Most of the references in this book (Roman's Data Science. How to monetize your data.) are provided via hyperlinks. Over time, some of them will stop working. I have developed a mechanism to ensure that all the references remain accessible that is available at[Reference number]. “Reference number” corresponds to the number of the respective reference in the text (for example, for number 23: If I learn that a link or QR code in this book has stopped working, I will restore it as soon as possible. All the reader has to do is let me know.

Make great presentations, longreads, and landing pages, as well as photo stories, blogs, lookbooks, and all other kinds of content oriented projects.
1. Behave The Biology of Humans at Our Best and Worst Robert Sapolsky The Biology of Humans at Our Best and Worst Robert Sapolsky
2. Letter to shareholders 2015
3. Letter to shareholders 2015
4. What is decision intelligence
5. Focus on decisions not outcomes
6. Russian Covid deaths three times the official toll
7. Understanding Decision Fatigue
9. Building Data Science Teams. DJ Patil
10. What’s the difference between analytics and statistics?
11. Debunking Narrative Fallacies with Empirically-Justified Explanations
12. AB test attack: recipe 'R'+t(101)+'es46'”
13. Measure What Matters: How Google, Bono, and the Gates Foundation Rock the World with OKRs. Doerr JohnИзмеряйте самое важное. Как Google, Intel и другие компании добиваются роста с помощью OKR | Дорр Джон
14. Dogs vs. Cats: Create an algorithm to distinguish dogs from cats
15. ResNet-50 is a convolutional neural network
16. Data scientists mostly just do arithmetic and that’s a good thing
17. Интервью для BBC Карл Густав Юнг, основатель аналитической психологии, 1955 год
18. The Tyranny of Metrics. Jerry Muller Tyranny of Metrics. Jerry Muller
19. Spark/Scala Young Fighter Course
20. Data science management
21. You and Your Research. Richard Hamming
22. Planning Poker
23. Hypothesis Testing: How to Eliminate Ideas as Soon as Possible. Roman Zykov
24. Application of Kullback-Leibler divergence for short-term user interest detection
25. Does Stylish Cross-Sell Store Need: Retail Rocket's Experience in Image Analysis for Formation of Recommendations
26. The most powerful idea in data science
27. Elementary Concepts in Statistics
28. Say It With Charts. Jene Zelazny It With Charts. Jene Zelazny
29. The Cognitive Style of Powerpoint: pitching out corrupts within. Edward R. Tafte Cognitive Style of Powerpoint: pitching out corrupts within. Edward R. Tufte
30. On Pair Programming. Martin Fowler
31. Technical Debt. Martin Fowler
32. Netflix Culture
33. Retailrocket recommender system dataset
34. Making Sense of Data Warehouse Architecture
35. Columnar database: a smart choice for data warehouses
36. System and method for efficient large-scale data processing (Google),650,331.PN.&OS=PN/7,650,331&RS=PN/7,650,331
37. MapReduce: Simplified Data Processing on Large Clusters
38. The Friendship That Made Google Huge
39. Apache Hadoop
40. Apache Spark
41. Loader of HDFS files with combining small files on Spark
42. Python for Data Analysis. Wes McKinney for Data Analysis. Wes McKinney
43. Cloudera Hadoop - Choosing and Configuring Data Compression
44. Google colab
45. Kaggle notebooks
46. Gartner Top 10 Trends in Data and Analytics for 2020
47. Metabase
48. SuperSet
49. Beyond Interactive: Notebook Innovation at Netflix
50. What Artificial Intelligence Can and Can’t Do Right Now
51. Regression Towards Mediocrity in Hereditary Stature. Francis Galton
52. Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches
53. Kaggle’s State of Data Science and Machine Learning 2019
54. Unity is strength — A story of model composition
55. Introduction to Machine Learning. Second Edition. Ethem Alpaydin. to Machine Learning. Second Edition. Ethem Alpaydin.
56. Scikit learn Ensemble methods
57. XGBoost: Introduction to Boosted Trees
58. LightGBM
59. Catboost
60. Andrew Ng. Machine learning Yearning
61. Coursera Machine Learning
62. How do I learn machine learning?
63. Fastml4j on Scala
64. Netflix prize
65. Netflix Recommendations: Beyond the 5 stars (Part 1)
66. Andrew Gelman, Jenifer Hill “Data Analysis Using Regression and Multilevel/Hierarchical Models”
67. Google Course of ML: Imbalanced Data
68. 10 More lessons learned from building real-life Machine Learning systems
69. ScaleFactor Raised $100 Million In A Year Then Blamed Covid-19 For Its Demise. Employees Say It Had Much Bigger Problems.
71. DRILLING DOWN: Turning Customer Data into Profits with a Spreadsheet - Third Edition, Jim Novo DOWN: Turning Customer Data into Profits with a Spreadsheet - Third Edition, Jim Novo
72. Google Rules of Machine Learning: Best Practices for ML Engineering
73. Louse laser pioneer in contention for invention award
74. Lox prices in city eateries could jump due to salt-water parasite
75. Adidas backpedals on robotic shoe production with Speedfactory closures
76. Ronald Aylmer Fisher biography
77. Larry Wasserman, All of Statistics: A Concise Course in Statistical Inference (Springer Texts in Statistics), Springer (December 1, 2010) Wasserman, All of Statistics: A Concise Course in Statistical Inference (Springer Texts in Statistics), Springer (December 1, 2010)
78. Nonparametric Statistics Introductory Overview - When to Use Which Method
79. B.Efron, Bootstrap Methods: Another Look at the Jackknife
80. Bootstrap confidence intervals
81. Criteo Labs: Why your A/B-test needs confidence intervals
82. Bayesian A/B tests
83. William Bolstard, Introduction to Bayesian Statistics Bolstard, Introduction to Bayesian Statistics
84. Ron Kohavi, Alex Deng, Roger Longbotham, and Ya Xu. Seven Rules of Thumb for Web Site Experimenters
85. Retail Rocket Segmentator
86. Reinforcement Learning: An Introduction
87. The Privacy Project
88. One Nation tracked
89. Google Authorized Buyers, Real-time Bidding
90. Explained: Data in the Criteo Engine
91. We Built an ‘Unbelievable’ (but Legal) Facial Recognition Machine,
92. What ISPs Can See, Upturn, March 2016
93. The GDPR Is a Cookie Monster
94. IAB. Cookies on Mobile 101
95. How Online Shopping Makes Suckers of Us All
96. Why are the largest Russian Internet sites removing the Liveinternet counter?
97. How To Break Anonymity of the Netflix Prize Dataset,
98. Alexa, are you invading my privacy? – the dark side of our voice assistants
99. LeakyPick: IoT Audio Spy Detector
100. I SEARCH, THEREFORE I AM, Andreas Weigend
101. We Read 150 Privacy Policies. They Were an Incomprehensible Disaster,
102. 5 Americans who used NSA facilities to spy on lovers
103. Pie & AI Asia: On Ethical AI with Andrew Ng
104. What Do We Do About the Biases in AI?
105. Ad Blocking Growth Is Slowing Down, but Not Going Away
106. IAB Europe Guide to the Post Third-Party Cookie Era
107. Comparing privacy laws: GDPR v. Russian Law on Personal Data
108. This Article Is Spying on You
109. Functionalism: A New Approach to Web Analytics
110. Strategic Database Marketing. Arthur Hughes Database Marketing. Arthur Hughes
111. A good founder’s guide to bad VC behaviour
112. Two Decades of Recommender Systems at
113. Item-to-Item Collaborative Filtering, Greg Linden, Brent Smith, and Jeremy York
114. How to use Merchandising eVars in Adobe Analytics,
116. Retail Rocket