Data Science and its relationship to Big Data and data-driven
decision making
F. Provost1
Leonard N. Stern School of Business
New York University
44 W. 4th St. New York, NY, USA
fprovost@stern.nyu.edu
T. Fawcett
Data Scientists, LLC
tfawcett@acm.org
Data Science and its Relationship to Big Data and Data-Driven Decision MakingDr. Volkan OBAN
Data Science and its Relationship to Big Data and Data-Driven Decision Making
To cite this article:
Foster Provost and Tom Fawcett. Big Data. February 2013, 1(1): 51-59. doi:10.1089/big.2013.1508.
Foster Provost and Tom Fawcett
Published in Volume: 1 Issue 1: February 13, 2013
ref:http://online.liebertpub.com/doi/full/10.1089/big.2013.1508
https://www.researchgate.net/publication/256439081_Data_Science_and_Its_Relationship_to_Big_Data_and_Data-Driven_Decision_Making
This document discusses using metadata and knowledge graphs to better organize health data and make it more findable. It explains how knowledge graphs work by connecting entities and their relationships, and how this can help match user search intent to the meaning of data. The document also discusses challenges in organizing diverse data sources and standards, and how semantic annotation and knowledge graphs can help integrate different data types and make them interoperable.
Data mining involves extracting useful patterns from large amounts of data. It involves defining a problem, preparing data, exploring data, building models, and deploying models. Some common applications of data mining include analyzing customer purchasing patterns, detecting fraud, predicting disease outbreaks, and analyzing financial/business data. While data warehousing provides insights into past trends, data mining can discover hidden patterns to predict future trends and behaviors from data.
The document discusses the field of data mining. It begins by defining data mining and describing its branches including classification, clustering, and association rule mining. It then discusses the growth of data in various domains that has created opportunities for data mining applications. The document outlines the history and development of data mining from empirical science to computational science to data science. It provides examples of data mining applications in various domains like healthcare, energy, climate science, and agriculture. Finally, it discusses future directions and challenges for the field of data mining.
Paradigm4 Research Report: Leaving Data on the tableParadigm4
While Big Data enjoys widespread media coverage, not enough attention has been paid to what practitioners think — data scientists who manage and analyze massive volumes of data. We wanted to know, so Paradigm4 teamed up with Innovation Enterprise to ask over 100 data scientists for their help separating Big Data hype from reality. What we learned is that data scientists face multiple challenges achieving their company’s analytical aspirations. The upshot is that businesses are leaving data — and money — on the table.
A gigantic archive of terabytes of information is created every day from current data frameworks and computerized advances, for example, Internet of Things and distributed computing. Examination of these gigantic information requires a ton of endeavors at various levels to extricate information for dynamic. Hence, huge information examination is an ebb and flow region of innovative work. The essential goal of this paper is to investigate the likely effect of huge information challenges, and different instruments related with it. Accordingly, this article gives a stage to investigate enormous information at various stages. Moreover, it opens another skyline for analysts to build up the arrangement, in light of the difficulties and open exploration issues.
Data Science and its Relationship to Big Data and Data-Driven Decision MakingDr. Volkan OBAN
Data Science and its Relationship to Big Data and Data-Driven Decision Making
To cite this article:
Foster Provost and Tom Fawcett. Big Data. February 2013, 1(1): 51-59. doi:10.1089/big.2013.1508.
Foster Provost and Tom Fawcett
Published in Volume: 1 Issue 1: February 13, 2013
ref:http://online.liebertpub.com/doi/full/10.1089/big.2013.1508
https://www.researchgate.net/publication/256439081_Data_Science_and_Its_Relationship_to_Big_Data_and_Data-Driven_Decision_Making
This document discusses using metadata and knowledge graphs to better organize health data and make it more findable. It explains how knowledge graphs work by connecting entities and their relationships, and how this can help match user search intent to the meaning of data. The document also discusses challenges in organizing diverse data sources and standards, and how semantic annotation and knowledge graphs can help integrate different data types and make them interoperable.
Data mining involves extracting useful patterns from large amounts of data. It involves defining a problem, preparing data, exploring data, building models, and deploying models. Some common applications of data mining include analyzing customer purchasing patterns, detecting fraud, predicting disease outbreaks, and analyzing financial/business data. While data warehousing provides insights into past trends, data mining can discover hidden patterns to predict future trends and behaviors from data.
The document discusses the field of data mining. It begins by defining data mining and describing its branches including classification, clustering, and association rule mining. It then discusses the growth of data in various domains that has created opportunities for data mining applications. The document outlines the history and development of data mining from empirical science to computational science to data science. It provides examples of data mining applications in various domains like healthcare, energy, climate science, and agriculture. Finally, it discusses future directions and challenges for the field of data mining.
Paradigm4 Research Report: Leaving Data on the tableParadigm4
While Big Data enjoys widespread media coverage, not enough attention has been paid to what practitioners think — data scientists who manage and analyze massive volumes of data. We wanted to know, so Paradigm4 teamed up with Innovation Enterprise to ask over 100 data scientists for their help separating Big Data hype from reality. What we learned is that data scientists face multiple challenges achieving their company’s analytical aspirations. The upshot is that businesses are leaving data — and money — on the table.
A gigantic archive of terabytes of information is created every day from current data frameworks and computerized advances, for example, Internet of Things and distributed computing. Examination of these gigantic information requires a ton of endeavors at various levels to extricate information for dynamic. Hence, huge information examination is an ebb and flow region of innovative work. The essential goal of this paper is to investigate the likely effect of huge information challenges, and different instruments related with it. Accordingly, this article gives a stage to investigate enormous information at various stages. Moreover, it opens another skyline for analysts to build up the arrangement, in light of the difficulties and open exploration issues.
Big Data Mining - Classification, Techniques and IssuesKaran Deep Singh
The document discusses big data mining and provides an overview of related concepts and techniques. It describes how big data is characterized by large volume, variety, and velocity of data that is difficult to manage with traditional methods. Common techniques for big data mining discussed include NoSQL databases, MapReduce, and Hadoop. Some challenges of big data mining are also mentioned, such as dealing with high volumes of unstructured data and limitations of traditional databases in handling diverse and continuously growing data sources.
This document discusses the need for a new paradigm in big data analytics using algorithms. It begins by describing the limitations of traditional analytics approaches like statistical analysis, data mining, visualization and business intelligence tools when applied to big data. These approaches are query-based and labor intensive. Emerging big data tools like Hadoop and in-memory databases help with storage and queries but do not provide automated insights. The document argues that the new paradigm should focus on algorithms that can automatically surface insights from data in seconds, replacing the need for data analysts to manually query databases. This represents a shift from humans digging for insights to algorithms surfacing insights for humans to evaluate.
The document discusses the importance of metadata for publishers in the digital era. It defines metadata as "data about data" and explains that metadata has become critical for allowing computers and systems to communicate about content. Metadata impacts publisher processes by enabling content to reach the right audiences through various relationships and channels. The document provides examples of how metadata was essential for a drug reference product and a medical content provider to organize their content and drive various outputs. It emphasizes that metadata is just as, if not more, important than the raw content itself for digital publishing.
Slides (currently unannotated) to support the "Preparing for the Future: Technological Challenges and Beyond" workshop presented with Brian Kelly - http://ukwebfocus.com/events/ili-2015-preparing-for-the-future/
Note - slideshare seems to have messed up the conversion - some slides are (unintentionally) blank....
This document summarizes an analysis of unstructured data and text analytics. It discusses how text analytics can extract meaning from unstructured sources like emails, surveys, forums to enhance applications like search, information extraction, and predictive analytics. Examples show how tools can extract entities, relationships, sentiments to gain insights from sources in domains like healthcare, law enforcement, and customer experience.
This document discusses data warehousing and data mining. It defines data warehousing as the process of centralizing data from different sources for analysis. Data mining is described as the process of analyzing data to uncover hidden patterns and relationships. The document provides examples of how data mining and data warehousing can be used together, with data warehousing collecting and organizing data that is then analyzed using data mining techniques to generate useful insights. Applications of data mining and data warehousing discussed include medicine, finance, marketing, and scientific discovery.
Big data is prevalent in our daily life. Not surprisingly, big data becomes a hot topic discussedby commercial worlds, media, magazines, general publics and elsewhere. From academic point of view, isit a research area of potential worth being explored? Or it is just another hype? Are there only computer orIS related scholars suitable for big data research due to its nature? Or scholars from other research areas are alsosuitable for this subject? This study aims to answer these questions through the use of informetricsapproach and data source form the SSCI Journal database, leveraging informetric‟s robust natures ofquantitative power of analyze information in any form onto the data source of representativeness. This research shows that big data research is at its growth phase with an exponential growth patternsince 2012 and with great potential for years to come. And perhaps surprisingly, computer or IS relateddisciplinesare not on the top 5 research areas fromthis research results. In fact, the top five research disciplinesare more diversified then expected: business economics (#1), Government Law (#2), InformationScience/ Library Science (#3), Social Science (#4) and Computer Science (#5). Scholars from the USuniversities are the most productive in this subject while Asian countries, including Taiwan, are alsovisible. Besides, this study also identifies that big data publications from SSCI journal database during2005-2015 do fit Lotka‟s law. This study contributes tounderstand the current big data research trends and also show the ways toresearchers who are interested to conduct future research in big data regardless of their research backgrounds.
This Is just a little overview on it not fully explaned.
Data Mining:-
Data mining is the process of analyzing data from different perspectives and summarizing it into useful information.
Data Mining is the Process that is used by big companies or organizations to handle,balance and analyzing big data.
Data mining is primarily used today by companies with a strong consumer focus - retail, financial, communication, and marketing organizations. It enables these companies to determine relationships among internal factors such as price, product positioning, or staff skills, and external factor.
It is used by the companies to increase their revenue or cut their costs or both.
While large-scale information technology has been evolving separate transaction and analytical systems, data mining provides the link between the two.
Data mining software analyzes relationships and patterns in stored transaction data based on open-ended user queries.
SOFTWARES FOR DATA MINING:
Microsoft SQL SERVER 2005.
Mircrsoft SQL SERVER 2008.
Oracle Data Mining etc.
One Super Market in Canada used data mining capacity of Oracle Software to analyze local buying patterns.They discovered that when Mens bought Food for home on Saturday and Sunday They like to tended to buy beer.On Other days of the week mens don’t usually buy beers.
The Shopkeeper said to his workers to put sufficient amount of beers on Saturday and Sunday.In this way income of the shop was increased.
BI refers to applications & technologies which are use to gather information about their company opertaions.
Data Mining is importand part of business intellegence.
Some Basic Examples of Use of Data Mining Are Given Below:
In Finance Data Mining is used for Credit Cards Analysis.
Astronomy:
Palomar Obstervatory discovered 22 quasars with the help of Data Mining.
3) Telecommunication:
In Telecommunication Data Mining is used for Call Records.
4) Offices:
In Offices it is used for to balance data and records of the staff. etc
Following are some of the types if Data Mining:
Assoication Rule is used for store layout. Etc.
Classification is used for weather prediction. Etc.
Clustering is used for Graphical Represention of Universe.
Sequential Pattern is used for medical diagnosis.
THANK YOU...!!!
over the past ten years, data has grown on the Internet, and we are the fuel and haste of this increase. Business owners, they produce apps for us, and we feed these companies with our data, unfortunately, it is all our private data. In the end, we become, through our private data, a commodity that is sold to the highest bidder.
Without security, not even privacy. Ethical oversight and constraints are needed to ensure that an appropriate balance. This article will cover: the contents of big data, what it includes, how data is collected, and the process of involving it on the Internet. In addition, it discuss the analysis of data, methods of collecting it, and factors of ethical challenges. Furthermore, the user's rights, which must be observed, and the privacy the user has.
This document provides an introduction to open data, including definitions of open data and its benefits. It discusses how open data can be used, such as to increase government transparency and allow for third party analysis. It also notes challenges with open data, such as data not always being truly open if it is difficult to access, use or redistribute. Government policies and public expectations are driving increased publication of open data.
Data has become an indispensable part of every economy, industry, organization, business
function and individual. Big Data is a term used to identify the datasets that whose size is
beyond the ability of typical database software tools to store, manage and analyze. The Big
Data introduce unique computational and statistical challenges, including scalability and
storage bottleneck, noise accumulation, spurious correlation and measurement errors. These
challenges are distinguished and require new computational and statistical paradigm. This
paper presents the literature review about the Big data Mining and the issues and challenges
with emphasis on the distinguished features of Big Data. It also discusses some methods to deal
with big data.
Data sciences is the topnotch in our world now as it enables us to predict the future and behaviors of people and systems alike.
Hence, this course focuses on introducing the processing involved in data sciences.
This document summarizes a literature review paper on big data analytics. It begins by defining big data as large datasets that are difficult to handle with traditional tools due to their size, variety, and velocity. It then discusses how big data analytics applies advanced analytics techniques to big data to extract valuable insights. The paper reviews literature on big data analytics tools and methods for storage, management, and analysis of big data. It also discusses opportunities that big data analytics provides for decision making in various domains.
The pioneers in the big data space have battle scars and have learnt many of the lessons in this report the hard way. But if you are a general manger & just embarking on the big data journey, you should now have what they call the 'second mover advantage’. My hope is that this report helps you better leverage your second mover advantage. The goal here is to shed some light on the people & process issues in building a central big data analytics function
Evaluation Mechanism for Similarity-Based Ranked Search Over Scientific DataAM Publications
The motto of this paper is to provide an essential and efficient method to retrieve the data profiles being stored in a particular storage database like the one scientific database. Our country has succeeded in our mars mission in our first attempt. So as far as the information about such an important mission is concerned the information should be retrieved safely as fast as possible. Keeping this in mind we have tried to implement and provide the fastest information retrieval technique. This can lead to better and better retrieval speed in the future missions in lesser time. Here, we have used Information Retrieval-style ranked search. We contemplate the IR-style ranked attend can be exercised to word firms to hold an expert capture the more disclosure between the numerable word firms in large amount templates, much love content-based ranked bring up the rear helps users the way one sees it feel of the large place of business of web content. To show this supposition, we innovated the management of rated accompany for business like information for a current multi-TB experimental certificate like our test. In this attempt, we assess in case the work of genius of differing resemblance, and hence rated attend, try differential data.
Ordinary people included anyone who is not a Geek like myself. This book is written for ordinary people. That includes manager, marketers, technical writers, couch potatoes and so on.
Data Science and Analytics for Ordinary People is a collection of blogs I have written on LinkedIn over the past year. As I continue to perform big data analytics, I continue to discover, not only my weaknesses in communicating the information, but new insights into using the information obtained from analytics and communicating it. These are the kinds of things I blog about and are contained herein.
This document provides an overview of data mining, including definitions, processes, tasks, and algorithms. It defines data mining as a process that takes data as input and outputs knowledge. The main steps in the data mining process are data preparation, data mining (applying algorithms to identify patterns), and evaluation/interpretation. Common data mining tasks are classification, regression, association rule mining, clustering, and text/link mining. Popular algorithms described are decision trees, rule-based classifiers, artificial neural networks, and nearest neighbor methods. Each have advantages and disadvantages related to predictive power, speed, and interpretability.
This document provides an introduction to data mining and data warehousing. It discusses how the volume of data being collected is growing exponentially in many fields due to advances in data collection technologies. It also describes how data mining can be used to extract useful knowledge and patterns from large datasets to help solve important problems. The document outlines some key techniques in data mining including classification, clustering, and association rule mining. It discusses how data mining draws from fields like machine learning, statistics, and databases to analyze large and complex datasets.
Reporting involves building, organizing, and summarizing raw data into reports that raise questions about what is happening in the business. Analysis transforms this information into insights by interpreting the data at a deeper level to answer questions and provide actionable recommendations about why things are happening and what can be done. Both reporting and analysis play important roles in driving actions that create greater value for organizations, with reporting providing information to identify issues and analysis providing explanations and solutions to help bridge the gap between data and actions.
This document discusses data mining and provides an overview of the topic. It begins by defining data mining as the process of analyzing large amounts of data to discover hidden patterns and rules. The goal is to analyze this data and summarize it into useful information that can be used to make decisions.
It then describes some common data mining techniques like decision trees, neural networks, and clustering. It also discusses the typical stages of a data mining project, including business understanding, data preparation, modeling, evaluation, and deployment.
Finally, it provides examples of applications for data mining, such as in healthcare to identify patterns in patient data, education to improve learning outcomes, and manufacturing to enhance product quality. In summary, the document outlines the
Jeffrey Gutierrez is seeking an entry-level position utilizing his skills in hospitality. He has over 10 years of experience working in restaurants and cafes in roles such as barista, cashier, receptionist, and juice/coffee maker. His objective is to get a challenging position where he can continuously improve his career and fully utilize his medical, professional, and practical experience.
Big Data Mining - Classification, Techniques and IssuesKaran Deep Singh
The document discusses big data mining and provides an overview of related concepts and techniques. It describes how big data is characterized by large volume, variety, and velocity of data that is difficult to manage with traditional methods. Common techniques for big data mining discussed include NoSQL databases, MapReduce, and Hadoop. Some challenges of big data mining are also mentioned, such as dealing with high volumes of unstructured data and limitations of traditional databases in handling diverse and continuously growing data sources.
This document discusses the need for a new paradigm in big data analytics using algorithms. It begins by describing the limitations of traditional analytics approaches like statistical analysis, data mining, visualization and business intelligence tools when applied to big data. These approaches are query-based and labor intensive. Emerging big data tools like Hadoop and in-memory databases help with storage and queries but do not provide automated insights. The document argues that the new paradigm should focus on algorithms that can automatically surface insights from data in seconds, replacing the need for data analysts to manually query databases. This represents a shift from humans digging for insights to algorithms surfacing insights for humans to evaluate.
The document discusses the importance of metadata for publishers in the digital era. It defines metadata as "data about data" and explains that metadata has become critical for allowing computers and systems to communicate about content. Metadata impacts publisher processes by enabling content to reach the right audiences through various relationships and channels. The document provides examples of how metadata was essential for a drug reference product and a medical content provider to organize their content and drive various outputs. It emphasizes that metadata is just as, if not more, important than the raw content itself for digital publishing.
Slides (currently unannotated) to support the "Preparing for the Future: Technological Challenges and Beyond" workshop presented with Brian Kelly - http://ukwebfocus.com/events/ili-2015-preparing-for-the-future/
Note - slideshare seems to have messed up the conversion - some slides are (unintentionally) blank....
This document summarizes an analysis of unstructured data and text analytics. It discusses how text analytics can extract meaning from unstructured sources like emails, surveys, forums to enhance applications like search, information extraction, and predictive analytics. Examples show how tools can extract entities, relationships, sentiments to gain insights from sources in domains like healthcare, law enforcement, and customer experience.
This document discusses data warehousing and data mining. It defines data warehousing as the process of centralizing data from different sources for analysis. Data mining is described as the process of analyzing data to uncover hidden patterns and relationships. The document provides examples of how data mining and data warehousing can be used together, with data warehousing collecting and organizing data that is then analyzed using data mining techniques to generate useful insights. Applications of data mining and data warehousing discussed include medicine, finance, marketing, and scientific discovery.
Big data is prevalent in our daily life. Not surprisingly, big data becomes a hot topic discussedby commercial worlds, media, magazines, general publics and elsewhere. From academic point of view, isit a research area of potential worth being explored? Or it is just another hype? Are there only computer orIS related scholars suitable for big data research due to its nature? Or scholars from other research areas are alsosuitable for this subject? This study aims to answer these questions through the use of informetricsapproach and data source form the SSCI Journal database, leveraging informetric‟s robust natures ofquantitative power of analyze information in any form onto the data source of representativeness. This research shows that big data research is at its growth phase with an exponential growth patternsince 2012 and with great potential for years to come. And perhaps surprisingly, computer or IS relateddisciplinesare not on the top 5 research areas fromthis research results. In fact, the top five research disciplinesare more diversified then expected: business economics (#1), Government Law (#2), InformationScience/ Library Science (#3), Social Science (#4) and Computer Science (#5). Scholars from the USuniversities are the most productive in this subject while Asian countries, including Taiwan, are alsovisible. Besides, this study also identifies that big data publications from SSCI journal database during2005-2015 do fit Lotka‟s law. This study contributes tounderstand the current big data research trends and also show the ways toresearchers who are interested to conduct future research in big data regardless of their research backgrounds.
This Is just a little overview on it not fully explaned.
Data Mining:-
Data mining is the process of analyzing data from different perspectives and summarizing it into useful information.
Data Mining is the Process that is used by big companies or organizations to handle,balance and analyzing big data.
Data mining is primarily used today by companies with a strong consumer focus - retail, financial, communication, and marketing organizations. It enables these companies to determine relationships among internal factors such as price, product positioning, or staff skills, and external factor.
It is used by the companies to increase their revenue or cut their costs or both.
While large-scale information technology has been evolving separate transaction and analytical systems, data mining provides the link between the two.
Data mining software analyzes relationships and patterns in stored transaction data based on open-ended user queries.
SOFTWARES FOR DATA MINING:
Microsoft SQL SERVER 2005.
Mircrsoft SQL SERVER 2008.
Oracle Data Mining etc.
One Super Market in Canada used data mining capacity of Oracle Software to analyze local buying patterns.They discovered that when Mens bought Food for home on Saturday and Sunday They like to tended to buy beer.On Other days of the week mens don’t usually buy beers.
The Shopkeeper said to his workers to put sufficient amount of beers on Saturday and Sunday.In this way income of the shop was increased.
BI refers to applications & technologies which are use to gather information about their company opertaions.
Data Mining is importand part of business intellegence.
Some Basic Examples of Use of Data Mining Are Given Below:
In Finance Data Mining is used for Credit Cards Analysis.
Astronomy:
Palomar Obstervatory discovered 22 quasars with the help of Data Mining.
3) Telecommunication:
In Telecommunication Data Mining is used for Call Records.
4) Offices:
In Offices it is used for to balance data and records of the staff. etc
Following are some of the types if Data Mining:
Assoication Rule is used for store layout. Etc.
Classification is used for weather prediction. Etc.
Clustering is used for Graphical Represention of Universe.
Sequential Pattern is used for medical diagnosis.
THANK YOU...!!!
over the past ten years, data has grown on the Internet, and we are the fuel and haste of this increase. Business owners, they produce apps for us, and we feed these companies with our data, unfortunately, it is all our private data. In the end, we become, through our private data, a commodity that is sold to the highest bidder.
Without security, not even privacy. Ethical oversight and constraints are needed to ensure that an appropriate balance. This article will cover: the contents of big data, what it includes, how data is collected, and the process of involving it on the Internet. In addition, it discuss the analysis of data, methods of collecting it, and factors of ethical challenges. Furthermore, the user's rights, which must be observed, and the privacy the user has.
This document provides an introduction to open data, including definitions of open data and its benefits. It discusses how open data can be used, such as to increase government transparency and allow for third party analysis. It also notes challenges with open data, such as data not always being truly open if it is difficult to access, use or redistribute. Government policies and public expectations are driving increased publication of open data.
Data has become an indispensable part of every economy, industry, organization, business
function and individual. Big Data is a term used to identify the datasets that whose size is
beyond the ability of typical database software tools to store, manage and analyze. The Big
Data introduce unique computational and statistical challenges, including scalability and
storage bottleneck, noise accumulation, spurious correlation and measurement errors. These
challenges are distinguished and require new computational and statistical paradigm. This
paper presents the literature review about the Big data Mining and the issues and challenges
with emphasis on the distinguished features of Big Data. It also discusses some methods to deal
with big data.
Data sciences is the topnotch in our world now as it enables us to predict the future and behaviors of people and systems alike.
Hence, this course focuses on introducing the processing involved in data sciences.
This document summarizes a literature review paper on big data analytics. It begins by defining big data as large datasets that are difficult to handle with traditional tools due to their size, variety, and velocity. It then discusses how big data analytics applies advanced analytics techniques to big data to extract valuable insights. The paper reviews literature on big data analytics tools and methods for storage, management, and analysis of big data. It also discusses opportunities that big data analytics provides for decision making in various domains.
The pioneers in the big data space have battle scars and have learnt many of the lessons in this report the hard way. But if you are a general manger & just embarking on the big data journey, you should now have what they call the 'second mover advantage’. My hope is that this report helps you better leverage your second mover advantage. The goal here is to shed some light on the people & process issues in building a central big data analytics function
Evaluation Mechanism for Similarity-Based Ranked Search Over Scientific DataAM Publications
The motto of this paper is to provide an essential and efficient method to retrieve the data profiles being stored in a particular storage database like the one scientific database. Our country has succeeded in our mars mission in our first attempt. So as far as the information about such an important mission is concerned the information should be retrieved safely as fast as possible. Keeping this in mind we have tried to implement and provide the fastest information retrieval technique. This can lead to better and better retrieval speed in the future missions in lesser time. Here, we have used Information Retrieval-style ranked search. We contemplate the IR-style ranked attend can be exercised to word firms to hold an expert capture the more disclosure between the numerable word firms in large amount templates, much love content-based ranked bring up the rear helps users the way one sees it feel of the large place of business of web content. To show this supposition, we innovated the management of rated accompany for business like information for a current multi-TB experimental certificate like our test. In this attempt, we assess in case the work of genius of differing resemblance, and hence rated attend, try differential data.
Ordinary people included anyone who is not a Geek like myself. This book is written for ordinary people. That includes manager, marketers, technical writers, couch potatoes and so on.
Data Science and Analytics for Ordinary People is a collection of blogs I have written on LinkedIn over the past year. As I continue to perform big data analytics, I continue to discover, not only my weaknesses in communicating the information, but new insights into using the information obtained from analytics and communicating it. These are the kinds of things I blog about and are contained herein.
This document provides an overview of data mining, including definitions, processes, tasks, and algorithms. It defines data mining as a process that takes data as input and outputs knowledge. The main steps in the data mining process are data preparation, data mining (applying algorithms to identify patterns), and evaluation/interpretation. Common data mining tasks are classification, regression, association rule mining, clustering, and text/link mining. Popular algorithms described are decision trees, rule-based classifiers, artificial neural networks, and nearest neighbor methods. Each have advantages and disadvantages related to predictive power, speed, and interpretability.
This document provides an introduction to data mining and data warehousing. It discusses how the volume of data being collected is growing exponentially in many fields due to advances in data collection technologies. It also describes how data mining can be used to extract useful knowledge and patterns from large datasets to help solve important problems. The document outlines some key techniques in data mining including classification, clustering, and association rule mining. It discusses how data mining draws from fields like machine learning, statistics, and databases to analyze large and complex datasets.
Reporting involves building, organizing, and summarizing raw data into reports that raise questions about what is happening in the business. Analysis transforms this information into insights by interpreting the data at a deeper level to answer questions and provide actionable recommendations about why things are happening and what can be done. Both reporting and analysis play important roles in driving actions that create greater value for organizations, with reporting providing information to identify issues and analysis providing explanations and solutions to help bridge the gap between data and actions.
This document discusses data mining and provides an overview of the topic. It begins by defining data mining as the process of analyzing large amounts of data to discover hidden patterns and rules. The goal is to analyze this data and summarize it into useful information that can be used to make decisions.
It then describes some common data mining techniques like decision trees, neural networks, and clustering. It also discusses the typical stages of a data mining project, including business understanding, data preparation, modeling, evaluation, and deployment.
Finally, it provides examples of applications for data mining, such as in healthcare to identify patterns in patient data, education to improve learning outcomes, and manufacturing to enhance product quality. In summary, the document outlines the
Jeffrey Gutierrez is seeking an entry-level position utilizing his skills in hospitality. He has over 10 years of experience working in restaurants and cafes in roles such as barista, cashier, receptionist, and juice/coffee maker. His objective is to get a challenging position where he can continuously improve his career and fully utilize his medical, professional, and practical experience.
With these components in place, we present the Data
Science Machine — an automated system for generating
predictive models from raw data. It starts with a relational
database and automatically generates features to be used
for predictive modeling.
This document is a resume for Samuel Abrams summarizing his technical skills, applications built, professional experience, and education. He has strong skills in JavaScript, jQuery, HTML, CSS, AngularJS, and more. Some applications he has built include a tic tac toe game using OOP and jQuery, a truck route app using AngularJS and PHP with mySQL, and games using HTML, CSS, and JavaScript. His professional experience includes tutoring SAT students and project consulting. He has a bachelor's degree in English and completed a web development bootcamp.
This document provides an overview of Spanish grammar topics including:
- Stem-changing verbs like pensar, almorzar, pedir, and jugar.
- Uses of para to indicate purpose or recipients.
- Indirect object pronouns and their placement.
- Object pronoun placement with infinitives and conjugated verbs.
- How to express liking with gustar.
- Affirmative and negative words in Spanish.
- Forming superlatives with adjectives and adverbs.
- Uses of reflexive verbs and placement of reflexive pronouns.
- Forming affirmative and negative commands as well as irregular forms.
- Words for sequencing events in Spanish like primer
Erwin Chester G. Vengco has over 15 years of experience in education, travel, human resources, and customer service. He holds a Bachelor's degree in Business Administration from De La Salle University and is a certified TESOL teacher. His career objective is to work for a company where he can serve customers competently and help the company achieve its goals by utilizing his strong teamwork, people, and training skills.
Keith Pettiford is seeking an IT position and has an Associate's degree in Computer Technology Integration from Vance Granville Community College. He has experience troubleshooting technical issues and working with software, hardware, networks and operating systems. Pettiford learns new technologies quickly, works well independently or in teams, and is proficient with various computer systems and tools. He has certifications in security fundamentals, networking fundamentals, and Microsoft Office programs.
The V30 GNSS RTK system is a dual-frequency, multi-constellation GNSS receiver with 220 tracking channels. It is designed for high-precision surveying applications including static, PPK, and RTK surveys. Key features include multi-day battery life, rugged design, integrated radio options, and compatibility with various field controllers and software.
GenerationYYZ is a social media marketing company with over 2,000 Twitter followers, 1,100 Instagram followers, and 525 Facebook followers. They average 63.5k monthly Twitter impressions and have a 50.57% email open rate and 7.73% click-through rate, with over 2,200 average monthly page loads, 2,000 average monthly unique visits, and 3,000 average monthly pageviews. They offer sponsored posts, reviews, and social media promotions on Twitter, Instagram, Facebook and Pinterest as well as brand ambassador opportunities and giveaways.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise boosts blood flow and levels of neurotransmitters and endorphins which elevate and stabilize mood.
This document is a grammar book that provides instruction on various Spanish grammar topics. It covers the subjunctive with adjective clauses, tu commands, nosotros commands, past participles used as adjectives, the present perfect, past perfect, future tense, conditional tense, and past subjunctives. For each topic, it provides examples and explanations of how to form and use the different verb tenses and structures.
Cryotherapy and halotherapy treatments are offered at The Spa. Cryotherapy exposes the body to extremely cold temperatures for 1-3 minutes using liquid nitrogen, triggering the body's defense mechanisms. Halotherapy takes place in a darkened room resembling a salt cave, where clients inhale salt and negative ions to relax and rejuvenate. Both treatments are said to improve conditions like joint pain, skin issues, and respiratory and stress-related problems according to clinical studies. Prices for cryotherapy sessions range from $50-550 depending on the number of sessions purchased. Halotherapy sessions are $75 each.
The document discusses the complex legal infrastructure for public health in the United States across federal, state, and local levels of government. While states have the primary legal responsibility for public health, the federal government has grown in influence through powers like the Commerce Clause, funding provided by taxing authority, and agenda-setting on national issues. Local governments are dependent on and limited by state authority based on Dillon's Rule. Overall, while legal authority is dispersed, informal powers from funding, politics, and national prioritization have increased the federal government's dominance in shaping public health policy despite public health primarily being implemented locally.
Ethan Noel has a Master's degree in Wildlife Ecology and Conservation from the University of Florida. His thesis investigated human-bear conflicts in Florida and ways to encourage bear-resistant garbage management. He currently works as a Fisheries & Wildlife Biological Scientist for the Florida Fish and Wildlife Conservation Commission. Noel has extensive experience in wildlife research, habitat restoration, and prescribed burning.
Af finding academic resources for your fyp oct 16CityUniLibrary
The document provides an overview of resources for students to effectively conduct literature reviews, including databases, referencing styles, and plagiarism policies. It recommends several academic databases and financial resources for research. It emphasizes searching effectively, citing sources properly to avoid plagiarism, and seeking help from the subject librarian as needed.
Pahuway Arnel Serano is a Child Data Analyst at World Vision Development Foundation, Inc. in Quezon City with over 10 years of experience in education, content analysis, and project management. He holds a Master's degree in Public Administration from Bicol University and Bachelor's degrees in Secondary Education and Electronics and Communication Engineering. He has extensive training in leadership, community development, and disaster response.
2016 vendor showcase: better our gnss data collection by mel philbrookGIS in the Rockies
The document discusses accuracy and precision of GPS/GNSS field data collection. It defines accuracy as how close data is to real-world values, while precision refers to how exact the data description is. Common sources of inaccuracy are satellite positioning, antenna placement, and body obstruction. Understanding the realization of the NAD83 datum used is important, as is knowing that NAD83 and WGS84 now differ by up to 2 meters in some areas due to updated definitions. A spreadsheet is recommended for checking accuracy of field data.
This document provides an overview of the key concepts in data science including statistics, machine learning, data mining, and data analysis tools. It also discusses classification, regression, clustering, and data reduction techniques. Additionally, it defines what a data scientist is and how they work with data to understand patterns, ask questions, and solve problems as part of a team. The document demonstrates some examples of admissions data and analyses simpson's paradox to illustrate data science concepts.
Two hour lecture I gave at the Jyväskylä Summer School. The purpose of the talk is to give a quick non-technical overview of concepts and methodologies in data science. Topics include a wide overview of both pattern mining and machine learning.
See also Part 2 of the lecture: Industrial Data Science. You can find it in my profile (click the face)
Best data science training Institute: Kellytechnologies is the best data science training Institutes in Hyderabad.Providing greate data science training by realtime faculty in hyderabad.
Applications of Big Data Analytics in BusinessesT.S. Lim
The document discusses big data and big data analytics. It begins with definitions of big data from various sources that emphasize the large volumes of structured and unstructured data. It then discusses key aspects of big data including the three Vs of volume, variety, and velocity. The document also provides examples of big data applications in various industries. It explains common analytical methods used in big data including linear regression, decision trees, and neural networks. Finally, it discusses popular tools and frameworks for big data analytics.
Big data is like a two-edged sword: It can bring many new opportunities for business, but it can also harm individuals and businesses in unanticipated ways
Big Data: Are you ready for it? Can you handle it? ScaleFocus
Big data presents both opportunities and challenges for companies. It provides a competitive advantage but organizing, analyzing, and drawing accurate conclusions from vast amounts of unsorted data can be difficult. Companies must critically examine their data to avoid making miscalculations from biases, gaps, or false senses of reliability. Technical solutions like Hadoop can help by supporting flexible handling of multiple data sources at low cost for tasks like data staging, processing, and archiving. However, big data requires experienced teams to ask the right questions and leverage these tools to accomplish business goals, rather than viewing them as guarantees of success. Companies must assess their readiness by considering resources, change management, success criteria, and partner selection.
The document provides an overview of data science. It defines data science as a field that encompasses data analysis, predictive analytics, data mining, business intelligence, machine learning, and deep learning. It explains that data science uses both traditional structured data stored in databases as well as big data from various sources. The document also describes how data scientists preprocess and analyze data to gain insights into past behaviors using business intelligence and then make predictions about future behaviors.
This document provides an overview of Big Data and its potential to transform businesses. It discusses Big Data's definition, history, and impact on management thinking. Big Data represents an evolution in how vast amounts of complex data can now be captured, stored, processed, and analyzed to generate insights. While Big Data was first described in 2008, its origins can be traced back earlier through related concepts in data mining and artificial intelligence. The document aims to explain Big Data in a clear and practical way so businesses can understand how to leverage it rather than viewing it as too complex or disruptive.
Paulraj Ponniah - Data Warehousing Fundamentals for IT Professionals-Wiley (2...AshrafDabbas2
Data warehousing has become mainstream and continues to grow significantly. More than half of US companies have committed to data warehousing, and 90% of multinational companies have or plan to implement one. Data warehousing is used across industries from retail to healthcare to analyze large amounts of transaction data and make strategic decisions. Data warehouses now store terabytes of data and larger ones are increasingly common as more detailed data is captured and analyzed.
The document discusses several myths about data mining. It summarizes that data mining is not instant predictions from a crystal ball, but rather a multi-step process requiring clean data. It also notes that data mining is a viable technology for businesses that can provide insights regardless of company size or amount of customer data. Advanced algorithms are not the only important aspect of data mining, as business knowledge is also essential.
Big Data & Analytics Trends 2016 Vin MalhotraVin Malhotra
This document discusses several trends in analytics for 2016:
1. Data security is a major concern as data volumes grow exponentially and security risks increase. Analytics can help secure data but requires integration across innovation, analytics, connectivity and technology.
2. The Internet of Things generates massive sensor data that requires new analytics to extract value, though challenges remain in integrating sensor and structured data in real time.
3. Open source analytics solutions like Hadoop are increasingly used by enterprises but also require careful risk management and a clear strategy to ensure they align with technology needs.
ETHICAL ISSUES WITH CUSTOMER DATA COLLECTIONPranav Godse
Data mining involves collecting and analyzing large amounts of customer data. While this can provide commercial benefits, it also raises ethical issues regarding customer privacy. Some key ethical challenges include ambiguity around how social networks label relationships, uncertainty around future uses of customer data by companies, and a lack of transparency around passive collection of mobile location data. To address these challenges, companies should focus on ethical data mining practices like verifying data sources, respecting customer expectations of privacy, developing trust through transparency and control over data access. Regulators also need to continue updating laws and regulations to balance the benefits of data analytics with protecting individual privacy rights.
This talk is an introduction to Data Science. It explains Data Science from two perspectives - as a profession and as a descipline. While covering the benefits of Data Science for business, It explaints how to get started for embracing data science in business.
Paulraj Ponniah - Data Warehousing Fundamentals for IT Professionals-Wiley (2...AshrafDabbas2
This document discusses trends in data warehousing. It begins by reviewing the continued growth of data warehousing and how it has become mainstream. Several major trends are then discussed individually, including real-time data warehousing, the inclusion of multiple data types beyond just structured numeric data, and the maturation of the vendor solutions and products market. The trends discussed are aimed to provide important context and knowledge about the current state of data warehousing.
The Insight Data Science Fellows Program is a 6-week postdoctoral training fellowship that teaches scientists industry skills in data science. The program is held in Silicon Valley and New York City and bridges the gap between academia and careers in data science. Fellows learn tools and techniques from mentors at companies and work on projects to gain skills and interview at mentor companies for jobs in data science.
Big Data (This paper has some minor issues with the refere.docxhartrobert670
Big Data
(This paper has some minor issues with the references at the end but is otherwise good)
Introduction
Information is one of the most important resources that companies have available to them; this
information allows decisions to be made to determine what the company is going to do for the next day,
the next month, and the next year. The core component of this important resource is data, and with a
little data, companies can have a little bit information to plan future operations. That same company
with large amounts of data, or big data as it is known, can much more accurately find trends, become
more efficient, increase productivity, and in turn be more profitable. What separates data from big data,
what defining characteristics does it have, how can such a massive resource be fully utilized, and why
should businesses, especially smaller businesses, even bother with such an undertaking.
To understand what big data is first one must start at what came before this big data revolution
that some big companies are just now at the cusp of. Before the advent of big data when companies
gathered data, first it was fairly cost prohibitive due to issue with storage of larger amounts of data and
since computers processing power was not equal to what most businesses are working with today what
those companies were trying to accomplish could end up taking larger or not being possible by the
equipment or techniques being used. Since the first reason has become less burdensome for companies
it has become easier to collect larger amounts of data and store larger amounts of data, which has
allowed some companies to use old data for things outside the original intended purpose. When a
business collects data it normally is towards a goal or trying to gain an understanding but after the
meaning from the data gathered had been extracted not much else would be done with the data and
typically thrown away. With it no longer being as cost prohibitive companies like Google were able to
reuse old data for other purposes and glean additional insight beyond what the initial set of data had
revealed. This is the idea behind big data and what companies hope to gain is more information beyond
the explicit information within very large sets of data.
Key information
How is data any different than big data; at what point does the size of this raw information
change how it’s labeled. Actually this is misleading because it is not just the size of the data, but three
defining characteristics that help to identify what big data is. According to the web site Gartner.com
(Laney, 2001), the focus area of data management were related to volume, variety, and velocity. Volume
specifies the actual size of the data being stored, and as such since overtime data storage has become
more efficient the for where big data starts is something that has changed with better technology.
Even with all of the advances in storage architecture and data ...
Preso on relevance of big data analytics to scholarly publishers, given at annual AAP/PSP conference. Focuses on the "product side" of big data and how advances in new models for evaluating medical evidence will affect medical publishers and offers recommendations on how to prepare for new developments in data-driven evidence-based medicine.
The document discusses big data challenges faced by organizations. It identifies several key challenges: heterogeneity and incompleteness of data, issues of scale as data volumes increase, timeliness in processing large datasets, privacy concerns, and the need for human collaboration in analyzing data. The document describes surveying various organizations in Pakistan, including educational institutions, telecommunications companies, hospitals, and electrical utilities, to understand the big data problems they face. Common challenges included data errors, missing or incomplete data, lack of data management tools, and issues integrating different data sources. The survey found that while some organizations used big data tools, many educational institutions in particular did not, limiting their ability to effectively manage and analyze their large and growing datasets.
The use of new forms of data is not an evolution. Instead, powering big data supply chains, and innovating through new forms of analytics, is a step change.
New forms of data do not fit traditional architectures. Traditional supply chains were architected to use structured data with software using relational databases. The big data era will make many of the investments from the last decade obsolete.
Big data offers the opportunity to redefine supply chain processes from the outside-in (from the channel back) and define the customer-centric supply chain. This is in stark contrast to the inflexible IT investments installed over the last decade to respond inside-out based on order shipments. These traditional investments in Enterprise Resource Planning (ERP), Advanced Planning Systems (APS) and traditional Business Intelligence (BI) for reporting, improved the supply chain response, but did not allow the organization to sense, shape or orchestrate outside-in. New forms of data (e.g., images, social data, sensor transmission, input from global positioning systems (GPS), the Internet of Things, and unstructured text from email, blogs and ratings and reviews) offer new opportunities. They also require new techniques and technologies.
Big data offers new opportunities for the corporation to listen, test and learn, and respond faster. In this study, companies see the greatest opportunity to use big data for “demand” (to better know the customer and improve the response); however, actual investments are in “supply” not “demand.” Respondents view supply-centric projects like product traceability (involving product serialization and traceability), supply chain visibility and temperature controlled handling as important.
Is big data a problem or a new market opportunity? Like the respondents of this survey, we believe that big data represents an opportunity for all. In the study, one-fourth of respondents currently have a big data initiative. However, interest is growing. Sixty-five percent have or plan to have a big data initiative in the future. Despite the hype, and the intensity of marketing rhetoric in the market, in our year-over-year studies on big data we see very little change in activity.
Despite the fact that the IT group is more likely to see big data as a problem, 49% of those with a big data initiative report that it is headed by an IT leader.
Big data represents a new opportunity, but seizing it requires a new form of leadership. It can ignite new business models and drive channel opportunities. However, it cannot be big data for big data itself. Instead, the initiatives need to be aligned to business objectives with a focus on small and iterative projects. It requires innovation. To move forward, companies need to embrace new technologies and redesign processes. It is not the case of stuffing new forms of data into old processes.
Unveiling the Power of Data Science.pdfKajal Digital
Data science is an interdisciplinary field that combines techniques from statistics, computer science, and domain expertise to extract insights and knowledge from data. It involves the collection, cleaning, analysis, and interpretation of data to make informed decisions and predictions. The goal is to uncover hidden patterns, trends, and correlations that might otherwise remain obscured.
Unlocking the Value of Big Data (Innovation Summit 2014)Dun & Bradstreet
Big Data is central to the strategic thinking of today’s innovators and business executives as companies are scrambling to figure out the secret to transforming Big Data to Big Insight and that Insight into Action. As many companies struggle with the emerging technologies and nascent capabilities to discover and curate massive quantities of highly dynamic data, new problems are emerging in the form of how to ask meaningful questions that leverage the “V’s” of large amounts of data (e.g. volume, variety, velocity, veracity). In the Business-to-Business space, these challenges are creating both significant opportunity and ominous new types of risk. This presentation discusses how companies are reacting to these changes and provide valuable insight into new ways of thinking in a world with overwhelming quantities of data.
Similar to Data Science and its relationship to Big Data and data-driven decision making (20)
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1) The document discusses using image processing and object detection techniques for insurance claims processing and underwriting. It aims to allow insurers to realistically assess images of damaged objects and claims.
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Covid19py by Konstantinos Kamaropoulos
A tiny Python package for easy access to up-to-date Coronavirus (COVID-19, SARS-CoV-2) cases data.
ref:https://github.com/Kamaropoulos/COVID19Py
https://pypi.org/project/COVID19Py/?fbclid=IwAR0zFKe_1Y6Nm0ak1n0W1ucFZcVT4VBWEP4LOFHJP-DgoL32kx3JCCxkGLQ
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The document discusses using the lpSolveAPI package in R to solve linear programming problems. It provides three examples:
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problems and minimum cut tree problem.
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k-means Clustering in Python
scikit-learn--Machine Learning in Python
from sklearn.cluster import KMeans
k-means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining. k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells.
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ref: http://scikit-learn.org/stable/auto_examples/cluster/plot_cluster_iris.html
This document describes using time series analysis in R to model and forecast tractor sales data. The sales data is transformed using logarithms and differencing to make it stationary. An ARIMA(0,1,1)(0,1,1)[12] model is fitted to the data and produces forecasts for 36 months ahead. The forecasts are plotted along with the original sales data and 95% prediction intervals.
k-means Clustering and Custergram with R.
K Means Clustering is an unsupervised learning algorithm that tries to cluster data based on their similarity. Unsupervised learning means that there is no outcome to be predicted, and the algorithm just tries to find patterns in the data. In k means clustering, we have the specify the number of clusters we want the data to be grouped into. The algorithm randomly assigns each observation to a cluster, and finds the centroid of each cluster.
ref:https://www.r-bloggers.com/k-means-clustering-in-r/
ref:https://rpubs.com/FelipeRego/K-Means-Clustering
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The Pandas library provides easy-to-use data structures and analysis tools for Python. It uses NumPy and allows import of data into Series (one-dimensional arrays) and DataFrames (two-dimensional labeled data structures). Data can be accessed, filtered, and manipulated using indexing, booleans, and arithmetic operations. Pandas supports reading and writing data to common formats like CSV, Excel, SQL, and can help with data cleaning, manipulation, and analysis tasks.
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R Machine Learning packages( generally used)
prepared by Volkan OBAN
reference:
https://github.com/josephmisiti/awesome-machine-learning#r-general-purpose
Data visualization with R.
Mosaic plot .
---Ref: https://www.stat.auckland.ac.nz/~ihaka/120/Lectures/lecture17.pdf
http://www.statmethods.net/advgraphs/mosaic.html
https://stat.ethz.ch/R-manual/R-devel/library/graphics/html/mosaicplot.html
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06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
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Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
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Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
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#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
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A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
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The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
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Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
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Data Science and its relationship to Big Data and data-driven decision making
1. Data Science and its relationship to Big Data and data-driven
decision making
F. Provost1
Leonard N. Stern School of Business
New York University
44 W. 4th St. New York, NY, USA
fprovost@stern.nyu.edu
T. Fawcett
Data Scientists, LLC
tfawcett@acm.org
Received: date / Accepted: date
1Corresponding author.
2. Abstract
Companies have realized they need to hire data scientists, academic institutions are scrambling to put
together data science programs, and publications are touting data science as a hot—even “sexy”—career
choice. However, there is confusion about what exactly data science is, and this confusion could lead to
disillusionment as the concept diffuses into meaningless buzz. In this paper we argue that there are good
reasons why it has been hard to pin down exactly what data science is. One reason is that data science
is intricately intertwined with other important concepts also of growing importance, such as big data
and data-driven decision making. Another reason is the natural tendency to associate what a practitioner
does with the definition of the practitioner’s field; this can result in overlooking the fundamentals of
the field. We believe that trying to define the boundaries of Data Science precisely right now is not of
the utmost importance. We can debate the boundaries of the field in an academic setting, but in order
for data science to serve business effectively, it is important (i) to understand its relationships to other
important related concepts, and (ii) to begin to identify the fundamental principles underlying data science.
Once we embrace (ii) we can much better understand and explain exactly what data science has to offer.
Furthermore, only once we embrace (ii) should we be comfortable calling it data science. In this paper
we present a perspective that addresses all these things. We close by offering as examples a partial list of
fundamental principles underlying data science.
3. Data Science and Big Data 1
1 Introduction
With vast amounts of data now available, companies in almost every industry are focused on exploiting
data for competitive advantage. The volume and variety of data have far outstripped the capacity of
manual analysis, and in some cases have exceeded the capacity of conventional databases. At the same
time, computers have become far more powerful, networking is ubiquitous, and algorithms have been
developed that can connect datasets to enable broader and deeper analyses than previously possible. The
convergence of these phenomena has given rise to the increasingly widespread business application of data
science.
Companies across industries have realized that they need to hire more data scientists. Academic in-
stitutions are scrambling to put together programs to train data scientists. Publications are touting data
science as a hot career choice, and even “sexy”1
. However, there is confusion about what exactly data
science is, and this confusion could well lead to disillusionment as the concept diffuses into meaningless
buzz. In this paper we argue that there are good reasons why it has been hard to pin down what exactly data
science is. One reason is that data science is intricately intertwined with other important concepts, like big
data and data-driven decision making, which are also growing in importance and attention. Another reason
is the natural tendency, in the absence of academic programs to teach one otherwise, to associate what a
practitioner actually does with the definition of the practitioner’s field; this can result in overlooking the
fundamentals of the field.
It is our position that trying to define the boundaries of Data Science precisely right now is not of the
foremost importance. Data science academic programs are being developed and in an academic setting we
can debate its boundaries. However, in order for data science to serve business effectively, it is important
(i) to understand its relationships to these other important and closely related concepts, and (ii) to begin
to understand what are the fundamental principles underlying data science. Once we embrace (ii) we can
4. Data Science and Big Data 2
much better understand and explain exactly what data science has to offer. Furthermore, only once we
embrace (ii) should we be comfortable calling it data science.
In this paper we present a perspective that addresses all these things. We first work to disentangle
this set of closely interrelated concepts. In the process we highlight data science as the connective tissue
between data processing technologies (including “big data”) and data-driven decision making. We discuss
the complicating issue of data science as a field versus data science as a profession. Finally, we offer as
examples a list of some fundamental principles underlying data science.
2 Data science
At a high level, data science is a set of fundamental principles that support and guide the principled
extraction of information and knowledge from data. Possibly the most closely related concept to data
science is data mining—the actual extraction of knowledge from data, via technologies that incorporate
these principles. There are hundreds of different data mining algorithms, and a great deal of detail to the
methods of the field. We argue that underlying all these details is a much smaller and more concise set of
fundamental principles.
These principles and techniques are applied broadly across functional areas in business. Probably the
broadest business applications are in marketing for tasks such as targeted marketing, online advertising,
and recommendations for cross-selling. Data science also is applied for general customer relationship
management to analyze customer behavior in order to manage attrition and maximize expected customer
value. The finance industry uses data science for credit scoring and trading, and in operations via fraud
detection and workforce management. Major retailers from Wal-Mart to Amazon apply data science
throughout their businesses, from marketing to supply-chain management. Many firms have differentiated
themselves strategically with data science, sometimes to the point of evolving into data mining companies.
5. Data Science and Big Data 3
But data science involves much more than just data mining algorithms. Successful data scientists
must be able to view business problems from a data perspective. There is a fundamental structure to
data-analytic thinking, and basic principles that should be understood. Data science draws from many
“traditional” fields of study. Fundamental principles of causal analysis must be understood. A large
portion of what has traditionally been studied within the field of Statistics is fundamental to data science.
There are also particular areas where intuition, creativity, common sense, and knowledge of a particular
application must be brought to bear. A data science perspective provides practitioners with structure and
principles, which give the data scientist a framework to systematically treat problems of extracting useful
knowledge from data.
3 Data science in action
For concreteness, let’s look at two brief case studies of analyzing data to extract predictive patterns. These
studies illustrate different sorts of applications of data science. The first was reported in the New York
Times:
Hurricane Frances was on its way, barreling across the Caribbean, threatening a direct hit
on Florida’s Atlantic coast. Residents made for higher ground, but far away, in Bentonville,
Ark., executives at Wal-Mart Stores decided that the situation offered a great opportunity for
one of their newest data-driven weapons ... predictive technology.
A week ahead of the storm’s landfall, Linda M. Dillman, Wal-Mart’s chief information
officer, pressed her staff to come up with forecasts based on what had happened when Hurri-
cane Charley struck several weeks earlier. Backed by the trillions of bytes’ worth of shopper
history that is stored in Wal-Mart’s data warehouse, she felt that the company could ‘start
predicting what’s going to happen, instead of waiting for it to happen,’ as she put it.2
6. Data Science and Big Data 4
Consider why data-driven prediction might be useful in this scenario. It might be useful to predict that
people in the path of the hurricane would buy more bottled water. Maybe, but it seems a bit obvious, and
why do we need data science to discover this? It might be useful to project the amount of increase in sales
due to the hurricane, to ensure that local Wal-Marts are properly stocked. Perhaps mining the data could
reveal that a particular DVD sold out in the hurricane’s path—but it sold out that week at Wal-Marts across
the country, not just where the hurricane landing was imminent. The prediction could be somewhat useful,
but probably too general.
It would be more valuable to discover patterns due to the hurricane that were not obvious. To do this,
analysts might examine the huge volume of Wal-Mart data from prior, similar situations (such as Hurricane
Charley earlier in the same season) to identify unusual local demand for products. From such patterns, the
company might be able to anticipate unusual demand for products and rush stock to the stores ahead of
the hurricane’s landfall.
Indeed, that is what happened. The New York Times reported that: “... the experts mined the data
and found that the stores would indeed need certain products—and not just the usual flashlights. ‘We
didn’t know in the past that strawberry Pop-Tarts increase in sales, like seven times their normal sales rate,
ahead of a hurricane,’ Ms. Dillman said in a recent interview.’ And the pre-hurricane top-selling item was
beer.1
”’2
Consider a second, more typical business scenario and how it might be treated from a data perspective.
Assume you just landed a great analytical job with MegaTelCo, one of the largest telecommunication firms
in the United States. They are having a major problem with customer retention in their wireless business.
In the mid-Atlantic region, 20% of cell-phone customers leave when their contracts expire, and it is getting
increasingly difficult to acquire new customers. Since the cell-phone market is now saturated, the huge
1
Of course! What goes better with strawberry Pop Tarts than a nice cold beer?
7. Data Science and Big Data 5
growth in the wireless market has tapered off. Communications companies are now engaged in battles
to attract each other’s customers while retaining their own. Customers switching from one company to
another is called churn, and it is expensive all around: one company must spend on incentives to attract a
customer while another company loses revenue when the customer departs.
You have been called in to help understand the problem and to devise a solution. Attracting new
customers is much more expensive than retaining existing ones, so a good deal of marketing budget is
allocated to prevent churn. Marketing has already designed a special retention offer. Your task is to devise
a precise, step-by-step plan for how the tech team should use MegaTelCo’s vast data resources to decide
which customers should be offered the special retention deal prior to the expiration of their contracts.
Specifically, how should the analytics team decide on the set of customers to target to best reduce churn for
a particular incentive budget? Answering this question is much more complicated than it seems initially.
4 Data science and data-driven decision making
Data science involves principles, processes and techniques for understanding phenomena via the (auto-
mated) analysis of data. For the perspective of this paper, the ultimate goal of data science is improving
decision making, as this generally is of paramount interest to business. Figure 1 places data science in the
context of other closely related data-related processes in the organization. Let’s start at the top.
Data-driven decision making (DDD)3
refers to the practice of basing decisions on the analysis of data,
rather than purely on intuition. For example, a marketer could select advertisements based purely on
her long experience in the field and her eye for what will work. Or, she could base her selection on the
analysis of data regarding how consumers react to different ads. She could also use a combination of these
approaches. DDD is not an all-or-nothing practice, and different firms engage in DDD to greater or lesser
degrees.
8. Data Science and Big Data 6
Figure 1: Data Science in the context of various data-related processes in the organization.
The benefits of data-driven decision making have been demonstrated conclusively. Economist Erik
Brynjolfsson and his colleagues from MIT and Penn’s Wharton School recently conducted a study of how
DDD affects firm performance3
. They developed a measure of DDD that rates firms as to how strongly
they use data to make decisions across the company. They show statistically that the more data-driven
a firm is, the more productive it is—even controlling for a wide range of possible confounding factors.
And the differences are not small: one standard deviation higher on the DDD scale is associated with a
4-6% increase in productivity. DDD also is correlated with higher return on assets, return on equity, asset
utilization and market value; and the relationship seems to be causal.
Our two example case studies illustrate two different sorts of decisions: (1) decisions for which “dis-
coveries” need to be made within data, and (2) decisions that repeat, especially at massive scale, and so
9. Data Science and Big Data 7
decision making can benefit from even small increases in decision-making accuracy based on data anal-
ysis. The Wal-Mart example above illustrates a type-1 problem: Linda Dillman would like to discover
knowledge that will help Wal-Mart prepare for Hurricane Frances’s imminent arrival. Our churn example
illustrates a type-2 DDD problem. A large telecommunications company may have hundreds of millions
of customers, each a candidate for defection. Tens of millions of customers have contracts expiring each
month, so each one of them has an increased likelihood of defection in the near future. If we can im-
prove our ability to estimate, for a given customer, how profitable it would be for us to focus on her, we
can potentially reap large benefits by applying this ability to the millions of customers in the population.
This same logic applies to many of the areas where we have seen the most intense application of data
science and data mining: direct marketing, online advertising, credit scoring, financial trading, help-desk
management, fraud detection, search ranking, product recommendation, and so on.
The diagram in Figure 1 shows data science supporting data-driven decision making, but also overlap-
ping with it. This highlights the fact that increasingly business decisions are being made automatically by
computer systems. Different industries have adopted automatic decision making at different rates. The
finance and telecommunications industries were early adopters. In the 1990s, automated decision mak-
ing changed the banking and consumer credit industries dramatically. In the 1990s, banks and telecom-
munications companies also implemented massive-scale systems for managing data-driven fraud control
decisions. As retail systems were increasingly computerized, merchandising decisions were automated.
Famous examples include Harrah’s casinos’ reward programs and the automated recommendations of
Amazon.com and Netflix. Currently we are seeing a revolution in advertising, due in large part to a huge
increase in the amount of time consumers are spending online, and the ability online to make (literally)
split-second advertising decisions.
10. Data Science and Big Data 8
5 Data processing and “Big Data”
Despite the impression one might get from the media, there is a lot to data processing that is not data sci-
ence. Data engineering and processing are critical to support data science activities, as shown in Figure 1,
but they are more general and are useful for much more. Data processing technologies are important for
many business tasks that do not involve extracting knowledge or data-driven decision making, such as ef-
ficient transaction processing, modern web system processing, online advertising campaign management,
and others.
“Big data” technologies, such Hadoop, Hbase and CouchDB, have received considerable media atten-
tion recently. For this article, we will simply take Big data to mean datasets that are too large for traditional
data processing systems, and that therefore require new technologies. As with the traditional technologies,
big data technologies are used for many tasks, including data engineering. Occasionally, big data tech-
nologies are actually used for implementing data mining techniques, but more often the well-known big
data technologies are used for data processing in support of the data mining techniques and other data
science activities, as represented in Figure 1.
Economist Prasanna Tambe of NYU’s Stern School has examined the extent to which the utilization of
big data technologies seems to help firms4
. He finds that, after controlling for various possible confound-
ing factors, the use of big data technologies correlates with significant additional productivity growth.
Specifically, one standard deviation higher utilization of big data technologies is associated with 1-3%
higher productivity than the average firm; one standard deviation lower in terms of big data utilization
is associated with 1-3% lower productivity. This leads to potentially very large productivity differences
between the firms at the extremes.
11. Data Science and Big Data 9
6 From Big Data 1.0 to Big Data 2.0
One way to think about the state of big data technologies is to draw an analogy with the business adoption
of internet technologies. In Web 1.0, businesses busied themselves with getting the basic technologies
in place so that they could establish a web presence, build electronic commerce capability, and improve
operating efficiency. We can think of ourselves as being in the era of Big Data 1.0, with firms engaged in
building capabilities to process large data. These primarily support their current operations—for example,
to make themselves more efficient.
With Web 1.0, once firms had incorporated basic technologies thoroughly (and in the process had
driven down prices) they started to look further. They began to ask what the Web could do for them,
and how it could improve things they’d always done. This ushered in the era of Web 2.0, where new
systems and companies started to exploit the interactive nature of the Web. The changes brought on by this
shift in thinking are extensive and pervasive; the most obvious are the incorporation of social-networking
components, and the rise of the “voice” of the individual consumer (and citizen).
Similarly, we should expect a Big Data 2.0 phase to follow Big Data 1.0. Once firms have become
capable of processing massive data in a flexible fashion, they should begin asking: What can I now do
that I couldn’t do before, or do better than I could do before? This is likely to usher in the golden era of
data science. The principles and techniques of data science will be applied far more broadly and far more
deeply than they are today.
It is important to note that in the Web 1.0 era some precocious companies began applying Web 2.0
ideas far ahead of the mainstream. Amazon.com is a prime example, incorporating the consumer’s “voice”
early on, in the rating of products and product reviews (and deeper, in the rating of reviewers). Similarly,
we see some companies already applying Big Data 2.0. Amazon again is a company at the forefront,
providing data-driven recommendations from massive data. There are other examples as well. Online
12. Data Science and Big Data 10
advertisers must process extremely large volumes of data (billions of ad impressions a day is not unusual)
and maintain a very high throughput (real-time bidding systems make decisions in tens of milliseconds).
We should look to these and similar industries for signs of advances in big data and data science that
subsequently will be adopted by other industries.
7 Data-analytic thinking
One of the most critical aspects of data science is the support of data-analytic thinking. Skill at thinking
data-analytically is important not just for the data scientist but throughout the organization. For exam-
ple, managers and line employees in other functional areas will only get the best from the company’s data
science resources if they have some basic understanding of the fundamental principles. Managers in enter-
prises without substantial data science resources should still understand basic principles in order to engage
consultants on an informed basis. Investors in data science ventures need to understand the fundamental
principles in order to assess investment opportunities accurately. More generally, businesses increasingly
are driven by data analytics and there is great professional advantage in being able to interact competently
with and within such businesses. Understanding the fundamental concepts, and having frameworks for
organizing data-analytic thinking not only will allow one to interact competently, but will help to envision
opportunities for improving data-driven decision making, or to see data-oriented competitive threats.
Firms in many traditional industries are exploiting new and existing data resources for competitive
advantage. They employ data science teams to bring advanced technologies to bear to increase revenue
and to decrease costs. In addition, many new companies are being developed with data mining as a
key strategic component. Facebook and Twitter, along with many other “Digital 100” companies5
, have
high valuations due primarily to data assets they are committed to capturing or creating.2
Increasingly,
2
Of course, this is not a new phenomenon. Amazon and Google are well established companies that get tremendous value
13. Data Science and Big Data 11
managers need to manage data-analytics teams and data-analysis projects, marketers have to organize and
understand data-driven campaigns, venture capitalists must be able to invest wisely in businesses with
substantial data assets, and business strategists must be able to devise plans that exploit data.
As a few examples, if a consultant presents a proposal to exploit a data asset to improve your business,
you should be able to assess whether the proposal makes sense. If a competitor announces a new data
partnership, you should recognize when it may put you at a strategic disadvantage. Or, let’s say you take
a position with a venture firm and your first project is to assess the potential for investing in an advertising
company. The founders present a convincing argument that they will realize significant value from a
unique body of data they will collect, and on that basis are arguing for a substantially higher valuation. Is
this reasonable? With a data-analytic background you should be able to devise a few probing questions to
determine whether their valuation arguments are plausible.
On a scale less grand, but probably more common, data analytics projects reach into all business units.
Employees throughout these units must interact with the data science team. If these employees do not
have a fundamental grounding in the principles of data-analytic thinking, they will not really understand
what is happening in the business. This lack of understanding is much more damaging in data science
projects than in other technical projects, because the data science supports improved decision making.
Data science projects require close interaction between the scientists and the business people responsible
for the decision making. Firms where the business people do not understand what the data scientists
are doing are at a substantial disadvantage, because they waste time and effort or, worse, because they
ultimately make wrong decisions. A recent article in Harvard Business Review concludes: “For all the
breathless promises about the return on investment in Big Data, however, companies face a challenge.
Investments in analytics can be useless, even harmful, unless employees can incorporate that data into
from their data assets.
14. Data Science and Big Data 12
complex decision making.”6
.
8 Some fundamental concepts of Data Science
There is a set of well-studied, fundamental concepts underlying the principled extraction of knowledge
from data, with both theoretical and empirical backing. These fundamental concepts of data science are
drawn from many fields that study data analytics. Some reflect the relationship between data science and
the business problems to be solved. Some reflect the sorts of knowledge discoveries that can be made,
and are the basis for technical solutions. Others are cautionary and prescriptive. We briefly discuss a few
now. This list is not intended to be exhaustive; detailed discussions even of these would fill a book. The
important thing is that we do think about these fundamental concepts.
Fundamental concept: Extracting useful knowledge from data to solve business problems can be
treated systematically by following a process with reasonably well-defined stages. The Cross-Industry
Standard Process for Data Mining (CRISP-DM7
) is one codification of this process. Keeping such a pro-
cess in mind can structure our thinking about data analytics problems. For example, in actual practice
one repeatedly sees analytical “solutions” that are not based on careful analysis of the problem or are not
carefully evaluated. Structured thinking about analytics emphasizes these often under-appreciated aspects
of supporting decision making with data. Such structured thinking also contrasts critical points where
human creativity is necessary versus points where high-powered analytical tools can be brought to bear.
Fundamental concept: Evaluating data science results requires thinking carefully about the context in
which they will be used. Whether knowledge extracted from data will aid in decision making depends
critically on the application in question. For our churn management example, how exactly are we going
to use the patterns that are extracted from historical data? More generally, does the pattern lead to better
decisions than some reasonable alternative? How well would one have done by chance? How well would
15. Data Science and Big Data 13
one do with a smart “default” alternative?
Fundamental concept: The relationship between the business problem and the analytics solution often
can be decomposed into tractable subproblems via the framework of analyzing expected value. Various
tools for mining data exist, but business problems rarely come neatly prepared for their application. Break-
ing the business problem up into components corresponding to estimating probabilities and computing or
estimating values, along with a structure for recombining the components, is broadly useful. We have
many specific tools for estimating probabilities and values from data. For our churn example, should
the value of the customer be taken into account in addition to the likelihood of leaving? It is difficult to
realistically assess any customer targeting solution without phrasing the problem as one of expected value.
Fundamental concept: Information technology can be used to find informative data items from within a
large body of data. One of the first data science concepts encountered in business analytics scenarios is the
notion of finding correlations. “Correlation” often is used loosely to mean data items that provide infor-
mation about other data items—specifically, known quantities that reduce our uncertainty about unknown
quantities. In our churn example, a quantity of interest is the likelihood that a particular customer will
leave after her contract expires. Before the contract expires this would be an unknown quantity. However,
there may be known data items (usage, service history, how many friends have cancelled contracts) that
correlate with our quantity of interest. This fundamental concept underlies a vast number of techniques
for statistical analysis, predictive modeling, and other data mining.
Fundamental concept: Entities that are similar with respect to known features or attributes often are
similar with respect to unknown features or attributes. Computing similarity is one of the main tools of
data science. There are many ways to compute similarity, and more are invented each year.
Fundamental concept: If you look too hard at a set of data, you will find something—but it might not
generalize beyond the data you’re looking at. This is referred to as “overfitting” a dataset. Techniques for
16. Data Science and Big Data 14
mining data can be very powerful, and the need to detect and avoid overfitting is one of the most important
concepts to grasp when applying data mining tools to real problems. The concept of overfitting and its
avoidance permeates processes, algorithms, and evaluation methods.
Fundamental concept: To draw causal conclusions one must pay very close attention to the presence of
confounding factors, possibly unseen ones. Often, it is not enough simply to uncover correlations in data;
we may want to use our models to guide decisions on how to influence the behavior producing the data.
For example, for our churn problem we want to intervene and cause customer retention. All methods for
drawing causal conclusions—from interpreting the coefficients of regression models to randomized con-
trolled experiments—incorporate assumptions regarding the presence or absence of confounding factors.
In applying such methods it is important to understand these assumptions clearly in order to understand
the scope of any causal claims.
9 Chemistry is not about test tubes: Data science vs. the work of the
data scientist
Two additional, related complications combine to make it more difficult to reach a common understanding
of just what data science is and how it fits with other related concepts.
First is the dearth of academic programs focusing on data science. Without academic programs defin-
ing the field for us, we need to define the field for ourselves. However, each of us sees the field from a
different perspective and thereby forms a different conception. The dearth of academic programs is largely
due to the inertia associated with academia and the concomitant effort involved in creating new academic
programs—especially ones that span traditional disciplines. Universities clearly see the need for such pro-
grams, and it is only a matter of time before this first complication will be resolved. For example, in New
York City alone, two top universities both are creating degree programs in Data Science. Columbia Uni-
17. Data Science and Big Data 15
versity is in the process of creating a master’s degree program within its new Institute for Data Sciences
and Engineering (and has founded a Center focusing on the Foundations of Data Science), and NYU will
commence a degree program in Data Science in Fall 2013.3
The second complication builds on confusion caused by the first. Workers tend to associate with their
field the tasks they spend considerable time on or those they find challenging or rewarding. This is in
contrast to the tasks that differentiate the field from other fields. Forsythe8
described this phenomenon in
an ethnographic study of practitioners in Artificial Intelligence (AI):
The AI specialists I describe view their professional work as science (and in some cases
engineering) ... The scientists’ work and the approach they take to it make sense in relation to
a particular view of the world that is taken for granted in the laboratory...Wondering what it
means to “do AI,” I have asked many practitioners to describe their own work.Their answers
invariably focus on one or more of the following: problem solving, writing code, and building
systems.
Forsythe goes on to explain that the AI practitioners focus on these three activities even when it is clear
that they spend much time doing other things (even less related specifically to AI). Importantly, none of
these three tasks differentiates AI from many other scientific and engineering fields. Clearly just being very
good at these three things does not an AI scientist make. And as Forsythe points out, technically the latter
two are not even necessary, as the lab director had not written code or built systems for years. Nonetheless,
these are the tasks the AI scientists saw as defining their work—they apparently did not explicitly consider
the notion of what makes doing AI different from doing other tasks that involve problem solving, writing
3
The NYU degree is just about a fait accompli; it should receive state approval presently, after which it can be advertised.
We should check before publication. Otherwise, we should state it more like what Columbia says.
18. Data Science and Big Data 16
code, and system building. (This is possibly due to the fact that in AI there were academic distinctions to
call on.)
Taken together, these two complications cause particular confusion in data science, because there are
few academic distinctions to fall back on, and moreover due to the state of the art in data processing, data
scientists tend to spend a majority of their problem-solving time on data preparation and processing. The
goal of such preparation is either to subsequently apply data science methods, or to understand the results.
However, that does not change the fact that the day-to-day work of a data scientist—especially an entry-
level one—may be largely data processing. This is directly analogous to an entry-level chemist spending
the majority of her time doing technical lab work. If this were all she were trained to do, she likely would
not be rightly called a chemist, but rather a lab technician. Importantly for being a chemist is that this
work is in support of the application of the science of chemistry, and hopefully the eventual advancement
to jobs involving more chemistry and less technical work. Similarly for Data Science: a Chief Scientist
in a data-science-oriented company will do much less data processing and more data analytics design and
interpretation.
At the time of this writing, discussions of data science inevitably mention not just the analytical skills
but the popular tools used in such analysis. For example, it is common to see job advertisements men-
tioning data mining techniques (e.g. random forests, support vector machines), specific application areas
(recommendation systems, ad placement optimization), alongside popular software tools for processing
big data (SQL, Hadoop, CouchDB). This is natural. The particular concerns of data science in business are
fairly new, and businesses are still working to figure out how best to address them. Continuing our anal-
ogy, the state of data science may be likened to that of chemistry in the mid 19th century, when theories
and general principles were being formulated and the field was largely experimental. Every good chemist
had to be a competent lab technician. Similarly, it is hard to imagine a working data scientist who is not
19. Data Science and Big Data 17
proficient with certain software tools. A firm may be well served by requiring that their data scientists
have skills to access, prepare, and process data using tools the firm has adopted.
Nevertheless, we emphasize that there is an important reason to focus here on the general principles
of data science. In ten years’ time the predominant technologies will likely have changed or advanced
enough that today’s choices would seem quaint. On the other hand, the general principles of data science
are not so differerent than they were 20 years ago, and likely will change little over the coming decades.
10 Conclusion
Underlying the extensive collection of techniques for mining data is a much smaller set of fundamental
concepts comprising data science. It is our position that in order for Data Science to flourish as a field,
rather than to drown in the flood of popular attention, we must think beyond the algorithms, techniques, and
tools in common use. We must think about the core principles and concepts that underlie the techniques,
and also the systematic thinking that fosters success in data-driven decision making. These concepts are
general and very broadly applicable.
Success in today’s data-oriented business environment requires being able to think about how these
fundamental concepts apply to particular business problems—to think data-analytically. This is aided by
conceptual frameworks that themselves are part of data science. For example, the automated extraction of
patterns from data is a process with well-defined stages. Understanding this process and its stages helps
structure problem solving, makes it more systematic, and thus less prone to error.
There is strong evidence that business performance can be improved substantially via data-driven de-
cision making3
, big data technologies4
, and data science techniques based on big data9;10
. Data science
supports data-driven decision making—and sometimes allows making decisions automatically at massive
scale—and depends upon technologies for “big data” storage and engineering. However, the principles
20. Data Science and Big Data 18
of data science are its own, and should be considered and discussed explicitly in order for data science to
retain meaning.
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