AI News, My Data Science Book - Table of Contents

My Data Science Book - Table of Contents

65 The Big Data Ecosystem 70 Summary 71 Chapter 3 - Becoming a Data Scientist 73 Key Features of Data Scientists 73 Types of Data Scientists 78 Data Scientist Demographics 82 Training for Data Science 82 Data Scientist Career Paths 89 Summary 107 Chapter 4 - Data Science Craftsmanship, Part I 109 New Types of Metrics 110 Choosing Proper Analytics Tools 113 Visualization 118 Statistical Modeling Without Models 122 Three Classes of Metrics: Centrality, Volatility, Bumpiness 125 Statistical Clustering for Big Data 129 Correlation and R-Squared for Big Data 130 Computational Complexity 137 Structured Coefficient 140 Identifying the Number of Clusters 141 Internet Topology Mapping 143 Securing Communications: Data Encoding 147 Summary 149 Chapter 5 - Data Science Craftsmanship, Part II 151 Data Dictionary 152 Hidden Decision Trees 153 Model-Free Confidence Intervals 158 Random Numbers 161 Four Ways to Solve a Problem 163 Causation Versus Correlation 165 How Do You Detect Causes?

166 Life Cycle of Data Science Projects 168 Predictive Modeling Mistakes 171 Logistic-Related Regressions 172 Experimental Design 176 Analytics as a Service and APIs 178 Miscellaneous Topics 183 New Synthetic Variance for Hadoop and Big Data 187 Summary 193 Chapter 6 - Data Science Application Case Studies 195 Stock Market 195 Encryption 209 Fraud Detection 216 Digital Analytics 230 Miscellaneous 245 Summary 253 Chapter 7 - Launching Your New Data Science Career 255 Job Interview Questions 255 Testing Your Own Visual and Analytic Thinking 263 From Statistician to Data Scientist 268 Taxonomy of a Data Scientist 273 400 Data Scientist Job Titles 279 Salary Surveys 281 Summary 285 Chapter 8 - Data Science Resources 287 Professional Resources 287 Career-Building Resources 295 Summary 298 Index 299 Other links

R for Data Science

pradhan (@adidoit), Andrea Gilardi (@agila5), Ajay Deonarine (@ajay-d), @AlanFeder, pete (@alonzi), Alex (@ALShum), Andrew Landgraf (@andland), @andrewmacfarland, Michael Henry (@aviast), Mara Averick (@batpigandme), Brent Brewington (@bbrewington), Bill Behrman (@behrman), Ben Herbertson (@benherbertson), Ben Marwick (@benmarwick), Ben Steinberg (@bensteinberg), Brandon Greenwell (@bgreenwell), Brett Klamer (@bklamer), Christian Mongeau (@chrMongeau), Cooper Morris (@coopermor), Colin Gillespie (@csgillespie), Rademeyer Vermaak (@csrvermaak), Abhinav Singh (@curious-abhinav), Curtis Alexander (@curtisalexander), Christian G.

Storey (@jdstorey), Jeff Boichuk (@jeffboichuk), Gregory Jefferis (@jefferis), 蒋雨蒙 (@JeldorPKU), Jennifer (Jenny) Bryan (@jennybc), Jen Ren (@jenren), Jeroen Janssens (@jeroenjanssens), Jim Hester (@jimhester), JJ Chen (@jjchern), Joanne Jang (@joannejang), John Sears (@johnsears), @jonathanflint, Jon Calder (@jonmcalder), Jonathan Page (@jonpage), Justinas Petuchovas (@jpetuchovas), Jose Roberto Ayala Solares (@jroberayalas), Julia Stewart Lowndes (@jules32), Sonja (@kaetschap), Kara Woo (@karawoo), Katrin Leinweber (@katrinleinweber), Karandeep Singh (@kdpsingh), Kyle Humphrey (@khumph), Kirill Sevastyanenko (@kirillseva), @koalabearski, Kirill Müller (@krlmlr), Noah Landesberg (@landesbergn), @lindbrook, Mauro Lepore (@maurolepore), Mark Beveridge (@mbeveridge), Matt Herman (@mfherman), Mine Cetinkaya-Rundel (@mine-cetinkaya-rundel), Matthew Hendrickson (@mjhendrickson), @MJMarshall, Mustafa Ascha (@mustafaascha), Nelson Areal (@nareal), Nate Olson (@nate-d-olson), Nathanael (@nateaff), Nick Clark (@nickclark1000), @nickelas, Nirmal Patel (@nirmalpatel), Nina Munkholt Jakobsen (@nmjakobsen), Jakub Nowosad (@Nowosad), Peter Hurford (@peterhurford), Patrick Kennedy (@pkq), Radu Grosu (@radugrosu), Ranae Dietzel (@Ranae), Robin Gertenbach (@rgertenbach), Richard Zijdeman (@rlzijdeman), Robin (@Robinlovelace), Emily Robinson (@robinsones), Rohan Alexander (@RohanAlexander), Romero Morais (@RomeroBarata), Albert Y.

Data Science for Business by Foster Provost, Tom Fawcett

Modeling the probability of default had changed the industry from personal assessment of the likelihood of default to strategies of massive scale and market share, which brought along concomitant economies of scale.

It may seem strange now, but at the time, credit cards essentially had uniform pricing, for two reasons: (1) the companies did not have adequate information systems to deal with differential pricing at massive scale, and (2) bank management believed customers would not stand for price discrimination.

Around 1990, two strategic visionaries (Richard Fairbanks and Nigel Morris) realized that information technology was powerful enough that they could do more sophisticated predictive modeling—using the sort of techniques that we discuss throughout this book—and offer different terms (nowadays: pricing, credit limits, low-initial-rate balance transfers, cash back, loyalty points, and so on).

They knew that a small proportion of customers actually account for more than 100% of a bank’s profit from credit card operations (because the rest are break-even or money-losing).

Since banks were offering credit with a specific set of terms and a specific default model, they had the data to model profitability (1) for the terms they actually have offered in the past, and (2) for the sort of customer who was actually offered credit (that is, those who were deemed worthy of credit by the existing model).

out 45,000 of these “scientific tests” as they called them.[4] Studies giving clear quantitative demonstrations of the value of a data asset are hard to find, primarily because firms are hesitant to divulge results of strategic value.

The relationship is clear and striking and—significantly, for the point here—the predictive performance continues to improve as more data are used, increasing throughout the range investigated by Martens and Provost with no sign of abating.

If these trends generalize, and the banks are able to apply sophisticated analytics, banks with bigger data assets should be better able to identify the best customers for individual products.

Amazon was able to gather data early on online customers, which has created significant switching costs: consumers find value in the rankings and recommendations that Amazon provides.

Harrah’s casinos famously invested in gathering and mining data on gamblers, and moved itself from a small player in the casino business in the mid-1990s to the acquisition of Caesar’s Entertainment in 2005 to become the world’s largest gambling company.

The huge valuation of Facebook has been credited to its vast and unique data assets (Sengupta, 2012), including both information about individuals and their likes, as well as information about the structure of the social network.

Data Science Book

65 The Big Data Ecosystem 70 Summary 71 Chapter 3 - Becoming a Data Scientist73 Key Features of Data Scientists 73 Types of Data Scientists 78 Data Scientist Demographics 82 Training for Data Science 82 Data Scientist Career Paths 89 Summary 107 Chapter 4 - Data Science Craftsmanship, Part I109 New Types of Metrics 110 Choosing Proper Analytics Tools 113 Visualization 118 Statistical Modeling Without Models 122 Three Classes of Metrics: Centrality, Volatility, Bumpiness 125 Statistical Clustering for Big Data 129 Correlation and R-Squared for Big Data 130 Computational Complexity 137 Structured Coefficient 140 Identifying the Number of Clusters 141 Internet Topology Mapping 143 Securing Communications: Data Encoding 147 Summary 149 Chapter 5 - Data Science Craftsmanship, Part II151 Data Dictionary 152 Hidden Decision Trees 153 Model-Free Confidence Intervals 158 Random Numbers 161 Four Ways to Solve a Problem 163 Causation Versus Correlation 165 How Do You Detect Causes?

166 Life Cycle of Data Science Projects 168 Predictive Modeling Mistakes 171 Logistic-Related Regressions 172 Experimental Design 176 Analytics as a Service and APIs 178 Miscellaneous Topics 183 New Synthetic Variance for Hadoop and Big Data 187 Summary 193 Chapter 6 - Data Science Application Case Studies195 Stock Market 195 Encryption 209 Fraud Detection 216 Digital Analytics 230 Miscellaneous 245 Summary 253 Chapter 7 - Launching Your New Data Science Career255 Job Interview Questions 255 Testing Your Own Visual and Analytic Thinking 263 From Statistician to Data Scientist 268 Taxonomy of a Data Scientist 273 400 Data Scientist Job Titles 279 Salary Surveys 281 Summary 285 Chapter 8 - Data Science Resources287 Professional Resources 287 Career-Building Resources 295 Summary 298 Index299 Other links

Data science

Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured,[1][2]

Data science is a 'concept to unify statistics, data analysis, machine learning and their related methods' in order to 'understand and analyze actual phenomena' with data.[3]

Turing award winner Jim Gray imagined data science as a 'fourth paradigm' of science (empirical, theoretical, computational and now data-driven) and asserted that 'everything about science is changing because of the impact of information technology' and the data deluge.[4][5]

In many cases, earlier approaches and solutions are now simply rebranded as 'data science' to be more attractive, which can cause the term to become 'dilute[d] beyond usefulness.'[10]

In 1974, Naur published Concise Survey of Computer Methods, which freely used the term data science in its survey of the contemporary data processing methods that are used in a wide range of applications.

In his report, Cleveland establishes six technical areas which he believed to encompass the field of data science: multidisciplinary investigations, models and methods for data, computing with data, pedagogy, tool evaluation, and theory.

In 2005, The National Science Board published 'Long-lived Digital Data Collections: Enabling Research and Education in the 21st Century' defining data scientists as 'the information and computer scientists, database and software and programmers, disciplinary experts, curators and expert annotators, librarians, archivists, and others, who are crucial to the successful management of a digital data collection' whose primary activity is to 'conduct creative inquiry and analysis.'[25]

Turing award winner Jim Gray envisioned 'data-driven science' as a 'fourth paradigm' of science that uses the computational analysis of large data as primary scientific method[4][5]

Similarly, in business sector, multiple researchers and analysts state that data scientists alone are far from being sufficient in granting companies a real competitive advantage[33]

and consider data scientists as only one of the four greater job families companies require to leverage big data effectively, namely: data analysts, data scientists, big data developers and big data engineers.[34]

Now the data in those disciplines and applied fields that lacked solid theories, like health science and social science, could be sought and utilized to generate powerful predictive models.[1]

In an effort similar to Dhar's, Stanford professor David Donoho, in September 2015, takes the proposition further by rejecting three simplistic and misleading definitions of data science in lieu of criticisms.[36]

Second, data science is not defined by the computing skills of sorting big data sets, in that these skills are already generally used for analyses across all disciplines.[36]

Third, data science is a heavily applied field where academic programs right now do not sufficiently prepare data scientists for the jobs, in that many graduate programs misleadingly advertise their analytics and statistics training as the essence of a data science program.[36][37]

This way, the future of data science not only exceeds the boundary of statistical theories in scale and methodology, but data science will revolutionize current academia and research paradigms.[36]

As Donoho concludes, 'the scope and impact of data science will continue to expand enormously in coming decades as scientific data and data about science itself become ubiquitously available.'[36]

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