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Simulation For Data Science With R: Effective Datadriven Decision Making
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Harness Actionable Insights From Your Data With Computational Statistics And Simulations Using Rkey Features: Learn Five Different Simulation Techniques (Monte Carlo, Discrete Event Simulation, System Dynamics, Agentbased Modeling, And Resampling) Indepth Using Realworld Case Studies A Unique Book That Teaches You The Essential And Fundamental Concepts In Statistical Modeling And Simulationbook Description:Data Science With R Aims To Teach You How To Begin Performing Data Science Tasks By Taking Advantage Of Rs Powerful Ecosystem Of Packages. R Being The Most Widely Used Programming Language When Used With Data Science Can Be A Powerful Combination To Solve Complexities Involved With Varied Data Sets In The Real World.The Book Will Provide A Computational And Methodological Framework For Statistical Simulation To The Users. Through This Book, You Will Get In Grips With The Software Environment R. After Getting To Know The Background Of Popular Methods In The Area Of Computational Statistics, You Will See Some Applications In R To Better Understand The Methods As Well As Gaining Experience Of Working With Realworld Data And Realworld Problems. This Book Helps Uncover The Largescale Patterns In Complex Systems Where Interdependencies And Variation Are Critical. An Effective Simulation Is Driven By Data Generating Processes That Accurately Reflect Real Physical Populations. You Will Learn How To Plan And Structure A Simulation Project To Aid In The Decisionmaking Process As Well As The Presentation Of Results.By The End Of This Book, You Reader Will Get In Touch With The Software Environment R. After Getting Background On Popular Methods In The Area, You Will See Applications In R To Better Understand The Methods As Well As To Gain Experience When Working On Realworld Data And Realworld Problems.What You Will Learn: The Book Aims To Explore Advanced R Features To Simulate Data To Extract Insights From Your Data. Get To Know The Advanced Features Of R Including Highperformance Computing And Advanced Data Manipulation See Random Number Simulation Used To Simulate Distributions, Data Sets, And Populations Simulate Closetoreality Populations As The Basis For Agentbased Micro, Model And Designbased Simulations Applications To Design Statistical Solutions With R For Solving Scientific And Real World Problems Comprehensive Coverage Of Several R Statistical Packages Like Boot, Simpop, Vim, Data.Table, Dplyr, Parallel, Statda, Simecol, Simecolmodels, Desolve And Many More.Who This Book Is For:This Book Is For Users Who Are Familiar With Computational Methods. If You Want To Learn About The Advanced Features Of R, Including The Computerintense Montecarlo Methods As Well As Computational Tools For Statistical Simulation, Then This Book Is For You. Good Knowledge Of R Programming Is Assumed/Required.
⚠️ WARNING (California Proposition 65):
This product may contain chemicals known to the State of California to cause cancer, birth defects, or other reproductive harm.
For more information, please visit www.P65Warnings.ca.gov.
- Q: How many pages does this book have? A: This book has three hundred ninety-eight pages. It provides comprehensive coverage of simulation techniques and R programming.
- Q: What is the binding type of this book? A: The binding type is paperback. This makes it flexible and easy to handle while reading.
- Q: What are the dimensions of this book? A: The dimensions are seven point five two inches in length, zero point nine one inches in width, and nine point two five inches in height. These dimensions make it a compact and portable choice.
- Q: Is this book suitable for beginners in data science? A: Yes, this book is suitable for beginners with some familiarity in computational methods. It covers fundamental concepts and advanced R features.
- Q: How can I apply the techniques learned in this book? A: You can apply the techniques by following the real-world case studies and examples provided. They guide you in performing simulations and data analysis using R.
- Q: What level of R programming knowledge is required? A: A good knowledge of R programming is assumed. This book is targeted towards users who are familiar with computational methods.
- Q: How do I keep this book in good condition? A: To keep the book in good condition, store it in a cool, dry place away from direct sunlight. Handle it gently to avoid wear and tear.
- Q: Is this book safe for young readers? A: Yes, the book is safe for young readers interested in data science, but it requires a foundational understanding of computational methods.
- Q: Can I return this book if I am not satisfied? A: Yes, you can return the book if you are not satisfied with your purchase, but please check the specific return policy of the seller.
- Q: What if the book arrives damaged? A: If the book arrives damaged, you should contact the seller immediately to initiate a return or replacement process.
- Q: Does this book cover advanced statistical techniques? A: Yes, this book covers advanced statistical techniques including Monte Carlo methods and agent-based modeling. It provides in-depth insights into these methods.
- Q: Is there a specific audience for this book? A: The book is primarily aimed at users familiar with computational methods and those wanting to learn advanced features of R programming.
- Q: What are the key features of this book? A: Key features include in-depth learning of five different simulation techniques and comprehensive coverage of R statistical packages.
- Q: Does this book include real-world case studies? A: Yes, it includes real-world case studies that illustrate the application of simulation techniques in data science.
- Q: What topics does this book primarily focus on? A: The book focuses on statistical modeling, simulation, and data-driven decision-making using R. It provides practical guidance for real-world problems.