the benefits and limitations of simulation, what is involved in setting up a simulation capability in an organization, the steps involved in developing a simulation model and how to ensure that model results are implemented. In addition, detailed example applications are provided to show where the tool is useful and what it can offer the decision maker. In Simulating Business Processes for Descriptive, Predictive, and Prescriptive Analytics, Andrew Greasley provides an in-depth discussion of Business process simulation and how it can enable business analytics How business process simulation can provide speed, cost, dependability, quality, and flexibility metrics Industrial case studies including improving service delivery while ensuring an efficient use of staff in public sector organizations such as the police service, testing the capacity of planned production facilities in manufacturing, and ensuring on-time delivery in logistics systems State-of-the-art developments in business process simulation regarding the generation of simulation analytics using process mining and modeling people’s behavior Managers and decision makers will learn how simulation provides a faster, cheaper and less risky way of observing the future performance of a real-world system. The book will also benefit personnel already involved in simulation development by providing a business perspective on managing the process of simulation, ensuring simulation results are implemented, and that performance is improved.

Master the concepts and techniques of statistical analysis using JMP Practical Data Analysis with JMP, Third Edition, highlights the powerful interactive and visual approach of JMP to introduce readers to statistical thinking and data analysis. It helps you choose the best technique for the problem at hand by using real-world cases. It also illustrates best-practice workflow throughout the entire investigative cycle, from asking valuable questions through data acquisition, preparation, analysis, interpretation, and communication of findings. The book can stand on its own as a learning resource for professionals, or it can be used to supplement a college-level textbook for an introductory statistics course. It includes varied examples and problems using real sets of data. Each chapter typically starts with an important or interesting research question that an investigator has pursued. Reflecting the broad applicability of statistical reasoning, the problems come from a wide variety of disciplines, including engineering, life sciences, business, and economics, as well as international and historical examples. Application Scenarios at the end of each chapter challenge you to use your knowledge and skills with data sets that go beyond mere repetition of chapter examples. New in the third edition, chapters have been updated to demonstrate the enhanced capabilities of JMP, including projects, Graph Builder, Query Builder, and Formula Depot.

The official guide by the SAS Global Certification Program, SAS Certified Professional Prep Guide: Advanced Programming Using SAS 9.4 prepares you to take the new SAS 9.4 Advanced Programming Performance-Based Exam. New in this edition is a workbook whose sample scenarios require you to write code to solve problems and answer questions. Answers to the chapter quizzes and solutions to the sample scenarios in the workbook are included. You will also find links to exam objectives, practice exams, and other resources such as the Base SAS Glossary and a list of practice data sets. Major topics include SQL processing, SAS macro language processing, and advanced SAS programming techniques. All exam topics are covered in the following chapters: SQL Processing with SAS PROC SQL Fundamentals Creating and Managing Tables Joining Tables Using PROC SQL Joining Tables Using Set Operators Using Subqueries Advanced SQL Techniques SAS Macro Language Processing Creating and Using Macro Variables Storing and Processing Text Working with Macro Programs Advanced Macro Techniques Advanced SAS Programming Techniques Defining and Processing Arrays Processing Data Using Hash Objects Using SAS Utility Procedures Using Advanced Functions Practice Programming Scenarios (Workbook)

Time series data analysis is increasingly important due to the massive production of such data through the internet of things, the digitalization of healthcare, and the rise of smart cities. As continuous monitoring and data collection become more common, the need for competent time series analysis with both statistical and machine learning techniques will increase. Covering innovations in time series data analysis and use cases from the real world, this practical guide will help you solve the most common data engineering and analysis challengesin time series, using both traditional statistical and modern machine learning techniques. Author Aileen Nielsen offers an accessible, well-rounded introduction to time series in both R and Python that will have data scientists, software engineers, and researchers up and running quickly. You’ll get the guidance you need to confidently: Find and wrangle time series data Undertake exploratory time series data analysis Store temporal data Simulate time series data Generate and select features for a time series Measure error Forecast and classify time series with machine or deep learning Evaluate accuracy and performance

In the "R Bioinformatics Cookbook", you will explore the full potential of the R programming language and the Bioconductor ecosystem to overcome challenges in bioinformatics. By working through real-world examples, you will learn to handle biological data effectively and gain insights into genomics, RNA sequencing, and advanced data visualization. What this Book will help me do Develop skills to analyze RNA sequencing data using R and Bioconductor packages such as edgeR and DESeq. Learn to create professional-grade graphical representations of biological data using ggplot and other visualization tools. Understand how to perform genome-wide studies like variant calling and metagenomics analysis with R. Master the integration of external genomic databases with Ensembl for functional annotation. Explore machine learning applications in bioinformatics including classification and clustering models. Author(s) None MacLean and Dr. Dan Maclean are experienced bioinformatics researchers and R programmers. With a deep understanding of computational biology and visualization techniques, they bring years of academic and practical expertise to help readers excel in bioinformatics. Their approachable writing style ensures that complex topics are made accessible. Who is it for? This book is ideal for bioinformatics professionals and data analysts with an interest in applying R to biological data. It is particularly suited for those with a basic knowledge of R and bioinformatics looking to enhance their analysis skills. Researchers seeking to integrate genomics and computational methods into their workflows will find this book valuable. It's perfect for anyone aiming to tackle intermediate to advanced topics in biological data analysis.

A classic that just keeps getting better, The Little SAS Book is essential for anyone learning SAS programming. Lora Delwiche and Susan Slaughter offer a user-friendly approach so that readers can quickly and easily learn the most commonly used features of the SAS language. Each topic is presented in a self-contained, two-page layout complete with examples and graphics. Nearly every section has been revised to ensure that the sixth edition is fully up-to-date. This edition is also interface-independent, written for all SAS programmers whether they use SAS Studio, SAS Enterprise Guide, or the SAS windowing environment. New sections have been added covering PROC SQL, iterative DO loops, DO WHILE and DO UNTIL statements, %DO statements, using variable names with special characters, the ODS EXCEL destination, and the XLSX LIBNAME engine. This title belongs on every SAS programmer's bookshelf. It's a resource not just to get you started, but one you will return to as you continue to improve your programming skills. Learn more about the updates to The Little SAS Book, Sixth Edition here. Reviews for The Little SAS Book, Sixth Edition can be read here.

Instructs readers on how to use methods of statistics and experimental design with R software Applied statistics covers both the theory and the application of modern statistical and mathematical modelling techniques to applied problems in industry, public services, commerce, and research. It proceeds from a strong theoretical background, but it is practically oriented to develop one's ability to tackle new and non-standard problems confidently. Taking a practical approach to applied statistics, this user-friendly guide teaches readers how to use methods of statistics and experimental design without going deep into the theory. Applied Statistics: Theory and Problem Solutions with R includes chapters that cover R package sampling procedures, analysis of variance, point estimation, and more. It follows on the heels of Rasch and Schott's Mathematical Statistics via that book's theoretical background—taking the lessons learned from there to another level with this book’s addition of instructions on how to employ the methods using R. But there are two important chapters not mentioned in the theoretical back ground as Generalised Linear Models and Spatial Statistics. Offers a practical over theoretical approach to the subject of applied statistics Provides a pre-experimental as well as post-experimental approach to applied statistics Features classroom tested material Applicable to a wide range of people working in experimental design and all empirical sciences Includes 300 different procedures with R and examples with R-programs for the analysis and for determining minimal experimental sizes Applied Statistics: Theory and Problem Solutions with R will appeal to experimenters, statisticians, mathematicians, and all scientists using statistical procedures in the natural sciences, medicine, and psychology amongst others.

Explore biostatistics using JMP in this refreshing introduction Presented in an easy-to-understand way, Introduction to Biostatistics with JMP introduces undergraduate students in the biological sciences to the most commonly used (and misused) statistical methods that they will need to analyze their experimental data using JMP. It covers many of the basic topics in statistics using biological examples for exercises so that the student biologists can see the relevance to future work in the problems addressed. The book starts by teaching students how to become confident in executing the right analysis by thinking like a statistician then moves into the application of specific tests. Using the powerful capabilities of JMP, the book addresses problems requiring analysis by chi-square tests, t tests, ANOVA analysis, various regression models, DOE, and survival analysis. Topics of particular interest to the biological or health science field include odds ratios, relative risk,

Point and click your way to performing statistics! Many people are intimidated by learning statistics, but A Gentle Introduction to Statistics Using SAS Studio is here to help. Whether you need to perform statistical analysis for a project or, perhaps, for a course in education, psychology, sociology, economics, or any other field that requires basic statistical skills, this book teaches the fundamentals of statistics, from designing your experiment through calculating logistic regressions. Serving as an introduction to many common statistical tests and principles, it explains concepts in a non-technical way with little math and very few formulas. Once the basic statistical concepts are covered, the book then demonstrates how to use them with SAS Studio and SAS University Edition’s easy point-and-click interface. Topics included in this book are: How to install and use SAS University Edition Descriptive statistics One-sample tests T tests (for independent or paired samples) One-way analysis of variance (ANOVA) N-way ANOVA Correlation analysis Simple and multiple linear regression Binary logistic regression Categorical data, including two-way tables and chi-square Power and sample size calculations Questions are provided to test your knowledge and practice your skills.

"Hands-On SAS for Data Analysis" is a practical guide that introduces you to the fundamentals of using SAS for managing and analyzing data effectively. Through a hands-on approach, you'll explore key topics such as data manipulation with SAS 4GL, SQL querying, and creating insightful visualizations and reports. By the end of the book, you'll not only have a robust understanding of SAS but also be prepared for the SAS certification exam. What this Book will help me do Effectively use SAS modules and tools for comprehensive data analysis tasks. Master SAS 4GL functions to perform advanced data manipulation and transformation. Leverage advanced SQL options within SAS to query and analyze datasets. Become proficient in writing SAS Macros to automate repetitive tasks efficiently. Produce professional reports and visualizations using SAS Output Delivery System. Author(s) None Gulati is a renowned expert in data analysis and business intelligence, with years of professional experience in leveraging SAS for enterprise solutions. An experienced trainer and technical author, None has a unique ability to simplify complex concepts. Through this book, None shares practical knowledge that aligns with industry needs and certification goals. Who is it for? This book is designed for data professionals seeking to enhance their skills in SAS programming and data analysis. Whether you're just starting out with SAS or aiming to pass the SAS certification exam, this book will provide valuable insights. Readers with basic knowledge of data management will find this guide especially beneficial.

BRIDGES THE GAP BETWEEN SAS AND R, ALLOWING USERS TRAINED IN ONE LANGUAGE TO EASILY LEARN THE OTHER SAS and R are widely-used, very different software environments. Prized for its statistical and graphical tools, R is an open-source programming language that is popular with statisticians and data miners who develop statistical software and analyze data. SAS (Statistical Analysis System) is the leading corporate software in analytics thanks to its faster data handling and smaller learning curve. SAS for R Users enables entry-level data scientists to take advantage of the best aspects of both tools by providing a cross-functional framework for users who already know R but may need to work with SAS. Those with knowledge of both R and SAS are of far greater value to employers, particularly in corporate settings. Using a clear, step-by-step approach, this book presents an analytics workflow that mirrors that of the everyday data scientist. This up-to-date guide is compatible with the latest R packages as well as SAS University Edition. Useful for anyone seeking employment in data science, this book: Instructs both practitioners and students fluent in one language seeking to learn the other Provides command-by-command translations of R to SAS and SAS to R Offers examples and applications in both R and SAS Presents step-by-step guidance on workflows, color illustrations, sample code, chapter quizzes, and more Includes sections on advanced methods and applications Designed for professionals, researchers, and students, SAS for R Users is a valuable resource for those with some knowledge of coding and basic statistics who wish to enter the realm of data science and business analytics. AJAY OHRI is the founder of analytics startup Decisionstats.com. His research interests include spreading open source analytics, analyzing social media manipulation with mechanism design, simpler interfaces to cloud computing, investigating climate change, and knowledge flows. He currently advises startups in analytics off shoring, analytics services, and analytics. He is the author of Python for R Users: A Data Science Approach (Wiley), R for Business Analytics, and R for Cloud Computing.

Model Management and Analytics for Large Scale Systems covers the use of models and related artefacts (such as metamodels and model transformations) as central elements for tackling the complexity of building systems and managing data. With their increased use across diverse settings, the complexity, size, multiplicity and variety of those artefacts has increased. Originally developed for software engineering, these approaches can now be used to simplify the analytics of large-scale models and automate complex data analysis processes. Those in the field of data science will gain novel insights on the topic of model analytics that go beyond both model-based development and data analytics. This book is aimed at both researchers and practitioners who are interested in model-based development and the analytics of large-scale models, ranging from big data management and analytics, to enterprise domains. The book could also be used in graduate courses on model development, data analytics and data management. Identifies key problems and offers solution approaches and tools that have been developed or are necessary for model management and analytics Explores basic theory and background, current research topics, related challenges and the research directions for model management and analytics Provides a complete overview of model management and analytics frameworks, the different types of analytics (descriptive, diagnostics, predictive and prescriptive), the required modelling and method steps, and important future directions

Master the art of business intelligence with "Learn Power BI". This beginner-friendly guide introduces you to building interactive business reports and dashboards using Microsoft Power BI. By covering data modeling, visualization, and analysis techniques, you will be equipped with the tools to derive actionable insights and decision-making support from your organization's data. What this Book will help me do Create interactive dashboards using Power BI features to enhance business reporting. Import and transform raw data using Power BI's Query Editor for cleaner analysis. Perform dynamic and complex calculations with DAX to enrich your data insights. Develop storytelling reports that convey impactful business insights effectively. Publish and securely share Power BI reports, enabling collaboration across teams. Author(s) Greg Deckler is an expert in business intelligence and data analysis, with years of experience leveraging Power BI to solve real-world challenges. As a seasoned author, Greg combines technical depth with an approachable teaching style, making intricate concepts accessible. Through his writing, he focuses on empowering readers to unlock the full potential of Power BI for solving business challenges. Who is it for? This book is ideal for IT managers, data analysts, or professionals looking to adopt Power BI for their business intelligence needs. It's accessible to beginners with no prior Power BI experience. Additionally, if you're transitioning from other BI tools and aim to learn how to build interactive dashboards and reports in Power BI, this is the guide for you. Advance your skills and meet your business goals with confidence.

Gain insight into essential data science skills in a holistic manner using data engineering and associated scalable computational methods. This book covers the most popular Python 3 frameworks for both local and distributed (in premise and cloud based) processing. Along the way, you will be introduced to many popular open-source frameworks, like, SciPy, scikitlearn, Numba, Apache Spark, etc. The book is structured around examples, so you will grasp core concepts via case studies and Python 3 code. As data science projects gets continuously larger and more complex, software engineering knowledge and experience is crucial to produce evolvable solutions. You'll see how to create maintainable software for data science and how to document data engineering practices. This book is a good starting point for people who want to gain practical skills to perform data science. All the code willbe available in the form of IPython notebooks and Python 3 programs, which allow you to reproduce all analyses from the book and customize them for your own purpose. You'll also benefit from advanced topics like Machine Learning, Recommender Systems, and Security in Data Science. Practical Data Science with Python will empower you analyze data, formulate proper questions, and produce actionable insights, three core stages in most data science endeavors. What You'll Learn Play the role of a data scientist when completing increasingly challenging exercises using Python 3 Work work with proven data science techniques/technologies Review scalable software engineering practices to ramp up data analysis abilities in the realm of Big Data Apply theory of probability, statistical inference, and algebra to understand the data sciencepractices Who This Book Is For Anyone who would like to embark into the realm of data science using Python 3.

A revised edition that explores random numbers, probability, and statistical inference at an introductory mathematical level Written in an engaging and entertaining manner, the revised and updated second edition of Probably Not continues to offer an informative guide to probability and prediction. The expanded second edition contains problem and solution sets. In addition, the book’s illustrative examples reveal how we are living in a statistical world, what we can expect, what we really know based upon the information at hand and explains when we only think we know something. The author introduces the principles of probability and explains probability distribution functions. The book covers combined and conditional probabilities and contains a new section on Bayes Theorem and Bayesian Statistics, which features some simple examples including the Presecutor’s Paradox, and Bayesian vs. Frequentist thinking about statistics. New to this edition is a chapter on Benford’s Law that explores measuring the compliance and financial fraud detection using Benford’s Law. This book: Contains relevant mathematics and examples that demonstrate how to use the concepts presented Features a new chapter on Benford’s Law that explains why we find Benford’s law upheld in so many, but not all, natural situations Presents updated Life insurance tables Contains updates on the Gantt Chart example that further develops the discussion of random events Offers a companion site featuring solutions to the problem sets within the book Written for mathematics and statistics students and professionals, the updated edition of Probably Not: Future Prediction Using Probability and Statistical Inference, Second Edition combines the mathematics of probability with real-world examples. LAWRENCE N. DWORSKY, PhD, is a retired Vice President of the Technical Staff and Director of Motorola’s Components Research Laboratory in Schaumburg, Illinois, USA. He is the author of Introduction to Numerical Electrostatics Using MATLAB from Wiley.

Learn Python by Building Data Science Applications takes a hands-on approach to teaching Python programming by guiding you through building engaging real-world data science projects. This book introduces Python's rich ecosystem and equips you with the skills to analyze data, train models, and deploy them as efficient applications. What this Book will help me do Get proficient in Python programming by learning core topics like data structures, loops, and functions. Explore data science libraries such as NumPy, Pandas, and scikit-learn to analyze and process data. Learn to create visualizations with Matplotlib and Altair, simplifying data communication. Build and deploy machine learning models using Python and share them as web services. Understand development practices such as testing, packaging, and continuous integration for professional workflows. Author(s) None Kats and None Katz are seasoned Python developers with years of experience in teaching programming and deploying data science applications. Their expertise spans providing learners with practical knowledge and versatile skills. They combine clear explanations with engaging projects to ensure a rewarding learning experience. Who is it for? This book is ideal for individuals new to programming or data science who want to learn Python through practical projects. Researchers, analysts, and ambitious students with minimal coding background but a keen interest in data analysis and application development will find this book beneficial. It's a perfect choice for anyone eager to explore and leverage Python for real-world solutions.

Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product. Use machine learning to understand your customers, frame decisions, and drive value The business analytics world has changed, and Data Scientists are taking over. Business Data Science takes you through the steps of using machine learning to implement best-in-class business data science. Whether you are a business leader with a desire to go deep on data, or an engineer who wants to learn how to apply Machine Learning to business problems, you’ll find the information, insight, and tools you need to flourish in today’s data-driven economy. You’ll learn how to: •Use the key building blocks of Machine Learning: sparse regularization, out-of-sample validation, and latent factor and topic modeling •Understand how use ML tools in real world business problems, where causation matters more that correlation •Solve data science programs by scripting in the R programming language Today’s business landscape is driven by data and constantly shifting. Companies live and die on their ability to make and implement the right decisions quickly and effectively. Business Data Science is about doing data science right. It’s about the exciting things being done around Big Data to run a flourishing business. It’s about the precepts, principals, and best practices that you need know for best-in-class business data science.

In this handy, practical book you will cover each concept concisely, with many illustrative examples. You'll be introduced to several R data science packages, with examples of how to use each of them. In this book, you’ll learn about the following APIs and packages that deal specifically with data science applications: readr, dibble, forecasts, lubridate, stringr, tidyr, magnittr, dplyr, purrr, ggplot2, modelr, and more. After using this handy quick reference guide, you'll have the code, APIs, and insights to write data science-based applications in the R programming language. You'll also be able to carry out data analysis. What You Will Learn Import data with readr Work with categories using forcats, time and dates with lubridate, and strings with stringr Format data using tidyr and then transform that data using magrittr and dplyrWrite functions with R for data science, data mining, and analytics-based applications Visualize data with ggplot2 and fit data to models using modelr Who This Book Is For Programmers new to R's data science, data mining, and analytics packages. Some prior coding experience with R in general is recommended.