Posted by Jianhua Li on GitHub. This was proposed as a data science project on Data Science Central, to challenge your data science skills on a real data set. Below is an overview.
Basically one should try to answer the following three questions:
This article was posted by Ryan Swanstrom on Data Science 101. Ryan is helping the world learn data science at Microsoft.
The differences between Data Scientists, Data Engineers, and Software engineers can get a little confusing at times. Thus, here is a guest post provided by Jake Stein, CEO at Stitch formerly RJ Metrics, which aims to clear up some of that confusion based upon LinkedIn data.
As data grows, so does the expertise needed to manage it. The past few years…Continue
Added by Emmanuelle Rieuf on November 28, 2016 at 6:00pm — No Comments
Guest blog by Chris Rigatuso. Chris is Founder and Board Member at the Skyfollow Consulting Group. He earned his MBA from the Haas School of Business (UC Berkeley), and lives in the Bay Area.
Your CRM data, is it the stairway to Heaven or…Continue
Added by Emmanuelle Rieuf on November 28, 2016 at 5:30pm — No Comments
This article was written by Michal Tadeusiak.
Underground mining poses a number of threats including fires, methane outbreaks or seismic tremors and bumps. An automatic system for…Continue
Added by Emmanuelle Rieuf on November 23, 2016 at 7:00pm — No Comments
Guest blog post by Arpit Saxena, Apprentice Leader at Mu Sigma Inc.
In the stormy June of 1944, D-Day - the date of the Normandy Landings was looming. The allied forces backed up by tanks and military were painstakingly assembled and prepped in the lead-up to the invasion.
The supreme commander of the Allied Forces had to ensure the success of the D-Day, originally scheduled for June 5.…Continue
Added by Emmanuelle Rieuf on November 21, 2016 at 8:30am — No Comments
Added by Emmanuelle Rieuf on November 18, 2016 at 12:30pm — No Comments
This article comes from Togaware.
A Survival Guide to Data Science with R
These draft chapters weave together a collection of tools for the data scientist—tools that are all part of the R Statistical Software Suite.
Added by Emmanuelle Rieuf on November 15, 2016 at 1:00pm — No Comments
We may be years away from the “AI-enabled Coworker,” but the first implementations of machine-learning capabilities are finding their way into the everyday data-analysis tools used by businesses of all types. Cognitive assistance promises to reshape business processes, but only if app development and deployment tools are adapted to support machine learning.
While it has become fashionable to hypeAIas the next game-changing technology promising to have an impact greater than either…Continue
Added by Emmanuelle Rieuf on November 15, 2016 at 11:00am — No Comments
This article comes from Data Revelations, a training, consulting, and development practice devoted to helping organizations understand, analyze, and share their data.
Here is a consolidation of the various blog posts Data Revelations has written on visualizing survey data:
Added by Emmanuelle Rieuf on November 15, 2016 at 8:00am — No Comments
White House report on embedding civil rights principles into algorithms.
Table of Contents:
Added by Emmanuelle Rieuf on November 14, 2016 at 8:30am — No Comments
This article was posted by Patricia Hall and Varghese George on Amstatnews.
The ASA contacted the Statistical Consulting and Survey Center in the Augusta University Department of Biostatistics to help design and analyze the data for a survey of the association’s nonacademic members in the United States employed by business, industry, or government. Members were asked to report their annual base salary (in…Continue
Added by Emmanuelle Rieuf on November 11, 2016 at 6:00pm — No Comments
This article on data visualization tools was written by Jessica Davis. She's passionate about the practical use of business intelligence, predictive analytics, and big data for smarter business and a better world.
Data visualizations can help business users understand analytics insights and actually see the reasons why certain recommendations make the most sense. Traditional business intelligence and analytics vendors, as well as newer market entrants, are offering data…Continue
Added by Emmanuelle Rieuf on November 10, 2016 at 9:00pm — No Comments
This article contains phrases taken from the machine learning and analysis world. Data scientists and algorithm engineers will feel more comfortable with reading it although it’s targeted at anyone who is interested in some deep data science learnings. It was written by Ella Gati. Ella is fascinated by machine learning and data science and is excited to be making big data valuable.
Hacking applications such as …Continue
Added by Emmanuelle Rieuf on November 10, 2016 at 4:30pm — No Comments
This article was written by
Watching the appeal and applications of machine intelligence expand:
Almost a year ago, we published our now-annual …Continue
Added by Emmanuelle Rieuf on November 9, 2016 at 5:30pm — No Comments
The Python Data Science Handbook provides a reference to the breadth of computational and statistical methods that are central to data-intensive science, research, and discovery. People with a programming background who want to use Python effectively for data science tasks will learn how to face a variety of problems: e.g., how can I read this data format into my…Continue
Added by Emmanuelle Rieuf on November 9, 2016 at 8:30am — No Comments
This article on a complete tutorial on data exploration, was posted by Sunil Ray. Sunil is a Business Analytics and Intelligence professional with deep experience in the Indian Insurance industry.
There are no shortcuts for data exploration. If you are in a state of mind, that machine learning can sail you away from every data storm, trust me, it won’t. After some point of time, you’ll realize that you are struggling at improving model’s…Continue
Added by Emmanuelle Rieuf on November 8, 2016 at 9:00am — No Comments
Do you want to learn the history of data visualization? Or do you want to learn how to create more engaging visualizations and see some examples? It’s easy to feel overwhelmed with the amount of information available today, which is why sometimes the answer can be as simple as picking up a good book.
This article was posted by Saurav Kaushik. Saurav is a Data Science enthusiast, currently in the final year of his graduation at MAIT, New Delhi. He loves to use machine learning and analytics to solve complex data problems.
Have you come across a situation when a Chief Marketing Officer of a company tells you – “Help me understand our customers better so that we can market our products to them in a better manner!”
I did and the analyst in…Continue
Added by Emmanuelle Rieuf on November 7, 2016 at 7:00pm — No Comments
This article was written by Sebastian Ruder. Sebastian is a PhD student in Natural Language Processing and a research scientist at AYLIEN. He blogs about Machine Learning, Deep Learning, NLP, and startups.
Gradient descent is one of the most popular algorithms to perform optimization and by far the most common way to optimize neural networks. At the same time, every state-of-the-art Deep Learning library contains implementations of various algorithms to optimize gradient…Continue
Added by Emmanuelle Rieuf on November 7, 2016 at 6:30pm — No Comments
This article was written by Ryan Grim. Ryan is the Washington bureau chief for The Huffington Post and an MSNBC contributor. He is a former staff reporter with Politico.com and Washington City Paper, as well as the author of the book "This Is Your Country on Drugs". Nate Silver is a famous statistician, founder and editor in chief of FiveThirtyEight. Some of the arguments in this article are about the type of regression being used.…Continue