I was comparing home prices in San Francisco between 1994 and 2018, and I noticed that it has increased by a factor 4 over 25 years. In the meanwhile, the inflation index increased by a factor 1.7 (see here.) I am not saying here that my sources are correct or wrong -- entire books have been written on the subject -- but instead, my purpose here is to show how some visualizations can be…

Here is one of the most viral videos about data science posted in the last few months, with over 500,000 views. I could not locate the original copy; I found it in a re-tweet by Kirk Borne. See link to the video below the picture. However, I was able to find who created it (Welcome.ai) and it turns out that they have a YouTube channel with plenty of even more interesting videos. To search these videos by keyword, click…

Below is an extract of a 10-page cheat sheet about data science, compiled by Maverick Lin. This cheatsheet is currently a reference in data science that covers basic concepts in probability, statistics, statistical learning, machine learning, deep learning, big data frameworks and SQL. The cheatsheet is loosely based off of The Data Science Design Manual by Steven S. Skiena and An Introduction to Statistical Learning by…

This article is a solid introduction to statistical testing, for beginners, as well as a reference for practitioners. It includes numerous examples as well as illustrations and definitions for concepts such as rejecting the null hypothesis, one sample hypothesis testing, P-values, critical values, and Bayesian hypothesis testing. It has references to additional topics, such as 

• What is Ad Hoc Testing?
• What is a Rejection Region?
• What is a Two Tailed…

This infographic was produced by 365DataScience. Last year they completed a research on 1,001 data scientists to get a profile of the ‘typical’ data scientist in 2018. They replicated the study with new data. Below are the findings.

Here are some of our key findings:…

Below is an extract of a 10-page cheat sheet about probability, compiled by William Chen (http://wzchen.com) and Joe Blitzstein, with contributions from Sebastian Chiu, Yuan Jiang, Yuqi Hou, and Jessy Hwang. Material based on Joe Blitzstein’s Harvard's introductory probability course (@stat110 - (http://stat110.net) and Blitzstein / Hwang’s Introduction to Probability textbook (…

Interesting picture comparing linear, logistic and Poisson regression. For more about regression, read our other articles on this subject, here. For other ML concepts summarized in one picture, follow this link. …

Guest blog by Igor Bobriakov.

First days after celebration of the New Year is the time when looking back we can analyze our actions, promises and draw conclusions whether our predictions and expectations came true. As 2018 came to its end, it is perfect time to analyze it and to set trends for the next year.…

Imagine it's 1994 and the dawn of the internet. In many ways, it is. Entrepreneurs are once again laying the rails for a new digital world. And, just like the first digital revolution, this one will again transform the way we live, work and play. The technology known as blockchain is poised to disrupt entrenched industries and shatter today's business models. With so much at stake, how do you prepare?…

For one- or two-semester business statistics courses. Not a new book, but a popular one (8th edition.)

This text is the gold standard for learning how to use Excel in business statistics, helping students gain the understanding they need to be successful in their careers. The authors present statistics in the context of specific business fields; full chapters on business analytics further prepare students for success in their professions. Current data throughout the text…

Independently published (November 20, 2018). 78 pages.

This book intends to provide an overview of Machine Learning and its algorithms & models with help of R software. Machine learning forms the basis for Artificial Intelligence which will play a crucial role in day to day life of human beings in the near future. A basic understanding of machine learning is required, as its application is widely seen in different fields such as banks and financial sectors,…

New book, in progress. By Andriy Burkov, Machine Learning Team Leader at Gartner.

The following chapters are currently available:

Foreword

Chapter 1: Introduction

Part I: Supervised Learning

Chapter 2: Notation and Definitions

Chapter 3: Fundamental Algorithms

Chapter 4: Anatomy of a Learning Algorithm

Chapter 5: Basic Practice

Chapter 6: Neural Networks and Deep…

Pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis…

This book, initially written for chemical engineers, is actually very interesting for data scientists and machine learning engineers alike. For more free books, visit this page.…

Online Statistics Education: A Multimedia Course of Study.  Project Leader: David M. Lane, Rice University.

Content:

• Introduction
• Graphing Distributions
• Summarizing Distributions
• Describing Bivariate Data
• Probability
• Research Design
• Normal Distributions
• Advanced Graphs
• Sampling…

These pictures were posted on Quora by Oleg Sergeykin, former Structural Analysis Engineer at Boeing. His philosophy is that Data science is actually an iterative processes. It is never possible to complete a DS project in a single pass. A data scientist constantly tries new ideas and changes steps of his pipeline.…

Many are free. They are available online. They are offered by Princeton, Georgia Tech, Harvard, Columbia, Stanford, and Penn State. 

• Neural Networks and Deel Learning - Andrew Ng via Coursera
• Machine Learning - Georgia Institute of Technology via Udacity…

This is the new book by Andrew Ng, still in progress. Andrew Yan-Tak Ng is a computer scientist and entrepreneur. He is one of the most influential minds in Artificial Intelligence and Deep Learning. Ng founded and led Google Brain and was a former VP & Chief Scientist at Baidu, building the company's Artificial Intelligence Group into several thousand people. He is an adjunct professor (formerly associate professor and Director of the AI Lab) at Stanford University. Ng is also an early…

The Explainable Machine Learning Challenge is a collaboration between Google, FICO and academics at Berkeley, Oxford, Imperial, UC Irvine and MIT, to generate new research in the area of algorithmic explainability. Teams will be challenged to create machine learning models with both high accuracy and explainability; they will use a real-world financial dataset provided by FICO. Designers and end users of machine learning algorithms will both benefit from more interpretable and…

The Practical Guide to Storing, Managing and Analyzing Big and Small Data -- Cambridge University Press.

This comprehensive textbook teaches the fundamentals of database design, modeling, systems, data storage, and the evolving world of data warehousing, governance and more. Written by experienced educators and experts in big data, analytics, data quality, and data integration, it provides an up-to-date approach to database management. This full-color, illustrated text has a…