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We are in the process of writing and adding new material (compact eBooks) exclusively available to our members, and written in simple English, by world leading experts in AI, data science, and machine learning. In the upcoming months, the following will be added:

  • The Machine Learning Coding Book
  • Original Math, Stats and Probability Problems - with Solutions
  • Experimental Math for Data Scientists
  • Detecting and Leveraging Patterns in Data
  • Career Advice

We invite you to sign up here to not miss these free books.  Previous material (also for members only) can be found here.

Currently, the following content is available:

1. Statistics: New Foundations, Toolbox, and Machine Learning Recipes 

By Vincent Granville. This book is intended for busy professionals working with data of any kind: engineers, BI analysts, statisticians, operations research, AI and machine learning professionals, economists, data scientists, biologists, and quants, ranging from beginners to executives. In about 300 pages and 28 chapters it covers many new topics, offering a fresh perspective on the subject, including rules of thumb and recipes that are easy to automate or integrate in black-box systems, as well as new model-free, data-driven foundations to statistical science and predictive analytics. The approach focuses on robust techniques; it is bottom-up (from applications to theory), in contrast to the traditional top-down approach. The material is accessible to practitioners with a one-year college-level exposure to statistics and probability. The compact and tutorial style, featuring many applications with numerous illustrations, is aimed at practitioners, researchers, and executives in various quantitative fields.

The table of content is available hereThe book can be accessed here (members only.)

2. Book: Classification and Regression In a Weekend

This tutorial began as a series of weekend workshops created by Ajit Jaokar and Dan Howarth. The idea was to work with a specific (longish) program such that we explore as much of it as possible in one weekend. This book is an attempt to take this idea online. The best way to use this book is to work with the Python code as much as you can. The code has comments.  But you can extend the comments by the concepts explained here.

The table of contents is available here. The book can be accessed here (members only.)

3. Online Encyclopedia of Statistical Science

This online book is intended for beginners, college students and professionals confronted with statistical analyses. It is also a refresher for professional statisticians.  The book covers over 600 concepts, chosen out of more than 1,500 for their popularity. Entries are listed in alphabetical order, and broken down into 18 parts. In addition to numerous illustrations, we have added 100 topics not covered in our online series Statistical Concepts Explained in Simple English. We also included a number of visualizations from our series Statistical Concepts Explained in One Picture.

The table of content is available here. The book can be accessed here (members only.)

4. Book: Azure Machine Learning in a Weekend

This book by Ajit Jaokar and Ayse Mutlu is the second book in the ‘in a weekend’ series – after Classification and Regression in a weekend. The idea of the ‘in a weekend’ series of books is to study one complex section of code in a weekend to master the concept. Cloud computing changes the development paradigm. Specifically, it combines development and deployment (the DevOps approach). In complex environments, the developer has to know more than the coding. Rather, she has to be familiar with both the data engineering and the DevOps. This book helps you to get started with coding a complex AI application for the Cloud(Azure).

The table of content is available hereThe book can be accessed here (members only.)

5. Book: Enterprise AI - An Application Perspective 

Enterprise AI: An applications perspective takes a use case driven approach to understand the deployment of AI in the Enterprise. Designed for strategists and developers, the book provides a practical and straightforward roadmap based on application use cases for AI in Enterprises. The authors (Ajit Jaokar and Cheuk Ting Ho) are data scientists and AI researchers who have deployed AI applications for Enterprise domains. The book is used as a reference for Ajit and Cheuk's new course on Implementing Enterprise AI.

The table of content is available here.  The book can be accessed here (members only.)

6. Book: Applied Stochastic Processes

Full title: Applied Stochastic Processes, Chaos Modeling, and Probabilistic Properties of Numeration Systems. Published June 2, 2018. Author: Vincent Granville, PhD. (104 pages, 16 chapters.)

This book is intended to professionals in data science, computer science, operations research, statistics, machine learning, big data, and mathematics. In 100 pages, it covers many new topics, offering a fresh perspective on the subject. It is accessible to practitioners with a two-year college-level exposure to statistics and probability. The compact and tutorial style, featuring many applications (Blockchain, quantum algorithms, HPC, random number generation, cryptography, Fintech, web crawling, statistical testing) with numerous illustrations, is aimed at practitioners, researchers and executives in various quantitative fields.

New ideas, advanced topics, and state-of-the-art research are discussed in simple English, without using jargon or arcane theory. It unifies topics that are usually part of different fields (data science, operations research, dynamical systems, computer science, number theory, probability) broadening the knowledge and interest of the reader in ways that are not found in any other book. This short book contains a large amount of condensed material that would typically be covered in 500 pages in traditional publications. Thanks to cross-references and redundancy, the chapters can be read independently, in random order.

The table of content is available here. The book (PDF) can be accessed here (members only.) 

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Comment by David Scott on July 10, 2019 at 4:19pm

Wow! This is a lot of content. I look forward to using this for a refresher as well as learning something new.

Comment by Zarak Jamal Khan on June 26, 2019 at 6:40pm

That's Amazing, this platform is a big help for budding data scientist like me. Looking forward to 'Experimental Math' and 'Orignal math, stats and probability' books. 

Can someone guide me, I'm looking for an online course in applied statistics? Vincent if you can guide me through. 

Comment by hazzi on May 16, 2019 at 12:58am

Good !!

Comment by Vincent Granville on December 5, 2018 at 8:19pm

Hi Christoph, A better resolution picture is available here. The original can be found in this article. Best, Vincent

Comment by Christoph Tertel on November 23, 2018 at 12:01am

Hello Vincent, is it possible to receive the grafic "Data Science Roles & How they interact" in a higher Resolution? I ask, because I can´t read the whole text. Thank you very much and king regards, Christoph

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