Sustaining industry averages and benchmarks are the antithesis of innovation and a great way to ensure average performance. Doing whatever everyone else is doing is a “paving the cow path" management mentality, lacking aspirational goals which are critical for organizations to fuel innovation and create customer and market differentiation. Which brings me to why I teach.
As my students and I work through their “Thinking Like a Data Scientist” exercises together, I always learn…Continue
Added by Bill Schmarzo on November 30, 2019 at 12:00pm — No Comments
This article was written by Montana Low.
An open source framework for configuring, building, deploying and maintaining deep learning models in Python.
As Instacart has grown, we’ve learned a few things the hard way. We’re open sourcing Lore, a framework to make machine learning approachable for Engineers and maintainable for Machine Learning Researchers.…Continue
Added by Andrea Manero-Bastin on November 30, 2019 at 9:00am — No Comments
This article was written by Mohammad Sajid.
Statistical cluster analysis is an Exploratory Data Analysis Technique which groups heterogeneous objects(M.D.) into homogeneous groups. We will learn the basics of cluster analysis with mathematical way.
Cluster Analysis can be done by two…
Added by Andrea Manero-Bastin on November 30, 2019 at 8:30am — No Comments
At the time of writing, I'm a 52 year-old working in the fields of mathematics and data science. In mathematics, that makes me well-seasoned (and probably well-tenured, if I had chosen to continue in academia). In data science, some would consider me a dinosaur. In fact, many older people considering a career in data science might be put off by the thought that data science is tough to break into at a later age. But is that statement true? Should the over 50 crowd put down their textbooks…Continue
Here is our selection of featured articles and technical resources posted since Monday. There is a lot of very interesting material in this edition.
Added by Vincent Granville on November 29, 2019 at 9:00am — No Comments
Technology decision-makers are (and also should keep) seeking methods to successfully carry out artificial intelligence innovations into their businesses and, therefore, drive value. And though all AI innovations most definitely have their own merits, not all of them deserve purchasing, with each passing day we come across a number of AI development techniques.
If something and also only one thing occurs after you read this write-up, we hope it is that you are…Continue
Added by Allen Adams on November 29, 2019 at 4:30am — No Comments
We study the properties of a typical chaotic system to derive general insights that apply to a large class of unusual statistical distributions. The purpose is to create a unified theory of these systems. These systems can be deterministic or random, yet due to their gentle chaotic nature, they exhibit the same behavior in both cases. They lead to new models with numerous applications in Fintech, cryptography, simulation and benchmarking tests of statistical hypotheses. They are also related…Continue
Added by Vincent Granville on November 28, 2019 at 11:30pm — No Comments
Over the last few years, Excel has been redesigned from the ground up. Currently, Microsoft is making the new Excel core-features available to every user, regardless of your Office 365 license. Thanks to the Microsoft naming conventions, it is easy to confuse the new features with existing ones. That being said, Power Query and Power Pivot are not the same things as Pivot Tables, which you have likely been using for years.…Continue
Added by Rafael Knuth on November 28, 2019 at 6:10am — No Comments
The standard definition of a generalized Gaussian distribution can be found here. In this article, we explore a different type of generalized univariate normal distributions that satisfies useful statistical properties, with interesting applications. This new class of distributions is defined by its characteristic function, and applications are discussed in the last section. These…Continue
Added by Vincent Granville on November 27, 2019 at 8:30pm — No Comments
A successful business requires new approaches to data management in this age. Modern advances in data science area provide an efficient solutions for numerous use cases.
Data science embraces a broad spectrum of tasks in the sphere of…Continue
Added by Igor Bobriakov on November 27, 2019 at 6:27am — No Comments
In my prior blog post, I wrote of a clever elf that could predict the outcome of a mathematically fair process roughly ninety percent of the time. Actually, it is ninety-three percent of the time and why it is ninety-three percent instead of ninety percent is also important.
The purpose of the prior blog post was to illustrate the weakness of using the minimum variance unbiased estimator (MVUE) in applied finance. Nonetheless, that begs a more general question of when and why it…Continue
Added by David Harris on November 26, 2019 at 1:44pm — No Comments
When ever we visit a client and present our proposal, we start wondering if it will be accepted or rejected by the customer. Usually, our customer will analyze our proposal, compare it with other competitors’ and make a decision.
In order to build our commercial forecast system, we need to assign a probability to every proposal we have presented and assign a numerical value to every one of them.
One way of doing this is multiplying the value of the proposal by the probability of…
Added by Pablo Gutierrez on November 26, 2019 at 3:05am — No Comments
Added by Sameer Nigam on November 25, 2019 at 11:30pm — No Comments
The standard error is really just a type of standard deviation. For this simple example, I've used three samples as an illustration of how the standard deviation and standard error differ as they relate to…Continue
Added by Stephanie Glen on November 25, 2019 at 1:21pm — No Comments
Summary: Too many solutions. We are at an inflection point where too many vendors are offering too many solutions for moving our AI/ML models to production. The very real risk is duplication of effort, fragmentation of our data science resources, and incurring unintended new technical debt as we bind ourselves to platforms that have hidden assumptions or limitations in how that approach problems.
Added by William Vorhies on November 25, 2019 at 9:44am — No Comments
(I will give you a hint. It’s in the name.)
This post is intended as a response to an interesting discussion on…Continue
Added by Lucas Finco on November 25, 2019 at 8:30am — No Comments
Clouds for doing quantum computing are becoming increasingly popular. Here is a list with links of those quantum clouds that already exist or are imminent. All are commercial but usually free for small jobs and open to the public. Most use open source q c software but some don't and have opted to keep their software proprietary. In Alphabetical Order. ✅…Continue
Added by Robert R. Tucci on November 25, 2019 at 7:30am — No Comments
Data Scientists help find insights about the market and help make products better. They are responsible for analyzing and handling a massive amount of structured and unstructured data and require various tools to do so. Some of the tools used by Data Scientists to carry out their data operations are mentioned below.
Designed for statistical operations, SAS is an open source proprietary software that is used to…
Added by Simran Agarwal on November 25, 2019 at 3:30am — No Comments
If there’s one thing that’s common to all businesses across all industries in the world, it’s that the customer is always the primary focus. While this holds for all sectors and companies as mentioned above, the fact remains that it can be somewhat more relevant to certain types of businesses. Take the manufacturing industry, for example. So, as more and more manufacturing companies seek to adopt an increasingly customer-focused path forward, they realize they need for modern solutions and…Continue
Added by Ryan Williamson on November 24, 2019 at 10:00pm — No Comments
I was formally (1998-2016) a Senior Research Fellow in the Institute of Educational Technology (IET) at the Open University (OU) in the UK. It was in that context that I first started thinking about the potential of Learning Analytics in my field which is Accessibility of eLearning and Disabled Student Support. Looking back through my work-related blog (…Continue
Added by Martyn Cooper on November 24, 2019 at 4:30pm — No Comments