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

8. Thinking Like a Data Scientist

This chapter introduces a framework, techniques, and hands-on exercises to help business stakeholders think like a data scientist.

The eight-step thinking like a data scientist framework covers identifying key business initiative, developing business stakeholder personas, identifying strategic nouns, capturing business decisions, brainstorming business questions, leveraging by analysis, creating actionable scores and putting analytics into action.

Data Scientists at Work 1st ed. Edition

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A gallery of interesting Jupyter Notebooks

Important contribution instructions: If you add new content, please ensure that for any notebook you link to, the link is to the rendered version using nbviewer, rather than the raw file.

These are notebooks that use [one of the IPython kernels for other languages](IPython kernels for other languages): The IPython protocols to communicate between kernels and clients are language agnostic, and other programming language communities have started to build support for this protocol in their language.

The interactive plotting library Nyaplot has some case studies using IRuby: This section contains academic papers that have been published in the peer-reviewed literature or pre-print sites such as the ArXiv that include one or more notebooks that enable (even if only partially) readers to reproduce the results of the publication.

Is Data Science A Viable Career Path?

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Data Analysis for Social Scientists | MITx on edX | Course About Video

Learn methods for harnessing and analyzing data to answer questions of cultural, social, economic, and policy interest. Take this course free on edX:

Scientific Notation: Introduction

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The Growth of a Data Scientist

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Data Scientist & YouTuber Siraj Raval | The Lavendaire Lifestyle Podcast

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2030 Days of a Data Scientist's Time

Check out this data viz on where my time has gone in the last 2030 days. Some cool points to notes: - Project Alpha is my time I've put toward building my business Data Scientists Pty Ltd...

How to Read and Comprehend Scientific Research Articles

This tutorial will discuss how to read a scientific article, how to find the main points of the article, and how to take effective notes.

Notes from Nature empowers citizen scientists to add to biological collections

Today, there are an estimated two billion specimens housed in natural history museums around the world! For the information held in these collections to be used to its full potential there...

Becoming a Data Scientist Podcast: Episode 07 - Enda Ridge

Data Scientist, Author, and manager of data science teams Enda Ridge talks to us about data governance, data provenance, reproducible analysis, work pipelines and products, and people, among...