BigDataFr recommends: Big Data, Data Science, and Civil Rights […] Advances in data analytics bring with them civil rights implications. Data-driven and algorithmic decision making increasingly determine how businesses target advertisements to consumers, how police departments monitor individuals or groups, how banks decide who gets a loan and who does not, how employers hire, how […]
Documentation
[Analyticbridge] BigDataFr recommends: The Most Important Lessons Learned from Data Science Projects
BigDataFr recommends: The Most Important Lessons Learned from Data Science Projects […] An overwhelming expansion of data archives posed a challenge to various industries, as these are now struggling to make use of such enormous amount of information. Almost 90% of all data ever recorded worldwide has been created in the last decade alone. This […]
[Datasciencecentral] BigDataFr recommends: Analytic Profiles: Key to Data Monetization
BigDataFr recommends: Analytic Profiles: Key to Data Monetization […]Many organizations are associating data monetization with selling their data. But selling data is not a trivial task, especially for organizations whose primary business relies on its data. Organizations new to selling data need to be concerned with privacy and Personally Identifiable Information (PII), data quality and […]
[arXiv] BigDataFr recommends: Forecasting in the light of Big Data
BigDataFr recommends: When Will AI Exceed Human Performance? Evidence from AI Experts […] Predicting the future state of a system has always been a natural motivation for science and practical applications. Such a topic, beyond its obvious technical and societal relevance, is also interesting from a conceptual point of view. This owes to the fact […]
[Datasciencecentral] BigDataFr recommends: The essence of machine learning is function estimation
BigDataFr recommends: The essence of machine learning is function estimation […]Machine learning is cool. There is no denying in that. In this post we will try to make it a little uncool, well it will still be cool but you may start looking at it differently. Machine learning is not a black box. It is […]
[arXiv] BigDataFr recommends: When Will AI Exceed Human Performance? Evidence from AI Experts
BigDataFr recommends: When Will AI Exceed Human Performance? Evidence from AI Experts […] Advances in artificial intelligence (AI) will transform modern life by reshaping transportation, health, science, finance, and the military. To adapt public policy, we need to better anticipate these advances. Here we report the results from a large survey of machine learning researchers […]
[ArXiv] BigDataFr recommends: Data Visualization on Day One: Bringing Big Ideas into Intro Stats Early and Often
BigDataFr recommends: A Proposed Architecture for Big Data Driven Supply Chain Analytics […] In a world awash with data, the ability to think and compute with data has become an important skill for students in many fields. For that reason, inclusion of some level of statistical computing in many introductory-level courses has grown more common […]
[Datasciencecentral] BigDataFr recommends: Why R is Bad for You
BigDataFr recommends: Why R is Bad for You […] Someone had to say it. I know this will be controversial and I welcome your comments but in my opinion R is not the best way to learn data science and not the best way to practice it either. Why Should We Care What Language You […]
[ArXiv] BigDataFr recommends: A Proposed Architecture for Big Data Driven Supply Chain Analytics
BigDataFr recommends: A Proposed Architecture for Big Data Driven Supply Chain Analytics […] Advancement in information and communication technology (ICT) has given rise to explosion of data in every field of operations. Working with the enormous volume of data (or Big Data, as it is popularly known as) for extraction of useful information to support […]
[ArXiv] BigDataFr recommends: Big Data Analysis Using Shrinkage Strategies
BigDataFr recommends: Big Data Analysis Using Shrinkage Strategies […] In this paper, we apply shrinkage strategies to estimate regression coefficients efficiently for the high-dimensional multiple regression model, where the number of samples is smaller than the number of predictors. We assume in the sparse linear model some of the predictors have very weak influence on […]