Data Science (Books)

Bibliography Recommendation Data Science

 Gregory Piatetsky-Shapiro (Analytics, Data Mining, Data Science Expert, KDnuggets President) em More Free Data Mining, Data Science Books and Resources

The list below based on the list compiled by Pedro Martins, but we added the book authors and year, sorted alphabetically by title, fixed spelling, and removed the links that did not work.

  1. An Introduction to Data Science by Jeffrey Stanton, Robert De Graaf, 2013.
    An introductory level resource developed by Syracuse University
  2. An Introduction to Statistical Learning: with Applications in R by G. Casella, S, Fienberg, I Olkin, 2013.
    Overview of statistical learning based on large datasets of information. The exploratory techniques of the data are discussed using the R programming language.
  3. A Programmer’s Guide to Data Mining by Ron Zacharski, 2012.
    A guide through data mining concepts in a programming point of view. It provides several hands-on problems to practice and test the subjects taught on this online book.
  4. Bayesian Reasoning and Machine Learning by David Barber, 2012.
    focusing on applying it to machine learning algorithms and processes. It is a hands-on resource, great to absorb all the knowledge in the book.
  5. Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners by Jared Dean, 2014.
    On this resource the reality of big data is explored, and its benefits, from the marketing point of view. It also explains how to storage these kind of data and algorithms to process it, based on data mining and machine learning.
  6. Data Mining and Analysis: Fundamental Concepts and Algorithms by Mohammed J. Zaki, Wagner Meira, Jr., Data Mining and Analysis: Fundamental Concepts and Algorithms, Cambridge University Press, May 2014.
    A great cover of the data mining exploratory algorithms and machine learning processes. These explanations are complemented by some statistical analysis.
  7. Data Mining and Business Analytics with R by Johannes Ledolter, 2013.
    Another R based book describing all processes and implementations to explore, transform and store information. It also focus on the concept of Business Analytics.
  8. Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management by Michael J.A. Berry, Gordon S. Linoff, 2004.
    A data mining book oriented specifically to marketing and business management. With great case studies in order to understand how to apply these techniques on the real world.
  9. Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery by Graham Williams, 2011.
    The objective of this book is to provide you lots of information on data manipulation. It focus on the Rattle toolkit and the R language to demonstrate the implementation of these techniques.
  10. Gaussian Processes for Machine Learning by Carl Edward Rasmussen and Christopher K. I. Williams, 2006.
    This is a theoretical book approaching learning algorithms based on probabilistic Gaussian processes. It’s about supervised learning problems, describing models and solutions related to machine learning.

Read the full post on KDnuggets: http://www.kdnuggets.com/2015/03/free-data-mining-data-science-books-resources.html

Gregory Piatetsky-Shapiro (Analytics, Data Mining, Data Science Expert, KDnuggets President)

=======================================

Recomendação de Bibliografia Data Science = Kirk Borne‏@KirkDBorneDownload 50+ Free #DataScienceBooks:http://bit.ly/1Or1j5Z #abdsc#BigData #Analytics

Very interesting compilation published here, with a strong machine learning flavor (maybe machine learning book authors — usually academics — are more prone to making their books available for free). Many are O’Reilly books freely available. Here we display those most relevant to data science. I haven’t checked all the sources, but they seem legit. If you find some issue, let us know in the comment section below. Note that at DSC, we also have our free books:

There are several sections in the listing in question:

  1. Data Science Overviews (4 books)
  2. Data Scientists Interviews (2 books)
  3. How To Build Data Science Teams (3 books)
  4. Data Analysis (1 book)
  5. Distributed Computing Tools (2 books)
  6. Data Mining and Machine Learning (29 books)
  7. Statistics and Statistical Learning (5 books)
  8. Data Visualization (2 books)
  9. Big Data (3 books)

Here we mention #1, #5 and #6:

Data Science Overviews

Distributed Computing Tools

Data Mining and Machine Learning

=======================================

Data Science For Good – For All

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Data Science For Good – For All (14/02/2016)

Por Kirk Borne – Principal Data Scientist at Booz Allen Hamilton

https://www.linkedin.com/pulse/data-science-good-all-kirk-borne?trk=v-feed&trk=hp-feed-article-title-share

O que os proprietários de empresas precisam saber sobre segurança e apropriação de dados

O que os proprietários de empresas precisam saber sobre segurança e apropriação de dados

 

Os dados certos tornou-se um dos mais importantes negócios ativos dos nossos tempos, e a pura explosão na quantidade e tipos de dados tem o poder de mudar completamente o forma como fazemos negócios. Mas ainda significativa apropriação e dilemas de segurança que empresários precisam estar cientes antes deles embarcarem em qualquer viagem para coletar e utilizar dados.
Obtendo a propriedade dos dados errados e segurança, isso pode ter consequências desastrosas. A União Europeia irá em breve ser capaz de multar empresas, com 5% do volume de negócios global, se começá-lo errado, mas mesmo apesar das multas, os danos à reputação pode ser imenso. Graças a mídia social, escândalos e violações podem viajar ao redor do mundo em um piscar de olhos, e reputações que levou anos para construir pode ser danificado em apenas alguns segundos.
Aqui estão algumas coisas principais que os proprietários de empresas devem considerar em torno de uma propriedade de dados, para ter segurança e transparência.
Pensar sobre Propriedade
Se você considerar dados como um ativo de negócioschave, ou seja, se o negócio se baseia em certos dados para realizar funções todos os dias, então é muito importante que você possui dados particulares.
Isto é fácil se for seus próprios dados internos, como é seu, mas fica mais complicado com quaisquer dados externos. Por exemplo, se você é dependente dos dados da outra parte para poder executar funções de negócioschave, e caso o fornecedor de seus preços lhe negar o acesso por qualquer motivo, você estará minado. Se você não poder possuir os dados que você está usando, e em seguida, você precisará ter certeza de que pelo menos você não vai perder o acesso aos dados.
Além disso, se você está confiando nos dados que os clientes têm fornecido a vocêentão você precisa estar ciente de que novas leis dão qualquer cidadão da União Europeia «o direito de ser esquecido» que significa que eles podem solicitar que apague todos os registros que você segurar sobre eles. Isso pode representar riscos significativos para as empresas que construíram seu modelo de negócio em torno de dados do cliente.
Proteja seus dados
Dados precisam ser protegidos como qualquer um dos seus outros ativos de negócios, como sua propriedade, seu estoque/mercadoria, sua ferragem, etc. Dependendo do tipo de dados que você está armazenando, pode haver normas de segurança e privacidade a seguir, particularmente quando se trata de dados pessoais.
Sempre que possível, tente usar dados anonimizados para que ele não identificar detalhes dos indivíduos. Onde isso não for possível, você precisa garantir que os dados são mantidos seguros e protegidos. Além de requisitos legais, existem razões de reputação e morais para garantir que os dados dos seus clientes são mantidos seguros.
Violações de dados podem levar a grandes prejuízos para as empresas e houve algumas falhas de perfil muito alto nos últimos anos, tais como o varejista americano alvo e telecomunicações britânicas companhia TalkTalk. Incidentes como este e os muitos outros dados em grande escala roubos que ocorrem com frequência mostram que mesmo as maiores empresas muitas vezes não conseguem manter as promessas que eles fazem sobre proteção de dados. Portanto, é essencial para proteger seus dados contra violações. Medidas sensatas incluem treinamento de seus funcionários para que eles nunca dão proteger informações, criptografia de dados e ter sistemas no lugar que detectar e impedir violações enquanto estão acontecendo. Segurança de dados é uma área altamente técnica e é sempre uma boa ideia procurar ajuda especializada.
Como um aparte, as pessoas costumavam se preocupar com a segurança dos dados armazenados na nuvem, mas, hoje em dia, muitas vezes é mais seguro que as empresas armazenar seus próprios dados internos. Sistemas de segurança da nuvem são geralmente muito mais atualizados e o fato de que os dados são armazenados em mais de um lugar fornece uma rede de segurança extra. Pessoalmente, eu recomendaria o armazenamento em nuvem como uma opção de seguro para as empresas.
Ser transparente
Infelizmente, muitas das práticas de coleta de dados não muito éticos. Facebook, por exemplo, enterra um monte de o que está fazendo com os dados em um acordo de usuário de 50 páginas que ninguém . Acho que é vital, que as empresas expliquem aos seus clientes o que eles estão coletando dos dados e como pretendem usálos. Ninguém gosta de descobrir que foi enganado!
Se você coletar informações pessoais sobre seus clientes ou funcionários, se sinceroExplica por que você está coletando informações (por exemplo, para que você possa entender suas necessidades mais plenamente e prestar um melhor serviço, como resultado).
Não enterre detalhes dados de usuários em longos acordos, ou termos e condições que ninguém vai ler. Mantêlo curto e fácil de entender, e colocar as informações em um lugar óbvio algumas frases quando clientes registrar seus detalhes para compras online seria um bom exemplo.
Finalmente, sempre dar aos clientes a oportunidade de optar por sair. Mesmo se isso significa que eles não podem usar seu serviço, ou partes de seu serviço, é muito melhor para lhes dar a escolha. Em última análise, isso torna os dados mais valiosos para você a longo prazo não é bom usar dados para entender mais sobre seus clientes, se deixam então em massa porque eles sentem que invadiram sua privacidade.
Agregar valor para seus clientes
Quando você está coletando dados sobre pessoas, não é importante ser honesto sobre isso, é uma boa ideia para adicionar valor para eles – algo que faz valer a pena. Que seja benéfico para as pessoas a compartilhar suas informações com o seu negócio, talvez através de produtos ou serviços, melhores ou mais baratos, para que eles sintam que é uma troca justa e vale a pena. Mire um ganha-ganha para todas as partes.
Se você fornecer valor a maioria das pessoas vai ser feliz para você usar seus dados   especialmente se você é capaz de remover marcadores pessoais que ligação-los como um indivíduo à informação. Em geral, se você puder demonstrar que você está usando os dados eticamente, pessoas responderão positivamente.
Empresários não deveriam ser colocados do usando dados as recompensas potenciais e oportunidades de crescimento de negócios são enormes. Mas é vital para agir eticamente e firmemente para bloquear seus dados. Este é um tópico que eu exploro com mais detalhes no meu novo livro Big Data for Small Business For Dummies.

Por Bernard Marr (25/04/2016)

Bernard Marr –Best-Selling Author, Keynote Speaker and Leading Business and Data Expert

What everyone should know about #BigData #ownership and #security | #DataScience   https://www.hiscox.co.uk/business-blog/business-owners-need-know-data-ownership-security/

Translate:  english – portuguese (PT-BR)

Artigo traduzido por Ana Mercedes Gauna (29/04/2016)

BIG DATA – Data Science

data scienceData-Science-Skillset1 (R)

About Data Scientists

Rising alongside the relatively new technology of big data is the new job title data scientist. While not tied exclusively to big data projects, the data scientist role does complement them because of the increased breadth and depth of data being examined, as compared to traditional roles.

So what does a data scientist do?

A data scientist represents an evolution from the business or data analyst role. The formal training is similar, with a solid foundation typically in computer science and applications, modeling, statistics, analytics and math. What sets the data scientist apart is strong business acumen, coupled with the ability to communicate findings to both business and IT leaders in a way that can influence how an organization approaches a business challenge. Good data scientists will not just address business problems, they will pick the right problems that have the most value to the organization.

The data scientist role has been described as “part analyst, part artist.” Anjul Bhambhri, vice president of big data products at IBM, says, “A data scientist is somebody who is inquisitive, who can stare at data and spot trends. It’s almost like a Renaissance individual who really wants to learn and bring change to an organization.”

Whereas a traditional data analyst may look only at data from a single source – a CRM system, for example – a data scientist will most likely explore and examine data from multiple disparate sources. The data scientist will sift through all incoming data with the goal of discovering a previously hidden insight, which in turn can provide a competitive advantage or address a pressing business problem. A data scientist does not simply collect and report on data, but also looks at it from many angles, determines what it means, then recommends ways to apply the data.

Data scientists are inquisitive: exploring, asking questions, doing “what if” analysis, questioning existing assumptions and processes. Armed with data and analytical results, a top-tier data scientist will then communicate informed conclusions and recommendations across an organization’s leadership structure.

Fonte:  IBM.COM = http://www-01.ibm.com/software/data/infosphere/data-scientist/

===========================================================================

Analytics, Data Mining, Data Science Expert, KDnuggets President

Which Big Data, Data Mining, and Data Science Tools go together?
https://www.linkedin.com/pulse/which-big-data-mining-science-tools-go-together-piatetsky-shapiro?trk=hp-feed-article-title-share

More Free Data Mining, Data Science Books and Resources
https://www.linkedin.com/pulse/more-free-data-mining-science-books-resources-piatetsky-shapiro?trk=mp-reader-card

The list below based on the list compiled by Pedro Martins, but we added the book authors and year, sorted alphabetically by title, fixed spelling, and removed the links that did not work.

  1. An Introduction to Data Science by Jeffrey Stanton, Robert De Graaf, 2013.
    An introductory level resource developed by Syracuse University
  2. An Introduction to Statistical Learning: with Applications in R by G. Casella, S, Fienberg, I Olkin, 2013.
    Overview of statistical learning based on large datasets of information. The exploratory techniques of the data are discussed using the R programming language.
  3. A Programmer’s Guide to Data Mining by Ron Zacharski, 2012.
    A guide through data mining concepts in a programming point of view. It provides several hands-on problems to practice and test the subjects taught on this online book.
  4. Bayesian Reasoning and Machine Learning by David Barber, 2012.
    focusing on applying it to machine learning algorithms and processes. It is a hands-on resource, great to absorb all the knowledge in the book.
  5. Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners by Jared Dean, 2014.
    On this resource the reality of big data is explored, and its benefits, from the marketing point of view. It also explains how to storage these kind of data and algorithms to process it, based on data mining and machine learning.
  6. Data Mining and Analysis: Fundamental Concepts and Algorithms by Mohammed J. Zaki, Wagner Meira, Jr., Data Mining and Analysis: Fundamental Concepts and Algorithms, Cambridge University Press, May 2014.
    A great cover of the data mining exploratory algorithms and machine learning processes. These explanations are complemented by some statistical analysis.
  7. Data Mining and Business Analytics with R by Johannes Ledolter, 2013.
    Another R based book describing all processes and implementations to explore, transform and store information. It also focus on the concept of Business Analytics.
  8. Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management by Michael J.A. Berry, Gordon S. Linoff, 2004.
    A data mining book oriented specifically to marketing and business management. With great case studies in order to understand how to apply these techniques on the real world.
  9. Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery by Graham Williams, 2011.
    The objective of this book is to provide you lots of information on data manipulation. It focus on the Rattle toolkit and the R language to demonstrate the implementation of these techniques.
  10. Gaussian Processes for Machine Learning by Carl Edward Rasmussen and Christopher K. I. Williams, 2006.
    This is a theoretical book approaching learning algorithms based on probabilistic Gaussian processes. It’s about supervised learning problems, describing models and solutions related to machine learning.

Read the full post on KDnuggets: http://www.kdnuggets.com/2015/03/free-data-mining-data-science-books-resources.html

===========================================================================

Kirk Borne
Principal Data Scientist at Booz Allen Hamilton

Data Science Declaration for 2015
https://www.linkedin.com/pulse/data-science-declaration-2015-kirk-borne?trk=mp-reader-card

Big Data Complexity Requires Fast Modeling Technology
https://www.linkedin.com/pulse/big-data-complexity-requires-fast-modeling-technology-kirk-borne?trk=mp-reader-card

With Prescriptive Analytics, the future ain’t what it used to be
https://www.linkedin.com/pulse/prescriptive-analytics-future-aint-what-used-kirk-borne?trk=hp-feed-article-title-share

Recomendação de Bibliografia Data Science = Kirk Borne@KirkDBorne Download 50+ Free #DataScience Books:http://bit.ly/1Or1j5Z  #abdsc #BigData #Analytics

Very interesting compilation published here, with a strong machine learning flavor (maybe machine learning book authors – usually academics – are more prone to making their books available for free). Many are O’Reilly books freely available. Here we display those most relevant to data science. I haven’t checked all the sources, but they seem legit. If you find some issue, let us know in the comment section below. Note that at DSC, we also have our free books:

There are several sections in the listing in question:

  1. Data Science Overviews (4 books)
  2. Data Scientists Interviews (2 books)
  3. How To Build Data Science Teams (3 books)
  4. Data Analysis (1 book)
  5. Distributed Computing Tools (2 books)
  6. Data Mining and Machine Learning (29 books)
  7. Statistics and Statistical Learning (5 books)
  8. Data Visualization (2 books)
  9. Big Data (3 books)

Here we mention #1, #5 and #6:

Data Science Overviews

Distributed Computing Tools

Data Mining and Machine Learning

===========================================================================

Bernard Marr é um LinkedIn Influencer

Best-Selling Author, Keynote Speaker and Leading Business and Data Expert

4 Things Big Data Can Do, and 3 Things It Can’t Do #bigdata http://ow.ly/3yjonn
http://data-informed.com/4-things-big-data-can-do-and-3-things-it-cant-do/

4 Ways Big Data Will Change Every Business | SmartData Collectivehttp://www.smartdatacollective.com/bernardmarr/349932/4-ways-big-data-will-change-every-business

Big Data Decision Against Facebook: Implications For Google, Apple and 5,000 Other Companies
https://www.linkedin.com/pulse/big-data-decision-against-facebook-implications-google-bernard-marr?trk=hp-feed-article-title-channel-add

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Here Are the Schools With Degrees in Data Science [List]

http://bostinno.streetwise.co/2015/10/15/best-us-schools-for-data-science-colleges-degrees-in-data-science/?utm_content=bufferce60d&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer

According to a recent report released by RJMetrics called The State of Data Science, there are 11,400 self-identifying Data Scientists on LinkedIn. The organization made it clear that it would only count people who proclaim to be data scientists, rather than going through a painstaking process of determining which skill sets made someone this type of professional and which did not.

Although RJMetrics anticipates that the number it came up with is a wild underestimate, it still shows a steep and recent increase. The report explains that its findings indicate that 52% of data scientists have entered the field within the past four years—meaning that colleges with degrees in data science are becoming an increasing focus. Given that, the question for many becomes: what are the best U.S. schools for data science and the ideal paths toward this sort of career?

“The demand is clearly there, as data science students are finding many job offers when they graduate and in a diverse marketplace.” 

As you can imagine, because this niche but growing field is so new that most of the professionals don’t have educational backgrounds specific to Data Science. For the most part, data scientists have earned higher degrees – master’s and PhDs – but the subjects are all across the board. To be expected, the most popular courses of study are STEM-related. RJMetrics revealed that the top disciplines for data scientists with master’s degrees are Computer Science, Business Administration and Statistics. Meanwhile, for professionals holding PhDs, Physics, Computer Science and Mathematics are the most prevalent concentrations.

Schools are stepping up for Data Science

That being said, universities across the country are picking up on Data Science’s growing importance. Nowadays, businesses – regardless of their industry – are looking to accumulate, analyze and apply data to drive success in their space. To do that, it’s becoming key for employers to get their hands on Data Science talent. Select schools are stepping up and designing programs catered to developing students to thrive in this field.

There are Data Science – or something similarly named like Business Analytics or Data Mining – programs popping up in every U.S. state. In Massachusetts, schools like UMass and Worcester Polytechnic Institute (WPI), are among some of the first Data Science program pioneers.

“The demand is clearly there, as data science students are finding many job offers when they graduate and in a diverse marketplace,” said Elke Rundensteiner, professor at WPI — which has rolled out both a master’s and PhD program in Data Science in the past two years. “We are hearing from employers from marketing to cybersecurity to the pharmaceutical industry who have various data science needs. WPI has responded by digging deeper, offering more specific courses, and finding new intersections between disciplines.”

Because the field is so new and it’s being applied in so many different ways, universities are creating curricula that encompass a variety of disciplines needed to excel in Data Science. Most tracks are a melange of mathematics and IT, as well as business and computer sciences.

UMass' Dedication to STEM Meets Massachusetts Economy’s Demand for High-Tech WorkforceUMass’ Dedication to STEM Meets Massachusetts Economy’s Demand for…

Schools aren’t solely focused on letting students earn degrees in this field. Universities around the country also know they play a crucial role in the Data Science community itself – namely, in its development.

“The demand for new methods and tools for big data is also growing,” explained Andrew McCallum, director and professor at UMass’ Center of Data Science. “Data science centers, like ours at UMass Amherst, bring the data users – industry and government – together with the data science researchers to create new technologies resulting in better decision making and the discovery of new knowledge.”

Where to go

UMass and WPI are hardly alone in jumping on the Data Science educational bandwagon. Here’s a comprehensive list of U.S. universities offering degree programs specifically in this emerging subject (with links to each school’s specific data-science program):

Arizona State University W.P. Carey School of Business – Tempe, AZ

University of California Berkeley – Berkeley, CA

Chapman University – Orange, CA

Stanford – Stanford, CA

University of California San Diego – San Diego, CA

University of the Pacific – San Francisco, CA

University of Southern California – Los Angeles, CA

University of San Francisco – San Francisco, CA

Central Connecticut State University (CCSU) – New Britain, CT

University of Connecticut – Storrs, CT

American Sentinel University – Aurora, CO

University of Denver – Denver, CO

University of Central Florida – Orlando, FL

Catholic University of America – Washington, DC

George Washington University – Washington, DC

Georgetown University – Washington, DC

University of Iowa Tippie College of Business – Iowa City, IA

DePaul University – Chicago, IL

Illinois Institute of Technology – Chicago, IL

Northwestern University – Evanston, IL

University of Illinois Chicago Liautaud – Chicago, IL

University of Illinois at Urbana-Champaign – Urbana-Champaign, IL

University of Chicago Graham School – Chicago, IL

Indiana University Kelley School of Business – Bloomington, IN

Notre Dame – Notre Dame, IN

Purdue – Lafayette, IN

Saint Mary’s College – Notre Dame, IN

Northern Kentucky University – Highland Heights, KY

Lousiana State University – Baton Rouge, LA

Bentley – Waltham, MA

Brandeis – Waltham, MA

Harvard – Cambridge, MA

UMass Amherst Center for Data Science – Amherst, MA

WPI – Worcester, MA

University of Maryland – College Park, MD

Michigan State University – East Lansing, MI

University of Michigan Dearborn – Dearborn, MI

Winona State University – Winona, MN

University of Minnesota – Minneapolis, MN

North Carolina State University – Raleigh, NC

Saint Peter’s University – Jersey City, NJ

Rutgers – New Brunswick, NJ

Stevens Institute of Technology – Hoboken, NJ

Columbia New York, NY

Cornell – Ithaca, NY

Fordham – New York, NY

NYU – New York, NY

NYU Center for Data Science – New York, NY

Pace University – New York, NY and Westchester, NY

RPI – Troy, NY

Syracuse – Syracuse, NY

University of Rochester Institute for Data Science – Rochester, NY

The Ohio State University – Columbus, OH

University of Cincinnati – Cincinnati, OH

Xavier University – Cincinnati, OH

University of Oklahoma – Norman, OK

Carnegie Mellon University – Pittsburgh, PA

Drexel University – Philadelphia, PA

Saint Joseph’s University – Philadelphia, PA

College of Charleston – Charleston, SC

University of Tennessee – Knoxville, TN

Texas A&M University – Houston, TX

University of Texas Austin – Austin, TX

University of North Texas College of Business Information Technologies and Decision Sciences Center – Denton, TX

George Mason University – Fairfax, VA

Virginia Commonwealth University – Richmond, VA

University of Virginia – Charlottesville, VA