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