--- product_id: 1704337 title: "Practical Data Science with R" price: "SAR 121" currency: SAR in_stock: true reviews_count: 13 url: https://www.desertcart.com.sa/products/1704337-practical-data-science-with-r store_origin: SA region: Saudi Arabia --- # Real-world R data science use cases Includes free eBook formats Step-by-step statistical modeling Practical Data Science with R **Price:** SAR 121 **Availability:** ✅ In Stock ## Summary > 📈 Unlock the power of practical data science with R — your business’s secret weapon! ## Quick Answers - **What is this?** Practical Data Science with R - **How much does it cost?** SAR 121 with free shipping - **Is it available?** Yes, in stock and ready to ship - **Where can I buy it?** [www.desertcart.com.sa](https://www.desertcart.com.sa/products/1704337-practical-data-science-with-r) ## Best For - Customers looking for quality international products ## Why This Product - Free international shipping included - Worldwide delivery with tracking - 15-day hassle-free returns ## Key Features - • **Hands-On R Mastery:** Skip theory, dive into practical data science with R for business impact. - • **Multi-Format Access:** Print book purchase unlocks free PDF, Kindle & ePub eBooks for learning on the go. - • **From Data to Delivery:** Covers full project lifecycle—planning, modeling, and presenting results effectively. - • **Business-Driven Examples:** Marketing, BI, and decision support cases make data science immediately relevant. - • **Clear Statistical Insights:** Understand not just how, but why key models like logistic regression work. ## Overview Practical Data Science with R is a hands-on guide designed for business professionals and analysts eager to apply R programming and statistical techniques to real-world data challenges. It emphasizes practical use cases in marketing, business intelligence, and decision support, providing clear explanations of data science workflows from data loading to model deployment. The print edition includes free eBook formats, making it accessible anytime, anywhere. Ideal for those with basic stats or scripting knowledge, this book bridges the gap between theory and actionable insights. ## Description Summary Practical Data Science with R lives up to its name. It explains basic principles without the theoretical mumbo-jumbo and jumps right to the real use cases you'll face as you collect, curate, and analyze the data crucial to the success of your business. You'll apply the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support. About the Book Business analysts and developers are increasingly collecting, curating, analyzing, and reporting on crucial business data. The R language and its associated tools provide a straightforward way to tackle day-to-day data science tasks without a lot of academic theory or advanced mathematics. Practical Data Science with R shows you how to apply the R programming language and useful statistical techniques to everyday business situations. Using examples from marketing, business intelligence, and decision support, it shows you how to design experiments (such as A/B tests), build predictive models, and present results to audiences of all levels. This book is accessible to readers without a background in data science. Some familiarity with basic statistics, R, or another scripting language is assumed. What's Inside Data science for the business professional Statistical analysis using the R language Project lifecycle, from planning to delivery Numerous instantly familiar use cases Keys to effective data presentations About the Authors Nina Zumel and John Mount are cofounders of a San Francisco-based data science consulting firm. Both hold PhDs from Carnegie Mellon and blog on statistics, probability, and computer science at win-vector.com. Table of Contents PART 1 INTRODUCTION TO DATA SCIENCE The data science process Loading data into R Exploring data Managing data PART 2 MODELING METHODS Choosing and evaluating models Memorization methods Linear and logistic regression Unsupervised methods Exploring advanced methods PART 3 DELIVERING RESULTS Documentation and deployment Producing effective presentations Review: Fantastic introductory text for data science - This book is what I was looking for for my new job as a Credit Risk Data Modeler (basically data science applied to credit problems). It's coverage is broad, but deep and applied enough that I have been able to apply its contents in my day-to-day work. I look forward to a second edition which will hopefully rectify the following: Earlier in the book it seemed the authors took great pains to explain in layman's terms the various statistical elements of the topic they were covering. They provided very clear and meaningful explanations which made a lot of sense of complex topics. But later in the book it seemed that that approach largely went out the window and they started using more technical boiler plate to describe the various statistical tests and procedures. Rather than perhaps give the technical boilerplate (as you'd see it in a textbook) and then elaborate on it with a more human-centric explanation, they would just leave it at the nearly impervious technical description and then proceed to explain how to conduct the test/procedure/etc in R. But without understanding of what you're trying to accomplish and why, it's hard to write the code to actually do it. Keep in mind that I'm relatively well prepared for this book too, having had as much stats and econometrics as I could fit into my four-year degree. If I found some sections of the book too technical to understand then it seems likely that the book would benefit from some additional explanation and discussion in those later sections. Also, I have a good deal of "boots on the ground" experience with this book in my attempts to apply it in my daily work. I've found that it is useful, but could be more useful if there was more discussion of various practical problems. For instance, much of my work is focused on producing a predictive model of likelihood of charge-off. I.e., if we approve and fund this application, how likely is it to perform or charge-off. The book shares some high-level approaches to finding problems in data (using plots and summaries), fixing those problems using various techniques, selecting variables, and how to conduct the statistical modeling (logistic in my case). But it fails to really tie those areas together beyond the high-level. For instance, what are the assumptions of a logistic regression? How do you resolve issues in your data to ensure that you meet those assumptions and can perform a valid logistic regression? How do you really select variables when you're faced with at least 20 possibilities (and potentially many many more if you count interaction terms, unfixed variables, and variables which have been fixed in different ways)? I suppose, for what it was, that it is "mission accomplished." I'd just like to see a lot more. Perhaps there's need for a second volume? Perhaps "Advanced Practical Data Science with R?" Either this book could have a second edition with a lot more content covering finding data problems, resolving those problems intelligently (for instance, resolving missing data is basically left as "either drop the effected records" or "use the mean as a replacement or the missing value," but there are alternative methods which may be more suitable), what data problems will cause issues in OLS regression, logistic regression, and machine learning; And how to practically select variables and a model. I feel like the book gave me some tools to apply (like a small box of tools you might purchase from a hardware store), but left a lot out. So now I'm in deep water trying to figure out why my logistic regression isn't predictive enough and what I can do about it. Is it the data and how I fixed variables? Is it the variables I've selected? Should I have used automated variable selection techniques? Or just manually tried different variables? How does an experienced practitioner approach these problems? I know they iterate: explore data, clean data, select variables, select model, test model, look at data, change data, change variables, etc... but practically speaking what does it look like? In the book they offer a hand-coded basic variable selection script, and mention that one could also use stepwise variable selection. In the real world I'm reasonably sure that this is not actually done--mostly because their selection script does about as well as stepwise at selecting appropriate variables. There are many other better ways of selecting variables, I've discovered, and I wish that they'd discussed some of those ways (pros and cons), and shown how to conduct them in a meaningful fashion. Same thing with building a model. In my case, I have a whole bunch of variables, limited data (about 2000 records, with the desired outcome only occurring in 120 of those), and the automated tools (various R packages I've discovered and applied) either take a long time to run and/or yield poor results. But if not automated tools then what? Manually add variables and ANOVA test the difference between the first and second model? I'd just like more...more discussion and elaboration and examples of how practical data science is conducted. This book seems like it does a fantastic job as an introduction to the topic, but you'll quickly find that you'll be in deep water without a clue how to swim--as in my case. You'll be left to your own devices, and find yourself wishing, as I do, that there was more in the book (or another book) that I could study after this one which would help take me from beginner data scientist to intermediate. Overall, I'm very glad I bought and read the book. Review: "The" book to have if you want to be a data scientist and working with R - Hands-on textbook that covers pretty much all aspects of data science (with keen attention to business demands). Importantly, it doesn't shy away from discussing statistical details behind the most common routines in machine learning, which I really appreciated as I was tired of typical DS books that take a black box approach that just shows "how" without explaining the "why". I think it's worth having a copy of this book irrespective of whether you are a beginner to data science or a veteran. Highly recommended! If you buy a hard copy, I would also recommend having a look at the colorful figures in the companion soft copy of the book. ## Features - This refurbished product is tested and certified to work properly. The product will have minor blemishes and/or light scratches. The refurbishing process includes functionality testing, basic cleaning, inspection, and repackaging. The product ships with all relevant accessories, and may arrive in a generic box. ## Technical Specifications | Specification | Value | |---------------|-------| | Best Sellers Rank | #3,823,817 in Books ( See Top 100 in Books ) #884 in Mathematical & Statistical Software #1,274 in Data Mining (Books) #1,274 in Data Modeling & Design (Books) | | Customer Reviews | 4.3 out of 5 stars 65 Reviews | ## Images ![Practical Data Science with R - Image 1](https://m.media-amazon.com/images/I/71v3KRNJq3L.jpg) ## Customer Reviews ### ⭐⭐⭐⭐⭐ Fantastic introductory text for data science *by K***R on January 18, 2015* This book is what I was looking for for my new job as a Credit Risk Data Modeler (basically data science applied to credit problems). It's coverage is broad, but deep and applied enough that I have been able to apply its contents in my day-to-day work. I look forward to a second edition which will hopefully rectify the following: Earlier in the book it seemed the authors took great pains to explain in layman's terms the various statistical elements of the topic they were covering. They provided very clear and meaningful explanations which made a lot of sense of complex topics. But later in the book it seemed that that approach largely went out the window and they started using more technical boiler plate to describe the various statistical tests and procedures. Rather than perhaps give the technical boilerplate (as you'd see it in a textbook) and then elaborate on it with a more human-centric explanation, they would just leave it at the nearly impervious technical description and then proceed to explain how to conduct the test/procedure/etc in R. But without understanding of what you're trying to accomplish and why, it's hard to write the code to actually do it. Keep in mind that I'm relatively well prepared for this book too, having had as much stats and econometrics as I could fit into my four-year degree. If I found some sections of the book too technical to understand then it seems likely that the book would benefit from some additional explanation and discussion in those later sections. Also, I have a good deal of "boots on the ground" experience with this book in my attempts to apply it in my daily work. I've found that it is useful, but could be more useful if there was more discussion of various practical problems. For instance, much of my work is focused on producing a predictive model of likelihood of charge-off. I.e., if we approve and fund this application, how likely is it to perform or charge-off. The book shares some high-level approaches to finding problems in data (using plots and summaries), fixing those problems using various techniques, selecting variables, and how to conduct the statistical modeling (logistic in my case). But it fails to really tie those areas together beyond the high-level. For instance, what are the assumptions of a logistic regression? How do you resolve issues in your data to ensure that you meet those assumptions and can perform a valid logistic regression? How do you really select variables when you're faced with at least 20 possibilities (and potentially many many more if you count interaction terms, unfixed variables, and variables which have been fixed in different ways)? I suppose, for what it was, that it is "mission accomplished." I'd just like to see a lot more. Perhaps there's need for a second volume? Perhaps "Advanced Practical Data Science with R?" Either this book could have a second edition with a lot more content covering finding data problems, resolving those problems intelligently (for instance, resolving missing data is basically left as "either drop the effected records" or "use the mean as a replacement or the missing value," but there are alternative methods which may be more suitable), what data problems will cause issues in OLS regression, logistic regression, and machine learning; And how to practically select variables and a model. I feel like the book gave me some tools to apply (like a small box of tools you might purchase from a hardware store), but left a lot out. So now I'm in deep water trying to figure out why my logistic regression isn't predictive enough and what I can do about it. Is it the data and how I fixed variables? Is it the variables I've selected? Should I have used automated variable selection techniques? Or just manually tried different variables? How does an experienced practitioner approach these problems? I know they iterate: explore data, clean data, select variables, select model, test model, look at data, change data, change variables, etc... but practically speaking what does it look like? In the book they offer a hand-coded basic variable selection script, and mention that one could also use stepwise variable selection. In the real world I'm reasonably sure that this is not actually done--mostly because their selection script does about as well as stepwise at selecting appropriate variables. There are many other better ways of selecting variables, I've discovered, and I wish that they'd discussed some of those ways (pros and cons), and shown how to conduct them in a meaningful fashion. Same thing with building a model. In my case, I have a whole bunch of variables, limited data (about 2000 records, with the desired outcome only occurring in 120 of those), and the automated tools (various R packages I've discovered and applied) either take a long time to run and/or yield poor results. But if not automated tools then what? Manually add variables and ANOVA test the difference between the first and second model? I'd just like more...more discussion and elaboration and examples of how practical data science is conducted. This book seems like it does a fantastic job as an introduction to the topic, but you'll quickly find that you'll be in deep water without a clue how to swim--as in my case. You'll be left to your own devices, and find yourself wishing, as I do, that there was more in the book (or another book) that I could study after this one which would help take me from beginner data scientist to intermediate. Overall, I'm very glad I bought and read the book. ### ⭐⭐⭐⭐⭐ "The" book to have if you want to be a data scientist and working with R *by I***L on February 25, 2019* Hands-on textbook that covers pretty much all aspects of data science (with keen attention to business demands). Importantly, it doesn't shy away from discussing statistical details behind the most common routines in machine learning, which I really appreciated as I was tired of typical DS books that take a black box approach that just shows "how" without explaining the "why". I think it's worth having a copy of this book irrespective of whether you are a beginner to data science or a veteran. Highly recommended! If you buy a hard copy, I would also recommend having a look at the colorful figures in the companion soft copy of the book. ### ⭐⭐⭐⭐ A good how-to with consideration to business problems *by B***N on December 21, 2014* This is the most practical R book on enterprise approach to data analytics. Rarely any book that can spare several chapters on preparing data, which in fact build the foundation of a good modeling. However, I would prefer the data cleansing and the big data algorithm on data mining algorithm be expanded further. As a reader, I need additional exploration with other materials rather that only this book. --- ## Why Shop on Desertcart? - 🛒 **Trusted by 1.3+ Million Shoppers** — Serving international shoppers since 2016 - 🌍 **Shop Globally** — Access 737+ million products across 21 categories - 💰 **No Hidden Fees** — All customs, duties, and taxes included in the price - 🔄 **15-Day Free Returns** — Hassle-free returns (30 days for PRO members) - 🔒 **Secure Payments** — Trusted payment options with buyer protection - ⭐ **TrustPilot Rated 4.5/5** — Based on 8,000+ happy customer reviews **Shop now:** [https://www.desertcart.com.sa/products/1704337-practical-data-science-with-r](https://www.desertcart.com.sa/products/1704337-practical-data-science-with-r) --- *Product available on Desertcart Saudi Arabia* *Store origin: SA* *Last updated: 2026-10-17*