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Jumat, 30 November 2012

Download , by Matthew Chapman

Download , by Matthew Chapman

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, by Matthew Chapman

Product details

File Size: 722 KB

Print Length: 290 pages

Publisher: HarperCollins e-books; Reprint edition (October 13, 2009)

Publication Date: October 13, 2009

Sold by: HarperCollins Publishers

Language: English

ASIN: B000PDZFQQ

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Amazon Best Sellers Rank:

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A re-telling of the court case in a Pennsylvania small town where the school-board was taken over by ultra-Christians with the intention of including Creationism disguised as Intelligent Design in the high school Biology classes to counter "Darwinism." The author is a great grandson of Darwin who, obviously, was very interested and attended the trial and the machinations leading up to it in the town.The trial itself was a melange of expert witnesses on both sides. It was revealed just how deceit was used by those professing belief in ID as actual science. But all science relies on factual experiments, observations and conclusions and ID was found lacking in these science requirements that are needed to justify teaching it in ant science classroom. And so the judge ruled.The flavor of this book comes from the portraits of the townspeople involved in the contest and those who wished it would all go way because the trial had gone national because of the issues involved. Touches of humor spice the narrative from time to time. The book represents theongoing disputes about religion and science and the choice of what is to be taught in public school to our children: the Bible or science.

Film writer and director Matthew Chapman has a few reasons to be interested in the trial of Kitzmiller v. Dover School Board. First, he is a direct descendant of Charles Darwin. Second, he has written before on issues of faith vs. science (Trials of the Monkey). Third, he is the type of writer inherently drawn to exploring characters.This third reason is what seperates this book from the several others profiling the "battle of intelligent design versus science." As other reviewers point out, books like Edward Humes's "Monkey Girl" and Gordy Sack's "Battle Over the Meaning of Everything" may be more journalistic, this book explores the characters. Chapman seems to have a face-to-face with every major (and minor) player on all sides.Beyond this, Chapman himself notes, "One of my chief defects - or better qualities, I'm not sure which - is that I find it almost impossible to maintain animosity toward people with whom I violently disagree once I get to know them." (loc. 490, Kindle edition) No matter how judgmental and acerbic Chapman can be - and he is both, strongly pro-evolution - Chapman paints all characters in as fair a light as possible. (One of the best parts comes later in the book, where Chapman reveals the very human side of Bill Buckingham, the perjurer and hot-headed leader of the Dover School Board that started the whole mess. You almost - ALMOST - empathize with the guy!)If Chapman's book explores the more human Dover, it doesn't make for anywhere near the gripping blow-by-blow account of Edward Humes' or Gordy Sacks' books. Whereas those are very matter of fact and linear, this book jumps from spot to spot, intertwining scenes from the trial with scenes from the school-board meetings to philosophic rumination. The other two books are more snappy and journalistic in tone, whereas this one (for good or bad) is warmer in tone, which doesn't make for a page turner.In the end, Chapman's book on a well-trodden subject DOES manage to set itself apart form the others by focusing on the characters more than the others. With Humes,' and Sacks' book (I have yet to read Lebo's), I feel like I understood the trial quite well. With this book, I feel like I understand Dover quite well.

I became fascinated with the Kitzmiller vs. Dover trial nearly three years after Judge Jones issued his ruling. From a number of Internet sites, I read a great deal about the trial including many pages of testimony and 35 to 40 pages of the 139-page ruling in favor of the plaintiffs.But much of that information, while fascinating, lacked the narrative quality that a screenwriter like Chapman could bring to the story. I decided to buy this book along with Gordy Slack's "The Battle Over the Meaning of Everything" to fill in some gaps in my knowledge and to try and understand the roles played by the Thomas More Law Center, the Discovery Institute, and the members of the Dover Board of Education. I was very happy that I did.Chapman is a decendent of Charles Darwin; a great-great grandson, in fact. He is also a screenwriter and his method of telling a story borrows the flashback idea from the movies. Occasionally, he'll pull some piece of information about a past event and bring it into current time in a manner that can be entertaining but, at times, annoying.His writing skill is first-rate and he has the flair of a novelist. For example, he writes "If the plaintiffs' legal team was a well-oiled collegial machine, the defense was a dysfunctional family with a frequently absent father". You don't get the sense that you are reading an account from a daily newspaper with a quip like that.However, I sometimes felt that if I had not made myself aware of a good deal of the background information concerning this case prior to reading his book, that I might feel, if not lost, that at least I was missing something. For that reason and for the disorienting flashback technique, I gave the book a 4-star rating - his storytelling ability would be worth 5-stars in my opinion.I enjoyed this book a little bit more than Gordy Slack's, though they cannot be directly compared as they approach the subject in quite different ways. Slack sounds, at times, like a guy writing for the Huffington Post, rather than a former editor of a natural history magazine. I say that with affection. His chapter on "Assembling Goliath", the pooling of elite resources by the plaintiffs' team, made me feel like I was watching Eisenhower discuss the invasion plans with his staff. Chapman, however, went to the current event and filled in whatever background info he chose to as the narrative of the trial moved along.Read in tandem, the two books give you a good sense of the trial, the outcome, the implications for years to come. Each is a very well-told tale.

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Senin, 26 November 2012

Download Ebook Positive Affirmations Journal: 100 Journal Writing Prompts to Explore Your Thoughts, Focus on the Positive, and Visualize the Life You Really Want, by Susan LaBorde

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Positive Affirmations Journal: 100 Journal Writing Prompts to Explore Your Thoughts, Focus on the Positive, and Visualize the Life You Really Want, by Susan LaBorde

Positive Affirmations Journal: 100 Journal Writing Prompts to Explore Your Thoughts, Focus on the Positive, and Visualize the Life You Really Want, by Susan LaBorde


Positive Affirmations Journal: 100 Journal Writing Prompts to Explore Your Thoughts, Focus on the Positive, and Visualize the Life You Really Want, by Susan LaBorde


Download Ebook Positive Affirmations Journal: 100 Journal Writing Prompts to Explore Your Thoughts, Focus on the Positive, and Visualize the Life You Really Want, by Susan LaBorde

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Positive Affirmations Journal: 100 Journal Writing Prompts to Explore Your Thoughts, Focus on the Positive, and Visualize the Life You Really Want, by Susan LaBorde

About the Author

As an avid student of personal development, Susan LaBorde has been using positive affirmations for years. They have played a significant role in her life and serve as part of the inspiration behind her desire to help others. Susan is the creator of the Make A Vision Board website, which began as personal journey and grew into a passion for sharing what she has learned along the way. You won't find her claiming to be any sort of self-help guru. What you will find in all her writing is the wisdom gleaned from those who do claim to be masters, as well as the knowledge acquired through traveling her own life path. That path has had its share of unexpected turns, some of which taught her the value of being able to find your center even when things go haywire. Her goal is to inform and educate others about practical ways to create a better life. Her main focus is on positive thinking and visualization techniques. She believes in keeping it real and, at the same time, always keeping an open mind.

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Product details

Paperback: 110 pages

Publisher: CreateSpace Independent Publishing Platform; 1 edition (May 24, 2016)

Language: English

ISBN-10: 1532822871

ISBN-13: 978-1532822872

Product Dimensions:

6 x 0.3 x 9 inches

Shipping Weight: 6.4 ounces (View shipping rates and policies)

Average Customer Review:

4.6 out of 5 stars

6 customer reviews

Amazon Best Sellers Rank:

#320,528 in Books (See Top 100 in Books)

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I love this journal helps me feel better when I journal TY

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Love the guidance it provides... self-reflection and deeper thoughts along with positive vibes!

Love this journal and just ordered another one to give as a gift!!

Received this as a gift. I try to write in my journal first thing in the morning. It gets you in a positive mindset for the rest of the day.

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Positive Affirmations Journal: 100 Journal Writing Prompts to Explore Your Thoughts, Focus on the Positive, and Visualize the Life You Really Want, by Susan LaBorde PDF

Positive Affirmations Journal: 100 Journal Writing Prompts to Explore Your Thoughts, Focus on the Positive, and Visualize the Life You Really Want, by Susan LaBorde PDF
Positive Affirmations Journal: 100 Journal Writing Prompts to Explore Your Thoughts, Focus on the Positive, and Visualize the Life You Really Want, by Susan LaBorde PDF

Kamis, 15 November 2012

Get Free Ebook Marketing Data Science: Modeling Techniques in Predictive Analytics with R and Python (FT Press Analytics)

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Marketing Data Science: Modeling Techniques in Predictive Analytics with R and Python (FT Press Analytics)


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Marketing Data Science: Modeling Techniques in Predictive Analytics with R and Python (FT Press Analytics)

From the Back Cover

To solve real marketing problems with predictive analytics, you need to master concepts, theory, skills, and tools.Now, one authoritative guide covers them all. Marketing Data Science brings together the knowledge you need to model consumer and buyer preferences and predict marketplace behavior, so you can make informed business decisions. Using hands-on examples built with R, Python, and publicly available data sets, Thomas W. Miller shows how to solve a wide array of marketing problems with predictive analytics. Building on the pioneering data science program at Northwestern University, Miller covers analytics for segmentation, target marketing, brand and product positioning, new product development, choice modeling, recommender systems, pricing research, retail site selection, demand estimation, sales forecasting, customer retention, and lifetime value analysis. Miller brings together essential concepts, principles, and skills that were formerly scattered across multiple texts. You’ll gain realistic experience extending predictive analytics with powerful techniques from web analytics, network science, programming, and marketing research. As you practice, you’ll master data management and modeling skills you can apply in all markets, business-to-consumer and business-to-business alike. All data sets, extensive R and Python code, and additional examples are available for download at www.ftpress.com/miller/. In a world transformed by information and communication technology, marketing, sales, and research have merged--and data rule them all. Today, marketers must master a new data science and use it to uncover meaningful answers rapidly and inexpensively. This book teaches marketing data science through real-world examples that integrate essential knowledge from the disciplines that have shaped it. Building on his pioneering courses at Northwestern University, Thomas W. Miller walks you through the entire process of modeling and answering marketing questions with R and Python, today’s leading open source tools for data science. Using real data sets, Miller covers a full spectrum of marketing applications, from targeting new customers to improving retention, setting prices to quantifying brand equity. Marketing professionals can use Marketing Data Science as a ready resource and reference for any project. For programmers, it offers an extensive foundation of working code for solving real problems--with step-by-step comments and expert guidance for taking your analysis even further. ADDRESS IMPORTANT MARKETING PROBLEMS: Reveal hidden drivers of consumer choice Target likely purchasers Strengthen retention Position products to exploit marketplace gaps Evaluate promotions Build recommender systems Assess response to brand and price Model the diffusion of innovation Analyze consumer sentiment Build competitive intelligence Choose new retail locations Develop an efficient and rigorous marketing research program, drawing on a wide range of data sources, internal and external

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About the Author

Thomas W. Miller is faculty director of the Predictive Analytics program at Northwestern University. He has designed courses for the program, including Marketing Analytics, Advanced Modeling Techniques, Data Visualization, Web and Network Data Science, and the capstone course. He has taught extensively in the program and works with more than forty other faculty members in delivering training in predictive analytics and data science. Miller is owner of Research Publishers LLC and its ToutBay Division, a publisher and distributor of data science applications. He has consulted widely in the areas of retail site selection, product positioning, segmentation, and pricing in competitive markets and has worked with predictive models for more than 30 years. Miller’s books include Web and Network Data Science, Modeling Techniques in Predictive Analytics, Data and Text Mining: A Business Applications Approach, Research and Information Services: An Integrated Approach for Business, and a book about predictive modeling in sports, Without a Tout: How to Pick a Winning Team. Before entering academia, Miller spent nearly 15 years in business IT in the computer and transportation industries. He also directed the A. C. Nielsen Center for Marketing Research and taught market research and business strategy at the University of Wisconsin-Madison. He holds a Ph.D. in psychology (psychometrics) and a master’s degree in statistics from the University of Minnesota and an MBA and master’s degree in economics from the University of Oregon.

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Product details

Series: FT Press Analytics

Hardcover: 480 pages

Publisher: Pearson FT Press; 1 edition (May 22, 2015)

Language: English

ISBN-10: 0133886557

ISBN-13: 978-0133886559

Product Dimensions:

7.4 x 1.3 x 9.4 inches

Shipping Weight: 2 pounds (View shipping rates and policies)

Average Customer Review:

3.9 out of 5 stars

24 customer reviews

Amazon Best Sellers Rank:

#407,247 in Books (See Top 100 in Books)

The organization of the book is confusing. You can't use the table of contents to decide what to read. After reading the first 4 chapters, I'm extremely disappointed.

Very good book, well written, and the best pas, as with all of Miller's books that I have purchased, is that it comes with real code examples in both Python and R. Great way to get up and running.

good book, but....no data sets to work with. Seems critical for a source code heavy book (ie almost every chapter has pages of code). We would prefer not to scan, then try to run the code ourselves. Read the appendix first at that seems to be where the theory is then go back to the chapters for practical work. Borrowed this book from the library....its really expensive otherwiseupdate: OK --kept reading, and paying overdue fines at the library, so I bought the book. Really worth the read if you're serious about focusing on marketing data science. Great starting read for technical marketers who want to do something in this field.Glad the code samples are now available. Will have to test and see. One thing I noticed about the content. Every time something got interesting Prof. Miller would quote a reference for further reading (ie. details). That's sort of OK, but leaves me wanting and having to go dig elsewhere. Suggestion: one more paragraph for such situations would put my curiosity at rest. A lot of content around product development, positioning, recommending, but a little light on broader examples - It might be helpful to describe a broader range of techniques (ie. list them), then drill down on one or two. It just seems too narrow, like drinking from a straw when really a funnel is needed with the huge alternatives. Enjoyed the book (looks like a text book but reads like a novel - that's a good thing)

If you want to have just one book on Marketing Data Science, this is the one.

Updated my rating from 2 to 5 stars as the code has become available on FTPress. I received an email from the publisher last week. Not sure why it took over 6 months for them to post this.

Good book, very well explained examples (the R and the Python codes are very well written) but if you have read other books from Prof. Miller, you would be able to remember some exacts paragraphs across some books.

Now that the data is available I will go through this book and do a proper review. But in general I do not like this book as much as this one R for Marketing Research and Analytics (Use R!)

This is a difficult book to review, and I struggled with it a bit. On one hand, it is well written with good use of hypothetical and relevant examples (.e.g Amazon, AT&T). On the other hand, it reads like a programming class - lectures and all, which can be dense and difficult to glean information from - not exactly the rapid fire approach many data scientists I work with/am use (caveat: I'm in life sciences).Pros:-Wealth of information - book is dense-Covers topics based on marketing not programming approaches (e.g. Recommending Products with approaches rather than Building Network Diagrams with marketing examples of how to use this technique)-Uses my two favorite languages - R & Python - very common and can be applied to modeling, charting and analytics more readily than other languages - they work well together - Python for building interfaces and specific R packages for doing the deep statistical/data crunching & visualization/presentation (at least that is how I use them)-Plenty of example code that can be readily used - sample data described in text available for download-I like that there is a list of Tables in the front of the book - makes it easy to rapidly find the right examplesCons:-This book is difficult to go through - you need to be comfortable with both R and Python-Book also assumes familiarity with common statistical/analytical approachesBottom line: as this book does not cover fundamentals of any of the core subjects (marketing, Python, R, Predictive Analytics) my gut is that to approach this topic you would be better served learning first predictive analytics and marketing concepts prior to this book being of full utility. That said it is incredibly informative and I found it a fascinating.

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