Recommended Readings on Data Science

Juan Murillo

d&a blog

The journey that has taken data and its applications from the periphery to the heart of today’s debate has multiple ramifications: social, ethical and legal questions about the opportunities and risks of “datification” in our society, business aspects around the challenge of transforming data into innovative solutions perceived as a new value by customers, and technical and methodological aspects around the profession in vogue: data science. At BBVA Data & Analytics, we make our efforts to disseminate information on all these fronts, and this time we share a selection of bibliographical references aimed at facilitating different objectives in different audiences.

In blocks A and D the target audience of the publications would be very broad: project managers, design and business development profiles, or simply anyone interested in learning and having an elementary fundamentals about the discipline, its possible applications and the implicit dilemmas.

In blocks B and C, however, we point to another audience: the “practitioner” data scientist who seeks to resolve methodological doubts and delve deeper into the various specializations of the discipline:

 

A] Approach to data science and its business applications: introduction to statistics, without the need to know programming codes, but certain mathematical notions to propose applications with rigor: definitions, catalog of possibilities, technical risks related to the treatment of biases , data quality, precision and accuracy metrics.

  • Statistics Done Wrong. Alex Reinhart
  • How to Lie with Statistics. Darrell Huff & Irving Geis
  • Data Science for Business: What you need to know about data mining and data-analytic thinking. Foster Provost
  • The Signal and the Noise: Why So Many Predictions Fail-but Some Don’t. Nate Silver
  • Everything Is Obvious: How Common Sense Fails Us. Duncan J. Watts
  • Big Data: A Revolution That Will Transform How We Live, Work, and Think. Viktor Mayer-Schönberger & Kenneth Cukier

 

B] Deepening in data science; analytics, data architecture and programming:

  • The entire O’ Reilly collection, highlighting the following:
    • Practical Statistics for Data Scientists: 50 Essential Concepts. Peter Bruce & Andrew Bruce
    • Advanced Analytics with Spark: Patterns for Learning from Data at Scale. Sandy Ryza & Uri Laserson
    • Python for Data Analysis. Wes McKinney
  • Doing Bayesian Data Analysis. John K. Kruschke
  • Transparent Data Mining for Big and Small Data. Tania Cerquitelli, Danielle Quercia & Frank Pasquale.
  • The R Book. Michael J. Crawley
  • The Data Warehouse Toolkit: The definitive guide to dimensional modeling. Ralph Kimball
  • Displaying Time Series, Spatial, and Space-Time Data with R. Oscar Perpinan Lamigueiro
  • Computer Age Statistical Inference. Algorithms, Evidence and data Science. Bradley Efron & Trevor Hastie.
  • Introduction to Linear Algebra. Gilbert Strang
  • Applied Predictive Modelling. Max Kuhn & Kjell Johnson
  • Mining Massive Datasets. Jeffrey Ullman

 

C]  Deepening in data science; statistics, machine learning and artificial intelligence:

  • The Elements of Statistical Learning. Trevor Hastie, Robert Tibshirani and  JH Friedman
  • Statistical Rethinking: A Bayesian Course with Examples in R and Stan. Richard McElreath
  • Network Science. Albert-László Barabási & Márton Pósfai
  • Artificial Intelligence. Peter Norvig & Stuart J. Russell
  • Machine Learning. A Probabilistic Perspective. Kevin P. Murphy
  • Pattern Recognition and Machine Learning. Christopher Bishop
  • Networks. An Introduction. M.E.J. Newman
  • Unsupervised Learning Algorithms. M. Emre Celebi & Kemal Aydin
  • Deep Learning (Adaptive Computation and Machine Learning series). Ian Goodfellow and Yoshua Bengio and Aaron Courville
  • Reinforcement Learning: an introduction. Richard S. Sutton, Andrew Barto
  • Learning From Data. Yaser S. Abu-Mostafa, Malik Magdon-Ismail, Hsuan-Tien Lin

 

D] Ethical dilemmas and forward-looking visions of artificial intelligence: moral risks, unjust discrimination, centralized control of society, the replacement of man by machine (speech by Nick Bostrom, Elon Musk or Stephen Hawking), but also his most visionary and techno-optimistic counterpoint (Ray Kurzweil, Grady Booch, etc.).

  • Algorithms to Live By: The Computer Science of Human Decisions. Brian Christian & Tom Griffiths
  • Weapons of Math Destruction. Cathy O’Neil
  • Data and Goliath: The Hidden Battles to Collect Your Data and Control Your World. Bruce Schneier
  • Homo Deus. Yuval Noah Harari
  • Naked Diplomacy. Understanding Power and Politics in the Digital Age. Tom Fletcher
  • Life 3.0: Being Human in the Age of Artificial Intelligence. Max Tegmark
  • Superintelligence: Paths, Dangers, Strategies. Nick Bostrom
  • Who Owns The Future? Lanier, Jaron
  • Machines Of Loving Grace. John Markoff
  • How to Create a Mind: The Secret of Human Thought Revealed. Ray Kurzweil