Author Archives: marianacendon2013

Data Science vs Engineering: Tension Points

This blog post provides highlights and a full written transcript from the panel, “Data Science Versus Engineering: Does It Really Have To Be This Way?” wit

Source: Data Science vs Engineering: Tension Points

Self-Education Is Our Best Bet in the Fight Against the Panopticon

Regulation is slow and Big Tech has its own interests, so it’s up to us to understand what we opt in to

Source: Self-Education Is Our Best Bet in the Fight Against the Panopticon

The best Mario Kart character according to data science

The question for an aspiring Mario Kart champion nowadays is “How can I pick a character / kart / tire combination that is in some sense optimal, even if there isn’t one ‘best’ option?”

 

By Henry Hinnefeld

Source: The best Mario Kart character according to data science

Excellent Code = Clean and Beautiful Code – The Startup – Medium

Programs must be written for people to read, and only incidentally for machines to execute.

Source: Excellent Code = Clean and Beautiful Code – The Startup – Medium

Essential Cheat Sheets for Machine Learning and Deep Learning Engineers

Machine learning is complex. For newbies, starting to learn machine learning can be painful if they don’t have right resources to learn…

Source: Essential Cheat Sheets for Machine Learning and Deep Learning Engineers

An End-to-End Project on Time Series Analysis and Forecasting with Python

Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics…

Source: An End-to-End Project on Time Series Analysis and Forecasting with Python

Why Data Is Never Raw – The New Atlantis

On the seductive myth of information free of human judgment

Source: Why Data Is Never Raw – The New Atlantis

At Netflix, Who Wins When It’s Hollywood vs. the Algorithm? – WSJ

As the video-streaming company plunges deeper into original production, its Los Angeles wing is doing the once-unthinkable: overriding the metrics.

Source: At Netflix, Who Wins When It’s Hollywood vs. the Algorithm? – WSJ

Skin Color and Social Mobility: Evidence From Mexico | SpringerLink

In many Latin American countries, census data on race and skin color are scarce or nonexistent. In this study, we contribute to understanding how skin color affects intergenerational social mobility in Mexico. Using a novel data set, we provide evidence of profound social stratification by skin color, even after controlling for specific individual characteristics that previous work has not been able to include, such as individual cognitive and noncognitive abilities, parental education and wealth, and measures of stress and parenting style in the home of origin. Results indicate that people in the lightest skin color category have an average of 1.4 additional years of schooling and 53 % more in hourly earnings than their darkest-skinned counterparts. Social mobility is also related to skin color. Individuals in the darkest category are 20 percentile ranks lower in the current wealth distribution than those in the lightest category, conditional on parental wealth. In addition, results of a quantile regression indicate that the darkest group shows higher downward mobility.

Source: Skin Color and Social Mobility: Evidence From Mexico | SpringerLink