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
Guest blog by Christopher Fernandes, Business Architecture / Strategy Director.
A decade ago straight through processing was a buzz word and speed to market was critical. The progress financial institutions have made in moving almost all aspects of their transaction foot print digital has left little to leverage on the transaction side.
In today’s day and time while most…Continue
Added by Vincent Granville on November 11, 2016 at 4:00pm — 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
My name is John Blankinship, and I am offering a neural network predictive modeling service. I have developed a set of C-language programs for the training, validation and delivery of Multilayer Perceptron neural network models. Given a training set of observations, I am reasonably confident that I can build a neural network predictive model that will outperform an existing model with respect to independent validation data. If I cannot develop a superior predictive model, then there is no…Continue
Added by John J. Blankinship on November 10, 2016 at 9:30am — No Comments
There is growing demand of data scientists in every organization. For growth of any business enterprise there is need to evaluate data in order to streamline the strategies and to keep a record. The skills of a data scientist boils down to the tools that they are able to use and are aware of. In this article we will talk of both the programming data science tools as well as ones which do not require much of coding to get started with.…Continue
Added by Joydeep Bhattacharya on November 10, 2016 at 6:54am — 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
Here is our new selection of featured articles and resources. Starred articles have interesting charts. The picture below is from the last article. Topics cover a salary survey, several programming languages with comparisons, a mathematical optimization technique widely used in machine learning algorithms (this article has great animated gifs that illustrate the convergence of various methods) and a methodology to make correct election forecasts.…Continue
Added by Vincent Granville on November 9, 2016 at 4:52pm — No Comments
We are living in the era of technological transformation that is bringing about changes in the way we take decisions. As big data is becoming pervasive across all the industries, use of machines to find patterns and predict future is gaining a lot of prominence in the…Continue
Added by Ashish Sukhadeve on November 9, 2016 at 10:30am — 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
In this post, I show a network analysis of the R and Python ecosystems in terms of their competitors. The objective of the post is to -
To identify the typical substitutes/ competitors of a tool, I use the Google search autofill recommendations.…Continue
Added by Archisman Majumdar on November 9, 2016 at 1:30am — No Comments
Psst! Want to know a secret?
Engineers are no longer taking home a five figure salary. Instead, they are now expanding their reach to earn six figure incomes.
Wondering how they do it?
Well, for starters, they are well read and know what's trending in the industry. If there is a hiring pattern in the industry, they realize the potential of getting in it and exploring it further.
But, how can you replicate the same for your career?
Data Science can't be…Continue
This article is contributed by Atiq Rehman.
What if you’re not an Excel expert? This is your guide. It was initially intended for SEO people, though many will find it useful.
Added by Shay Pal on November 8, 2016 at 9:30am — 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.
Summary: Next time you bring up Artificial Intelligence and your non-data scientist friends all say “Watson” here’s some perspective you can offer. Their understanding of AI and Watson is very likely to be inaccurate. Here’s what you need to know to set them straight.
When conversation with my non-data scientist friends turns to AI it’s almost inevitable that at least one will remark on the wonders of Watson. To many of the uninformed, Watson is…Continue
Added by William Vorhies on November 8, 2016 at 7:30am — No Comments
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
The shortage of data scientists is a hindrance to the widespread adoption of analytics across many industries.
At the same time, the preponderance of multiple tools, techniques and knowledge base along with the rapidly changing data science landscape make it difficult for analytics leaders to hire the right talent for their data science team. The attrition rate in this industry is also high due to which employers end up spending a lot of time and money to hire a data…Continue
Added by Aatash Shah on November 7, 2016 at 4:30am — 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