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Communicating with Data: The Art of Writing for Data Science [Kõva köide]

(Lecturer in Statistical & Data Sciences, Smith College), (Professor of Statistics and Associate Dean for Undergraduate Education in the Division of Computing, Data Science, and Society, University of California)
  • Formaat: Hardback, 352 pages, kõrgus x laius x paksus: 240x160x24 mm, kaal: 710 g
  • Ilmumisaeg: 25-Mar-2021
  • Kirjastus: Oxford University Press
  • ISBN-10: 0198862741
  • ISBN-13: 9780198862741
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  • Formaat: Hardback, 352 pages, kõrgus x laius x paksus: 240x160x24 mm, kaal: 710 g
  • Ilmumisaeg: 25-Mar-2021
  • Kirjastus: Oxford University Press
  • ISBN-10: 0198862741
  • ISBN-13: 9780198862741
Teised raamatud teemal:
Communication is a critical yet often overlooked part of data science. Communicating with Data aims to help students and researchers write about their insights in a way that is both compelling and faithful to the data. General advice on science writing is also provided, including how to distill findings into a story and organize and revise the story, and how to write clearly, concisely, and precisely. This is an excellent resource for students who want to learn how to write about scientific findings, and for instructors who are teaching a science course in communication or a course with a writing component.

Communicating with Data consists of five parts. Part I helps the novice learn to write by reading the work of others. Part II delves into the specifics of how to describe data at a level appropriate for publication, create informative and effective visualizations, and communicate an analysis pipeline through well-written, reproducible code. Part III demonstrates how to reduce a data analysis to a compelling story and organize and write the first draft of a technical paper. Part IV addresses revision; this includes advice on writing about statistical findings in a clear and accurate way, general writing advice, and strategies for proof reading and revising. Part V offers advice about communication strategies beyond the page, which include giving talks, building a professional network, and participating in online communities. This book also provides 22 portfolio prompts that extend the guidance and examples in the earlier parts of the book and help writers build their portfolio of data communication.

Arvustused

This book about technical writing is particularly suitable (and necessary) for students in data science, or in any mathematical science. It begins with chapters on how to read technical articles, science news stories, and press releases ("examine the argument"); continues with chapters on how to describe and present data and code; advises how to get started writing ("who is the audience?"); and emphasizes the importance of editing and revising. A concluding chapter offers 22 imaginative exercises. * Mathematics Magazine * excellent presentation. * T. J. Rao, zb Math Open *

Part I Reading to Write
1(48)
1 Reading Science Articles
3(19)
1.1 Identify the Elements of a Data Analysis
4(3)
1.1.1 Determining if the Golden State Warriors Have Hot Hands
5(2)
1.2 Examine the Argument
7(3)
1.2.1 Assessing a New Method to Weigh a Donkey
8(1)
1.2.2 Helping the Scientific Community Adopt a New Standard
9(1)
1.3 Map the Organization
10(2)
1.3.1 Organizing the Announcement of an Exoplanet
11(1)
1.4 A Guide to Reading as a Writer
12(3)
1.5 How to Weigh a Donkey in the Kenyan Countryside
15(3)
1.6 Notes
18(1)
1.7 References
19(1)
1.8 Activities
20(2)
1.8.1 Reading to Write
20(1)
1.8.2 Sharing Article Summaries
20(1)
1.8.3 Skipping Around as We Read
21(1)
2 Reading Materials Written for Broader Publics
22(27)
2.1 Reading Press Releases
23(7)
2.1.1 Identify the Elements-The Five Ws and H
23(3)
2.1.2 Examine the Argument
26(2)
2.1.3 Map the Organization-The Inverted Pyramid
28(2)
2.2 Reading a Press Release and Its Technical Article
30(3)
2.2.1 Identify the Elements
31(1)
2.2.2 Examine the Argument
31(2)
2.2.3 Map the Organization
33(1)
2.3 Reading Blogs
33(5)
2.3.1 Identify the Motivation
34(1)
2.3.2 Examine the Argument
34(3)
2.3.3 Map the Organization
37(1)
2.4 Reading a Sample Blog
38(3)
2.4.1 Identify the Motivation
38(1)
2.4.2 Examine the Argument
38(2)
2.4.3 Map the Organization
40(1)
2.5 Notes
41(1)
2.6 References
42(1)
2.7 Activities
43(6)
2.7.1 Identifying the Ws in a Press Release
43(1)
2.7.2 Reading a Press Release
44(1)
2.7.3 Comparing a News Story and Technical Article
44(2)
2.7.4 Identify Elements of Blog Style
46(3)
Part II Preparing to Write
49(76)
3 Describing Data
51(20)
3.1 Data Provenance
51(5)
3.1.1 Data Collection
52(1)
3.1.2 Data Preparation
53(2)
3.1.3 Multiple Sources
55(1)
3.2 Describing Variables
56(1)
3.2.1 Codebooks
56(1)
3.3 Simple Summaries
57(4)
3.4 Tracking the Analysis
61(2)
3.4.1 Replicable Results
61(1)
3.4.2 Reproducible Wrangling
62(1)
3.4.3 Accessible Data
62(1)
3.5 Notes
63(1)
3.6 References
64(2)
3.7 Activities
66(5)
3.7.1 Editing Data Descriptions
66(1)
3.7.2 Describing a Complex Survey
66(3)
3.7.3 Translating a Codebook into a Data Description
69(1)
3.7.4 Writing about Summary Statistics
69(2)
4 Communicating Through Statistical Graphs
71(33)
4.1 Matching Plot to Data
71(1)
4.2 Reading Plots
71(8)
4.2.1 Reading Distributions
72(3)
4.2.2 Reading Relationships and Trends
75(4)
4.3 Comparisons in Multivariate Settings
79(4)
4.4 Examining the Visual Argument
83(13)
4.4.1 Incorporating the Data Design
83(2)
4.4.2 Choosing the Scale to Reveal Structure
85(3)
4.4.3 Aggregating and Smoothing Data
88(3)
4.4.4 Facilitate Meaningful Comparisons
91(3)
4.4.5 Adding Contextual Information
94(2)
4.5 Editing and Revising
96(3)
4.6 Notes
99(1)
4.7 References
100(1)
4.8 Activities
100(4)
4.8.1 Deconstruct and Reconstruct a Plot
100(1)
4.8.2 Finding a One-Minute Revelation
101(1)
4.8.3 Turning the Table on a Simulation Study
102(1)
4.8.4 Review of a Statistical Graph
102(2)
5 Communicating Through Code
104(21)
5.1 Pseudocode
104(2)
5.2 Style Guidelines
106(5)
5.2.1 Naming Conventions
106(1)
5.2.2 Comments and Documentation
106(3)
5.2.3 Whitespace in Expressions
109(1)
5.2.4 Control Structure and Indentation
109(1)
5.2.5 Line Length
110(1)
5.3 Refactoring Code
111(2)
5.4 Coding Principles
113(2)
5.5 Computational Reproducibility
115(2)
5.5.1 Version-Control Tools
116(1)
5.5.2 Notebook Tools
116(1)
5.6 Notes
117(1)
5.7 References
118(1)
5.8 Activities
119(6)
5.8.1 Writing Pseudocode for Simple Statistical Tasks
119(1)
5.8.2 Mapping a Computational Task with Pseudocode
119(1)
5.8.3 Verifying Reproducibility
120(1)
5.8.4 Practicing Code Review
120(1)
5.8.5 Write a Blog Post: Vignette
121(1)
5.8.6 Dirty Dozen Code Recommendations
121(4)
Part III Composing the Story
125(76)
6 Organizing the Story
127
6.1 Creating a Storyboard
127(2)
6.2 Taking Notes on the Storyboard
129(1)
6.3 Iterating
130(1)
6.4 Creating a Storyboard for Drug-Related ED Visits
131(8)
6.4.1 Taking Notes on the DAWN Storyboard
138(1)
6.4.2 Iterate Over the Storyboard
138(1)
6.5 Notes
139(1)
6.6 References
139(1)
6.7 Activities
140
6.7.1 Make a Storyboard
140(1)
6.7.2 Peer Review a Storyboard
141(1)
6.7.3 Find a Blog Thread in Your Work
141(1)
6.7.4 Create a Storyboard for a Discussion Blog
142(1)
6.7.5 Make a Storyboard for a Press Release
143(1)
7 Writing the First Draft
144(7)
7.1 Structure: What Should My Report Look Like?
151(2)
7.2 Audience: For Whom Am I Writing?
153(2)
7.2.1 Secondary Audiences
155(1)
7.3 The Middle
155(11)
7.3.1 Starting Point-The Storyboard
155(2)
7.3.2 Data-Collection Methods
157(3)
7.3.3 Methods of Analysis
160(2)
7.3.4 Results
162(3)
7.3.5 Figures and Captions
165(1)
7.4 The End
166(6)
7.4.1 Discussion
167(4)
7.4.2 Conclusion
171(1)
7.5 The Beginning
172(7)
7.5.1 Background
172(3)
7.5.2 Introduction
175(2)
7.5.3 Abstract
177(1)
7.5.4 Title
178(1)
7.5.5 Keywords
179(1)
7.6 Notes
179(1)
7.7 References
180(2)
7.8 Activities
182(19)
7.8.1 Keep a Brainstorm Diary
182(1)
7.8.2 Writing a First Draft
183(1)
7.8.3 Practice Writing Captions
184(2)
7.8.4 Rewriting a Caption
186(1)
7.8.5 Scope
186(4)
7.8.6 Space
190(1)
7.8.7 Coming up with Titles
190(1)
7.8.8 Titles for Broader Publics
191(1)
7.8.9 Introductions for Broader Publics
191(2)
7.8.10 Conclusions for Broader Publics
193(2)
7.8.11 Drafting a Press Release
195(1)
7.8.12 Focus and Prior Knowledge for Different Audiences
196(5)
Part IV Editing and Revising
201(82)
8 Taking Care with Statistical Terms
203(29)
8.1 Statistical Terms and Everyday Usage
203(4)
8.2 Help the Reader with Statistical Terms
207(1)
8.3 Similar Words with Distinct Meanings
208(5)
8.4 Absolutes
213(1)
8.5 Causal Statements
213(4)
8.5.1 Avoiding Causal Exaggerations in Press Releases
216(1)
8.6 Numbers
217(1)
8.7 Mathematical Expressions
218(3)
8.7.1 Formatting Mathematics
220(1)
8.8 Notes
221(1)
8.9 References
222(2)
8.10 Activities
224(8)
8.10.1 Finding Statistical Terms in Everyday Language
224(1)
8.10.2 Practice Distinguishing Between Similar Words
224(1)
8.10.3 Focused Revision at Sentence Level
225(1)
8.10.4 Writing and Speaking Mathematics
226(1)
8.10.5 Statistical Terms in Press Releases
226(1)
8.10.6 Causal Statements in Press Releases
227(1)
8.10.7 Looking for Causal Statements
227(1)
8.10.8 Blogging to Teach a Statistical Concept
227(2)
8.10.9 Edit a Statistical Wikipedia Entry
229(3)
9 Crafting Words and Sentences
232(34)
9.1 Straightforward Sentences
232(6)
9.1.1 Eliminate Empty Phrases
232(2)
9.1.2 Trim Fat Phrases
234(1)
9.1.3 Reduce Strings of Modifiers
234(1)
9.1.4 Avoid Cliches and Colloquialism
235(1)
9.1.5 Vary Sentences
236(1)
9.1.6 Straighten Convoluted Sentence Structure
237(1)
9.2 Choosing the Right Word
238(5)
9.2.1 Use Concrete Nouns
239(1)
9.2.2 Use Strong Verbs
239(1)
9.2.3 Match a Word's Connotation with the Context
240(1)
9.2.4 Take Care Swapping in Synonyms
241(1)
9.2.5 Avoid Overly Complex Words
241(1)
9.2.6 Eliminate Redundant Adjectives
242(1)
9.2.7 Place Words and Phrases Mindfully
243(1)
9.3 Grammatical Details
243(4)
9.4 Describing Your Findings
247(5)
9.4.1 Tell What You Found, Not the Path You Traveled
247(2)
9.4.2 Provide Helpful Transitions
249(3)
9.5 Breaking Some Traditional Writing Rules as a Blogger
252(4)
9.6 Notes
256(1)
9.7 References
257(1)
9.8 Activities
258(8)
9.8.1 Focused Revision at Sentence Level
258(2)
9.8.2 Writing Transitions
260(2)
9.8.3 Press Release Dirty Half-Dozen
262(1)
9.8.4 Crafting a Wikipedia Section
263(1)
9.8.5 Removing Blog Elements
264(2)
10 Revising: Drafts #2 Through...
266(17)
10.1 Preparing to Rewrite
267(1)
10.2 Strategies for Targeted Revision
267(5)
10.3 Editing with Others
272(4)
10.3.1 Giving Feedback
273(1)
10.3.2 Receiving Feedback
273(3)
10.4 Revising the Argument
276(1)
10.5 Revising for the Intended Audience
277(1)
10.6 Notes
278(1)
10.7 References
279(1)
10.8 Activities
279(4)
10.8.1 Focused Revision at Sentence Level
279(1)
10.8.2 Formal Peer Review
279(1)
10.8.3 Writing Journal
280(3)
Part V Science Writing and You
283(43)
11 Embracing Your Role as a Scientist
285(13)
11.1 Expanding Our Professional Network
285(3)
11.1.1 Formal Talk
286(1)
11.1.2 Lightning/Speed Talk
287(1)
11.1.3 Poster
287(1)
11.1.4 Networking
288(1)
11.2 Building a Research Focus
288(1)
11.3 Fostering a Personal Community
289(2)
11.4 Welcoming Who We Are
291(1)
11.5 Notes
292(1)
11.6 References
293(2)
11.7 Activities
295(3)
11.7.1 Interview and Write a Bio
295(1)
11.7.2 Branching Out: Brainstorm Ideas Beyond Current Work
295(1)
11.7.3 Wikipedia Entry for an Underrepresented Statistician
296(1)
11.7.4 Getting Started on Social Media
297(1)
11.7.5 Post About What You Are Learning
297(1)
12 Building a Portfolio
298(28)
A Question Generation and the Ideal Data
299(1)
B Analyst Mindset+
300(2)
C Book Report+
302(2)
D Article Critique
304(1)
E Scope of a Journal+
305(1)
F xkcd Common Words
306(2)
G Listening Tour+
308(1)
H Write an Abstract
309(1)
I Dear Data+
310(1)
J Effective Examples and Analogies
311(1)
K Revise and Resubmit+
312(1)
L Science in the News+
313(1)
M Interactive Visualizations+
314(1)
N Five Thirty Eight Investigation+
315(1)
O Turning a Portfolio Piece into a Blog Post+
316(1)
P Blog Narrative Arc
317(1)
Q Blog Critique
318(1)
R Pictoral Key to a Blog Post+
319(1)
S Critique Wiki Entries+
320(1)
T Tidy Tuesday Visualization+
321(1)
U Diary Entry to Blog Post+
322(1)
V Q&A in an Online-Learning Community
323(1)
W References
324(2)
Index 326
Deborah Nolan is Professor of Statistics and Associate Dean for Undergraduate Education in the Division of Computing, Data Science, and Society at the University of California, Berkeley, where she holds the Zaffaroni Family Chair in Undergraduate Education. Her research has involved the empirical process, high-dimensional modeling, and, more recently, technology in education and reproducible research.



Sara Stoudt is an applied statistician whose work primarily focuses on environmental and ecological applications. She is currently a lecturer at Smith College. She received her PhD in Statistics from UC Berkeley. Prior to that she received a B.A. in Mathematics (with an emphasis on statistics) from Smith College.