AI News, Data Science Book

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

Big Data Analytics

Data mining technology helps you examine large amounts of data to discover patterns in the data – and this information can be used for further analysis to help answer complex business questions.

With data mining software, you can sift through all the chaotic and repetitive noise in data, pinpoint what's relevant, use that information to assess likely outcomes, and then accelerate the pace of making informed decisions.

It has become a key technology to doing business due to the constant increase of data volumes and varieties, and its distributed computing model processes big data fast.

Text mining uses machine learning or natural language processing technology to comb through documents – emails, blogs, Twitter feeds, surveys, competitive intelligence and more – to help you analyze large amounts of information and discover new topics and term relationships.

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

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.

Introducing the Center for Big Data Statistics - CBS

Statistics for Beginners 2018 | Introduction to Statistics | Statistics Tutorial for Data Analytics

Statistics for Beginners 2018 | Introduction to Statistics | Statistics Tutorial for Data Analytics ...

Types of Data: Nominal, Ordinal, Interval/Ratio - Statistics Help

The kind of graph and analysis we can do with specific data is related to the type of data it is. In this video we explain the different levels of data, with examples.

Ways to represent data | Data and statistics | 6th grade | Khan Academy

Here are a few of the many ways to look at data. Which is your favorite? Practice this lesson yourself on KhanAcademy.org right now: ...

How to Read Your Textbooks More Efficiently - College Info Geek

Don't be a textbook zombie. Companion blog post with notes, resource links, and the HabitRPG guild link: ..

What is data wrangling? Intro, Motivation, Outline, Setup -- Pt. 1 Data Wrangling Introduction

Data wrangling is too often the most time-consuming part of data science and applied statistics. Two tidyverse packages, tidyr and dplyr, help make data ...

How To Learn Faster

Get smart with Brilliant: Subscribe: The 9 BEST Scientific Study Tips: Created

Free Your Data Scientists With Industrialized R Analytics

Interactions among explanatory variables in R

Learn more about statistical modeling at In thinking about effect size, keep in mind that there ..

Project Description - Intro to Data Science

This video is part of an online course, Intro to Data Science. Check out the course here: This course was designed as ..