From bb21cc2fa35c7ac05d14db15a1ff65f28e31a5c6 Mon Sep 17 00:00:00 2001 From: Emily Bovee Date: Sun, 14 Apr 2024 22:27:17 -0500 Subject: [PATCH 1/3] first round of edits ch2 --- 02-how-to-use.Rmd | 60 ++++++++++++----------------------------------- 1 file changed, 15 insertions(+), 45 deletions(-) diff --git a/02-how-to-use.Rmd b/02-how-to-use.Rmd index 8261e436..36c89e82 100644 --- a/02-how-to-use.Rmd +++ b/02-how-to-use.Rmd @@ -10,11 +10,7 @@ learning a foreign language, it is not just about mastering vocabulary. It's als about learning the language's norms, its underlying structure, and the metaphors that hold the whole thing together. -The beginning of the learning journey is particularly challenging because it -feels slow. If you have experience as an educator or consultant, you already -have efficient solutions you use in your day-to-day work. Introducing code to -your workflow slows you down at first because you won't be as fast as you are -with your favorite spreadsheet software. However, you're probably reading this book because you realize that learning how to analyze data using R is like investing in your own personal infrastructure---it takes time while you're building the initial skills, but the investment pays off when you start solving complex problems faster and at scale. One person we spoke with shared this story about their learning journey: +The beginning of the learning journey is particularly challenging because it feels slow. If you have experience as an educator or consultant, you already have efficient solutions you use in your day-to-day work. Introducing code to your workflow slows you down at first because you won't be as fast as you are with your favorite spreadsheet software. However, you're probably reading this book because you realize that learning how to analyze data using R is like investing in your own personal infrastructure---it takes time while you're building the initial skills, but the investment pays off when you start solving complex problems faster and at scale. One person we spoke with shared this story about their learning journey: > The first six months were hard. I knew how quickly I could do a pivot table > in Excel. It took longer in R because I had to go through the syntax and take @@ -23,47 +19,26 @@ with your favorite spreadsheet software. However, you're probably reading this b > first few months. Our message is this: learning R for your education job is doable, challenging, -and rewarding all at once. We wrote this book for you because we do this work -every day. We're not writing as education data science masters. We're writing as -people who learned R and data science *after* we chose education. And like you, -improving the lives of students is our daily practice. Learning to use R and data -science helped us do that. Join us in enjoying all that comes with R and data science---both the challenge of -learning and the joy of solving problems in creative and efficient ways. +and rewarding all at once. We wrote this book for you because we do this work every day. We're not writing as education data science masters. We're writing as people who learned R and data science *after* we chose education. And like you, improving the lives of students is our daily practice. Learning to use R and data +science helped us do that. Join us in enjoying all that comes with R and data science---both the challenges of +learning and the joys of solving problems in creative and efficient ways. ## Different strokes for different data scientists in education -As we learned in the introduction, it's tough to define data science in education -because people are educated in all kinds of settings and in all kinds of age groups. Education organizations -require different roles to make it work, which creates different kinds of data -science uses. A teacher's approach to data analysis is different from an -administrator's or an operations manager's. - -We also know that learning data science and R is not in the typical job -description. Most readers of this book are educators working with data and -looking to expand their tools. You might even be an educator who *doesn't* -work with data, but you've discovered a love for learning about the lives of -students through data. Either way, learning data science and R is probably not -in your job description. - -Like most professionals in education, you've got a full work schedule and -challenging demands in the name of improving the student experience. Your busy -workday doesn't include regular professional development time or self-driven -learning. You also have a life outside of work, including family, hobbies, and -relaxation. We struggle with this ourselves, so we've designed this book to be -used in lots of different ways. The important part in learning this material is -to establish a routine that allows you to engage and practice the content every -day, even if for just a few minutes at a time. That will make the content -ever-present in your mind and will help you shift your mindset so you start seeing +As we learned in the introduction, it's tough to define data science in education because education spans many contexts and age groups. Education organizations require different roles to make them work, which creates different kinds of data +science uses. A teacher's approach to data analysis is different from an administrator's or an operations manager's. + +We also know that learning data science and R is not in the typical job description. Most readers of this book are educators working with data and looking to expand their tools. You might even be an educator who *doesn't* work with data, but you've discovered a love for learning about the lives of students through data. Either way, learning data science and R is probably not in your job description. + +Like most professionals in education, you've got a full work schedule and challenging demands in the name of improving the student experience. Your busy workday doesn't include regular professional development time or dedicated time for self-driven learning. You also have a life outside of work, including family, hobbies, and +relaxation. We struggle with this ourselves, so we've designed this book to be used in lots of different ways. The important part in learning this material is to establish a routine that allows you to engage and practice the content every day, even if for just a few minutes at a time. That will make the contentever-present in your mind and will help you shift your mindset so you start seeing even more opportunities for practice. -We want all readers to have a rewarding experience, and so we believe there -should be different ways to use this book. Here are some of those ways: +We want all readers to have a rewarding experience, and so we believe there should be different ways to use this book. Here are some of those ways: ### Read the book cover to cover (and how to keep going) -We wrote this book assuming you're at the start of your journey learning R and -using data science in your education job. The book takes you from installing R -to practicing more advanced data science skills like text analysis. +We wrote this book assuming you're at the start of your journey learning R and using data science in your education job. The book takes you from installing R to practicing more advanced data science skills like text analysis. If you've never written a line of R code, we welcome you to the community! We wrote this book for you. Consider reading the book cover to cover and doing all @@ -108,14 +83,9 @@ This book is primarily about learning to use R as a tool for data science in edu ### Read through the walkthroughs and run the code -If you're experienced in data analysis using R, you may be interested in -starting with the walkthroughs. Each walkthrough is designed to demonstrate -basic analytic routines using datasets that look familiar to people working in the education field. +If you're experienced in data analysis using R, you may be interested in starting with the walkthroughs. Each walkthrough is designed to demonstrate basic analytic routines using datasets that look familiar to people working in the education field. It can be hard to know where to begin, partly because it is a challenge to formulate a goal or path to get there without first understanding what data analysis can do. It is sometimes easier to learn with a concrete example in mind, rather than to try to learn the concepts and principles in the abstract. -In this approach, we suggest readers be intentional about what they want to -learn from the walkthroughs. For example, readers may seek out examples of -aggregated datasets, exploratory data analysis, the {ggplot2} package, or the -`pivot_longer()` function. Read the walkthrough and run the code in your R console as +In this approach, we suggest readers be intentional about what they want tolearn from the walkthroughs. For example, readers may seek out examples of aggregated datasets, exploratory data analysis, the {ggplot2} package, or the `pivot_longer()` function. Read the walkthrough and run the code in your R console as you go. After you successfully run the code, experiment with the functions and techniques you learned by changing the code and seeing new results (or new error messages!). After running the code in the walkthroughs, reflect on how what you From 246f9074ce2b67dcab7d4944dac2a68220998e2c Mon Sep 17 00:00:00 2001 From: Emily Bovee Date: Tue, 16 Apr 2024 22:34:11 -0500 Subject: [PATCH 2/3] round 2 of edits to Ch2 How to Use --- 02-how-to-use.Rmd | 120 +++++++++++++--------------------------------- 1 file changed, 34 insertions(+), 86 deletions(-) diff --git a/02-how-to-use.Rmd b/02-how-to-use.Rmd index 36c89e82..48ff3672 100644 --- a/02-how-to-use.Rmd +++ b/02-how-to-use.Rmd @@ -2,15 +2,13 @@ **Abstract** -This chapter describes different ways the reader can use these book to learn about using R and data science tools in their education job. Job descriptions, lifestyles, and programming experience differ for everyone. Learning how to program in R on the job or at home will also look different to each reader. Applying R and data science tools in an education job requires learning these skills in a practical and meaningful context. The chapter describes three suggested ways to learn from the book, taking the reader’s experience into account. It also introduces the reader to ways they can support and contribute to the book’s content. This reinforces the theme of building content based on stories from the data science in education community. +This chapter describes different ways the reader can use these book to learn about using R and data science tools in their education job. Job descriptions, lifestyles, and programming experience differ for everyone. Learning how to program in R on the job or at home will also look different to each reader. Applying R and data science tools in an education job requires learning these skills in a practical and meaningful context. The chapter describes three suggested ways to learn from the book, so that the reading experience can be customized to different learning journeys. It also introduces the reader to ways they can support and contribute to the book’s content. We've heard it from fellow data scientists and experienced it ourselves---learning a programming language is hard. Like -learning a foreign language, it is not just about mastering vocabulary. It's also -about learning the language's norms, its underlying structure, and the metaphors -that hold the whole thing together. +learning a foreign language, it is not just about mastering vocabulary. It's also about learning the language's norms, its underlying structure, and the metaphors that hold the whole thing together. -The beginning of the learning journey is particularly challenging because it feels slow. If you have experience as an educator or consultant, you already have efficient solutions you use in your day-to-day work. Introducing code to your workflow slows you down at first because you won't be as fast as you are with your favorite spreadsheet software. However, you're probably reading this book because you realize that learning how to analyze data using R is like investing in your own personal infrastructure---it takes time while you're building the initial skills, but the investment pays off when you start solving complex problems faster and at scale. One person we spoke with shared this story about their learning journey: +The beginning of the learning journey is particularly challenging because it feels slow. In your current work environment, you already have efficient solutions you use to accomplish your day-to-day work. Introducing code to your workflow will slow you down at first because you won't be as fast as you are with your favorite spreadsheet software. However, you're probably reading this book because you realize that learning how to analyze data using R is like investing in your own personal infrastructure---it takes time while you're building the initial skills, but the investment pays off when you start solving complex problems faster and at scale. One person we spoke with shared this story about their learning journey: > The first six months were hard. I knew how quickly I could do a pivot table > in Excel. It took longer in R because I had to go through the syntax and take @@ -18,130 +16,80 @@ The beginning of the learning journey is particularly challenging because it fee > better data scientist. I'm so glad I thought that way, but it was hard the > first few months. -Our message is this: learning R for your education job is doable, challenging, -and rewarding all at once. We wrote this book for you because we do this work every day. We're not writing as education data science masters. We're writing as people who learned R and data science *after* we chose education. And like you, improving the lives of students is our daily practice. Learning to use R and data -science helped us do that. Join us in enjoying all that comes with R and data science---both the challenges of -learning and the joys of solving problems in creative and efficient ways. +Our message is this: learning R for your education job is doable, challenging, and rewarding all at once. We wrote this book for you because we do this work every day. We're not writing as people who have mastered all there is to know about education data science. We're writing as people who learned R and data science *after* we chose education. And like you, improving the lives of students is our daily practice. Learning to use R and data science helped us do what matters most to us. Join us in enjoying all that comes with R and data science---both the challenges of learning and the joys of solving problems in creative and efficient ways. ## Different strokes for different data scientists in education -As we learned in the introduction, it's tough to define data science in education because education spans many contexts and age groups. Education organizations require different roles to make them work, which creates different kinds of data -science uses. A teacher's approach to data analysis is different from an administrator's or an operations manager's. +As we learned in the introduction, it's tough to define data science in education because education spans many contexts and age groups. Education organizations require different roles to make them work, which creates different kinds of uses for data science skills and tools. The approach to adopting data science will differ depending on job role, while some principles will generalize across contexts. A teacher's approach to data analysis is different from an administrator's. -We also know that learning data science and R is not in the typical job description. Most readers of this book are educators working with data and looking to expand their tools. You might even be an educator who *doesn't* work with data, but you've discovered a love for learning about the lives of students through data. Either way, learning data science and R is probably not in your job description. +We also know that learning data science and R is not in the typical job description. Many readers of this book are educators working with data and looking to expand their tools. You might even be an educator who *doesn't* work with data, but you've discovered an interest in learning about the lives of students through data. Either way, learning data science and R is probably not in your job description. Like most professionals in education, you've got a full work schedule and challenging demands in the name of improving the student experience. Your busy workday doesn't include regular professional development time or dedicated time for self-driven learning. You also have a life outside of work, including family, hobbies, and -relaxation. We struggle with this ourselves, so we've designed this book to be used in lots of different ways. The important part in learning this material is to establish a routine that allows you to engage and practice the content every day, even if for just a few minutes at a time. That will make the contentever-present in your mind and will help you shift your mindset so you start seeing -even more opportunities for practice. +relaxation. We struggle with this ourselves, so we've designed this book to be used in lots of different ways. One approach to learning this material is to establish a routine that allows you to engage and practice the content every day, even if for just a few minutes at a time. That will make the content ever-present in your mind and will help you shift your mindset so you start seeing +even more opportunities for practice. If daily practice is out of reach for you right now, that's okay! We want the book to fit into your life, however that may look. -We want all readers to have a rewarding experience, and so we believe there should be different ways to use this book. Here are some of those ways: +We want all readers to have a rewarding experience, and so we support you in however you'd like to use this book. Here are a couple of options: ### Read the book cover to cover (and how to keep going) -We wrote this book assuming you're at the start of your journey learning R and using data science in your education job. The book takes you from installing R to practicing more advanced data science skills like text analysis. - -If you've never written a line of R code, we welcome you to the community! We -wrote this book for you. Consider reading the book cover to cover and doing all -the analysis walkthroughs. Remember that you'll get more from a few minutes of -practice every day than you will from long hours of practice every once in -a while. Typing code every day, even if it doesn't always run, is a daily practice -that invites learning and "a-ha" moments. We know how easy it is to avoid coding -when it doesn't feel successful (we've been there), so we've designed this book to deliver frequent -small wins to keep the momentum going. But even then, we all eventually hit a -wall in our learning. When that happens, take a break and then come back and +We wrote this book assuming you're at the start of your journey of learning R and of using data science in your education job. The book takes you from installing R to practicing more advanced data science skills like text analysis. + +If you've never written a line of R code, we welcome you to the community! We wrote this book for you. Consider reading the book cover to cover and doing all the analysis walkthroughs. We designed the walkthroughs to help give a high-level view of an analysis from start to finish. In our experience, beginning to learn R can be hard, in part because of trying to figure out where to begin. It is a challenge to formulate a goal or path to get there without first understanding what data analysis can do. We wrote the walkthroughs because it is sometimes easier to learn with a concrete example in mind, rather than to try to learn the concepts and principles in the abstract. + +Remember that you'll get more benefit from a few minutes of practice every day than you will from long hours of practice every once in a while. Typing code every day, even if it doesn't always run, is a daily practice that invites learning and "a-ha" moments. We know how easy it is to avoid coding when it doesn't feel successful (we've been there), so we've designed this book to deliver frequent small wins to keep the momentum going. But even so, we all eventually hit a wall in our learning. When that happens, take a break and then come back and keep coding. When daily coding becomes a habit, so does the learning. -If you get stuck in an advanced chapter and you need a break, try reviewing an -earlier chapter. You'll be surprised at how much you learn from reviewing old -material with the benefit of new experiences. Sometimes that kind of back-to-basics +If you get stuck in an advanced chapter and you need a break, try reviewing an earlier chapter. You'll be surprised at how much you learn from reviewing old material with the benefit of new experiences. Sometimes that kind of back-to-basics attitude is what we need to get a fresh perspective on new challenges. ### Pick a chapter of interest and start there -We interviewed R users in education as research for this book. We chose people -with different levels of experience in R, in the education field, and in statistics. -We asked each interviewee to rate their level of experience on a scale from 1 -to 5, with 1 being "no experience" and 5 being "very experienced". You can try this -now---take a moment to rate your level of experience in: +We interviewed R users in education as research for this book. We chose people with different levels of experience in R, in the education field, and in statistics. +We asked each interviewee to rate their level of experience on a scale from 1 to 5, with 1 being "no experience" and 5 being "very experienced". You can try this now---take a moment to rate your level of experience in: - Using R - Education as a field - Statistics -If you rated yourself as a 1 in Using R, we recommend reading the book from -beginning to end as part of a daily practice. If you rated yourself higher than -a 1, consider reviewing the table of contents and skimming all the -chapters first. If a particular chapter calls to you, feel free to start your -daily practice there. Eventually, we do hope you choose to experience the whole -book, even if you start somewhere in the middle. +If you rated yourself as a 1 in Using R, we recommend reading the book from beginning to end as part of a daily practice. If you rated yourself higher than a 1, consider reviewing the table of contents and skimming all the chapters first. If a particular chapter calls to you, feel free to start your daily practice there. Eventually, we do hope you choose to experience the whole book, even if you start somewhere in the middle. -For example, you might be working through a specific use case in your education -job---perhaps you are analyzing student quiz scores, evaluating a school program, introducing a data science technique to your teammates, or designing data dashboards. If this describes your situation, feel free to find a section in the -book that inspires you or shows you techniques that apply to your project. +For example, you might be working through a specific use case in your education job---perhaps you are analyzing student quiz scores, evaluating a school program, introducing a data science technique to your teammates, or designing data dashboards. If this describes your situation, feel free to find a section in the book that inspires you or shows you techniques that apply to your project. This book is primarily about learning to use R as a tool for data science in education. Your experience level with R should be the main factor when you decide how to enjoy the book. But do consider how you rated your level of experience with education and statistics. If these are areas you want to focus on, take your time understanding the education scenarios and statistics techniques we describe. All three disciplines are important parts of being a data scientist in education. ### Read through the walkthroughs and run the code -If you're experienced in data analysis using R, you may be interested in starting with the walkthroughs. Each walkthrough is designed to demonstrate basic analytic routines using datasets that look familiar to people working in the education field. It can be hard to know where to begin, partly because it is a challenge to formulate a goal or path to get there without first understanding what data analysis can do. It is sometimes easier to learn with a concrete example in mind, rather than to try to learn the concepts and principles in the abstract. +If you're experienced in data analysis using R, you may be interested in starting with the walkthroughs. Each walkthrough is designed to demonstrate basic analytic routines using datasets that look familiar to people working in the education field. -In this approach, we suggest readers be intentional about what they want tolearn from the walkthroughs. For example, readers may seek out examples of aggregated datasets, exploratory data analysis, the {ggplot2} package, or the `pivot_longer()` function. Read the walkthrough and run the code in your R console as -you go. After you successfully run the code, experiment with the functions and -techniques you learned by changing the code and seeing new results (or new error -messages!). After running the code in the walkthroughs, reflect on how what you -learned can be applied to the datasets, problems, and analytic routines in your -education work. +In this approach, we suggest readers be intentional about what they want to learn from the walkthroughs. For example, readers may seek out examples of aggregated datasets, exploratory data analysis, the {ggplot2} package, or the `pivot_longer()` function. Read the walkthrough and run the code as you go. After you successfully run the code, experiment with the functions and techniques you learned by changing the code and seeing new results (or new error messages!). After running the code in the walkthroughs, reflect on how what you +learned can be applied to the datasets, problems, and analytic routines in your education work. -One last note on this approach to the book: we believe that doing data science in education -using R is, at its heart, an endeavor aimed at improving the student experience. The skills taught in the +One last note on this approach to the book: we believe that doing data science in education using R is, at its heart, an endeavor aimed at improving the student experience. The skills taught in the walkthroughs are only one part of doing data science in education using R. -As an experienced R user, you know that this endeavor involves complex problems and collaboration. Since part of your task may be to convince others around you of the merits of your analytic tools and approaches, we've written this book with that context in mind. [Chapter 15](#c15) in particular explores ways to introduce these skills to your education -job and invite others into analytic activities. We believe you'll glean useful perspectives from chapters on concepts you're already familiar with, too. + +As an experienced R user, you know that this endeavor involves complex problems and collaboration. Since part of your task may be to convince others around you of the merits of your analytic tools and approaches, we've written this book with that context in mind. [Chapter 15](#c15) in particular explores ways to introduce these skills to your education job and invite others into analytic activities. We believe you'll glean useful perspectives from chapters on concepts you're already familiar with, too. ## A note on statistics Data science is the intersection between content expertise, -programming, and statistics. You'll want to grow all three of these as you learn -more about using data science in your education job. Your education knowledge -will lead you to the right problems, your statistics skills will bring rigor to -your analysis, and your programming skills will scale your analysis to reach +programming, and statistics. You'll want to grow all three of these as you learn more about using data science in your education job. Your education knowledge will lead you to the right problems, your statistics skills will bring rigor to your analysis, and your programming skills will scale your analysis to reach more people. -What happens when we remove one of these pieces? Consider a data scientist -working in education who is an expert programmer and statistician but has not -learned about the real-life conditions that generate education data. She might -make analysis decisions that overlook the nuances in the data. As another example, consider a data -scientist who is an expert statistician and an education veteran, but who has not -learned to code. He will find it difficult to scale his analysis up, thereby foregoing the chance to make the -largest possible improvement to the student experience. Finally, -consider a data scientist who is an expert programmer and an education veteran. -She can only scale surface-level analysis and might miss chances to understand causal -relationships or predict student outcomes. - -In this book, we will spend a lot of time learning R by way of recognizable -education data examples. But doing a deep dive into statistics and how to use -statistical techniques responsibly is better covered by books dedicated solely to the topic. It's -hard to overstate how important this part of the learning is on the lives of -students and educators. One education data scientist we spoke to said this about -the difference between building a model for an online retailer and building a -model in education: +What happens when we remove one of these pieces? Consider a data scientist working in education who is an expert programmer and statistician but has not learned about the real-life conditions that generate education data. She might make analysis decisions that overlook the nuances in the data, and she may make ill-advised recommendations because of those decisions. As another example, consider a data scientist who is an expert statistician and an education veteran, but who has not learned to code. He will find it difficult to scale his analysis up, thereby foregoing the chance to make the largest possible improvement to the student experience. Finally, consider a data scientist who is an expert programmer and an education veteran. Because she is still learning statistics, she can only scale surface-level analysis and might miss chances to understand causal relationships or predict student outcomes. + +In this book, we will spend a lot of time learning R by way of recognizable education data examples. But doing a deep dive into statistics and how to use statistical techniques responsibly is better covered by books dedicated solely to the topic. It's +hard to overstate how important this part of the learning is on the lives of students and educators. One education data scientist we spoke to said this about the difference between building a model for an online retailer and building a model in education: >It’s not a big deal if an online shopper gets mistakenly shown 1000 brooms but if I got my model wrong and we close a school, that will change a child's entire life. -We want this book to be your go-to R reference as you start integrating data -science tools into your education job. Our aim is to help you learn R by -teaching data science techniques using education -datasets. We'll demonstrate statistics techniques like hypothesis testing and -model building and how to run these operations in R. However, the explanations in our chapters will not provide a complete background about the statistical techniques. +We want this book to be your go-to R reference as you start integrating data science tools into your education job. Our aim is to help you learn R by teaching data science techniques using education +datasets. We'll demonstrate statistics techniques like hypothesis testing and model building and how to run these operations in R. However, the explanations in our chapters will not provide a complete background about the statistical techniques. -We wrote within these boundaries because we believe that the technical and -ethical use of statistics techniques deserves its own space. If you already have a foundation in statistics, you will learn how to implement some familiar processes in R. If you have no foundation in statistics, you will be able to take a satisfying leap forward in your learning by successfully using R to run the models and experiencing the model interpretations in our +We wrote within these boundaries because we believe that the technical and ethical use of statistics techniques deserves its own space. If you already have a foundation in statistics, you will learn how to implement some familiar processes in R. If you have no foundation in statistics, you will be able to take a satisfying leap forward in your learning by successfully using R to run the models and experiencing the model interpretations in our walkthroughs. We provide enough background for you to understand the purpose of the analysis and its results. We encourage you to explore other excellent books like [*Learning Statistics With R*](https://learningstatisticswithr.com/) (https://learningstatisticswithr.com/) [@learningstatswithr], as you learn the required nuances of applying statistical techniques to scenarios outside our walkthroughs. ## What this book is not about -While we wrote *Data Science in Education Using R* to be a wide-ranging introduction -to the topic, there is a great deal that this book is not about. Some of these topics -are those that we would have liked to have been able to include, but we did not because they did +While we wrote *Data Science in Education Using R* to be a wide-ranging introduction to the topic, there is a great deal that this book is not about. Some of these topics are those that we would have liked to have been able to include, but we did not because they did not fit our intention of providing a solid foundation in doing data science in education. We chose to not include other topics because, frankly, excellent resources for those topics already exist. We detail some of what we had to not include in the book here. From 7ee296c08684e2b0de696585d13a509b71e0e2ed Mon Sep 17 00:00:00 2001 From: Emily Bovee Date: Tue, 16 Apr 2024 22:42:01 -0500 Subject: [PATCH 3/3] batch 3 of edits to ch 2 --- 02-how-to-use.Rmd | 26 ++++++++------------------ 1 file changed, 8 insertions(+), 18 deletions(-) diff --git a/02-how-to-use.Rmd b/02-how-to-use.Rmd index 48ff3672..d3ab03a6 100644 --- a/02-how-to-use.Rmd +++ b/02-how-to-use.Rmd @@ -89,10 +89,8 @@ walkthroughs. We provide enough background for you to understand the purpose of ## What this book is not about -While we wrote *Data Science in Education Using R* to be a wide-ranging introduction to the topic, there is a great deal that this book is not about. Some of these topics are those that we would have liked to have been able to include, but we did not because they did -not fit our intention of providing a solid foundation in doing data science in education. -We chose to not include other topics because, frankly, excellent resources for those topics already exist. We -detail some of what we had to not include in the book here. +While we wrote *Data Science in Education Using R* to be a wide-ranging introduction to the topic, there is a great deal that this book is not about. Some of these topics are those that we would have liked to have been able to include, but we did not because they did not fit our intention of providing a solid foundation in doing data science in education. +We chose to not include other topics because, frankly, excellent resources for those topics already exist. We detail some of what we had to not include in the book here. - Git/GitHub: Git and GitHub are version control software programs, which means that they help keep track of different versions of coding files and specific changes that were made for each version. Git and GitHub are parts of many data scientists' workflows for solo or collaborative work. However, there is a steep learning curve and these tools are not necessary to get started with coding in R. An outstanding introduction to Git and Github is @bryan2020's freely available book [*Happy Git with R*](https://happygitwithr.com/) (https://happygitwithr.com/). @@ -112,29 +110,21 @@ If you find this book useful, please support it by: * Starring the GitHub repository for the book (https://github.com/data-edu/data-science-in-education) * Starring the GitHub repository for the {dataedu} package (https://github.com/data-edu/dataedu) * Reviewing it (e.g., on Amazon or Goodreads) -* Buying a copy, especially +* Buying a copy * Letting others in education and data science know about it! ## Contributing to the book -We designed this book to be useful and practical for our readers in education. We wrote it as a guide to getting up and running in R, but we know this book does not comprehensively cover every topic related to R. We did this to create a -reference that is not intimidating to new users and that creates frequent, -small wins while learning to use R. +We designed this book to be useful and practical for our readers in education. We wrote it as a guide to getting up and running in R, but we know this book does not comprehensively cover every topic related to R. We did this to create a reference that is not intimidating to new users and that creates frequent, small wins while learning to use R. -One question we asked ourselves was: how do we expand this work as data science in education expands as a field? We want readers of this book to be equipped with an agile skillset, and we want this book to continue to provide that even as new R packages are developed and new methods arise. We wrote this book in the open on GitHub so that community members can help us -evolve the work, even after it is formally published. +One question we asked ourselves was: how do we expand this work as data science in education expands as a field? We want readers of this book to be equipped with an agile skillset, and we want this book to continue to provide that even as new R packages are developed and new methods arise. We wrote this book in the open on GitHub so that community members can help us evolve the work, even after it is formally published. We are excited that this idea of open collaboration is working! Indeed, this second edition incorporates feedback we received from readers and is much better because of it. -We want this to be the book new data scientists in education have with them as -they grow their craft. To achieve that goal, it's important to us that the stories and -examples in the book are based on **your** stories and examples. Therefore, we've -built ways for you to share with us. +We want this to be the book new data scientists in education have with them as they grow their craft. To achieve that goal, it's important to us that the stories and examples in the book are based on **your** stories and examples. Therefore, we've built ways for you to share with us. If you have some experience with Git and want to contribute that way, here's how you can contribute: - - Submit an "issue" to our [GitHub repository](https://github.com/data-edu/data-science-in-education) (https://github.com/data-edu/data-science-in-education/issues) that describes - a data science problem that is unique to the education setting - - Submit a pull request to share a solution for the problems discussed in the - book to the education setting + - Submit an "issue" to our [GitHub repository](https://github.com/data-edu/data-science-in-education) (https://github.com/data-edu/data-science-in-education/issues) that describes a data science problem that is unique to the education setting + - Submit a pull request to share a solution for the problems discussed in the book to the education setting - Share an anonymized dataset for use in the book (or a future version of it) If you are new to data science in education, welcome! We would love to have your feedback by [email](mailto:authors@datascienceineducation.com) (authors@datascienceineducation.com).