In a meeting with engineering leadership, I was told, "We'll tack on the AI later."
While doing ethnographic testing with a β customer, I was asked if the AI would just learn and then do everything perfectly.
Summary: The annual Burtch Works salary survey tells us a lot about which industries are using the most data scientists and the difference between higher and lower skilled data scientists. Salary increases show us whether demand is increasing, and finally we take a shot at determining which skills are most in demand.
Added by William Vorhies on July 1, 2019 at 8:00am — No Comments
By Ajit Jaokar and Dan Howarth. With contributions from Ayse Mutlu.
Exclusively for Data Science Central members, with free access. You can download this book (PDF) here.
This tutorial began as a series of weekend workshops created by Ajit Jaokar and Dan Howarth. The idea was to work with a specific (longish) program such that we explore as much of it…Continue
Summary: Can all AI strategies be defined by a few common needs or are the different AI strategy models sufficiently unique that they need to be considered as separate approaches.
Added by William Vorhies on February 3, 2020 at 11:15am — No Comments
How to build a project from scratch by using a latest, state-of-the-art AI technology stack?
After spending a few years working on data science - enriched products, we tried more than 10 different workflow concepts - adopted scrum & agile, waterfall, iterative kanban canvas etc.
As a result, we've drafted our own data science project development workflow that allows us to iterate over data science hypotheses, process and acquire data, build…Continue
Added by Max Frolov on January 19, 2020 at 3:00am — No Comments
Added by Mark Cramer on January 23, 2020 at 12:30pm — No Comments
Monday newsletter published by Data Science Central. Previous editions can be found here. The contribution flagged with a + is our selection for the picture of the week. To subscribe, follow this link. …Continue
Added by Vincent Granville on February 2, 2020 at 9:30am — No Comments
I often use this quote from Isaac Newton in my teaching.
AI is a vast and a complex subject. No matter how much you know - you realise that there is really a vast amount more to learn. So, my way of learning a subject as complex and dynamic as AI, is to share my insights. This helps me to refine my own thinking.
I also follow…Continue
Added by ajit jaokar on January 26, 2020 at 12:30pm — No Comments
Summary: Workforce forecasting and scheduling applications are rapidly upgrading their use of AI. Techniques of time series forecasting ranging from the simple Holt Winters to the complex, DNNs and Multiple Temporal Aggregation are available on some but not all platforms. Increasingly, AI differentiates the usefulness of these apps.
Added by William Vorhies on January 28, 2020 at 2:15pm — No Comments
Here are two great resources for machine learning and AI practitioners. Other recent free books can be found here.
1. Free and Open Machine Learning Documentation
This book is all about applying machine learning solutions for real practical use cases. This means the core focus is on outlining how to use machine learning…Continue
Added by Capri Granville on February 2, 2020 at 6:30am — No Comments
You can think what you want about Artificial Intelligence and Machine Learning, but one thing you can't deny that it's wider adoption is just around the corner. The prediction of customer behavior was always a tricky task for marketers all over the world. But now, with AI/ML innovations that get on the…Continue
Added by Roman Chuprina on January 16, 2020 at 12:00pm — No Comments
This article was written by John Mount.
In this note we will show how to speed up work in R by partitioning data and process-level parallelization. We will show the technique with three different R packages: rqdatatable, data.table,…Continue
Added by Andrea Manero-Bastin on August 6, 2019 at 5:00am — No Comments
This article was written by Stacey Ronaghan.
This post attempts to consolidate information on tree algorithms and their implementations in…Continue
Added by Andrea Manero-Bastin on July 4, 2019 at 5:00am — No Comments
Exploratory Data Analysis or EDA is that stage of Data Handling where the Data is intensely studied and the myriad limits are explored. EDA literally helps to unfold the mystery behind such data which might not make sense at first glance. However, with detailed analysis, we can use the same data to provide miraculous results which can help boost large scale business decisions with excellent accuracy. This not only helps business conglomerations to evade likely pitfalls in the future but also…Continue
Added by Divya Singh on July 4, 2019 at 7:30pm — No Comments
We begin the month of February of this special year 2020 with an analysis of the trends that gurus worldwide have made about what has to come to us in this year related to Artificial Intelligence and Data Science. For this I have made a infographic to summarize all these trends and I leave you different links with more information. I hope they are of your interest and we can continue expanding these trends with your contributions in the comments of the post!…Continue
Added by Noelia Gonzalez Rodriguez on February 2, 2020 at 2:30am — No Comments
Blockchain technology has taken the IT world by storm. It has the potential to change the digital world completely, including the way businesses operate and make transactions. Initially, it was used to handle cryptocurrencies such as Bitcoin. Today, numerous business applications across domains use Blockchain as their central component.
However, Blockchain is still at an evolving stage, and its adoption is slow in most industries. Toward this, a programming language like Java can help…Continue
Added by Ryan Williamson on January 31, 2020 at 12:30am — No Comments
Fifty years, ago, the lines between "data analysis" and "statistical analysis" were pretty clear. But as data analysis evolved, those lines became blurred. The differences between the two terms are now very much a grey area, but there are still a few notable differences.
Data scientists and statisticians typically define "data analysis" in different ways.
Added by Stephanie Glen on January 31, 2020 at 3:30am — No Comments
Machine learning (ML) is a hot topic nowadays. Everyone speaks about the new programming paradigm, models are implemented in very different domains, more and more startups are relying mainly on ML.
At the same time, machine…Continue
Added by Igor Bobriakov on January 28, 2020 at 8:12am — No Comments
The Economic Value Curve is a measure of the relationship between a dependent variable and independent variables to achieve a particular outcome such as retain customers, increase operational uptime, or optimize inventory. The Economic Value Curve measures the impact that increasing or decreasing one of the independent variables has on the dependent variable. In Figure 1, for example, if we want to improve the dependent variable “Uptime %” then we need to spend more on the independent…Continue
Added by Bill Schmarzo on January 30, 2020 at 11:30am — No Comments
When it comes to dealing with development and software, companies primarily have quite similar goals. All of them want the code to be consistent and self-documenting among other things. However, when Angular enters the picture, and it is when the scenario becomes exceptionally challenging. It can be associated with the fact that prominent the organization, more significant will be the number of developers they have working on various apps. For a company, that means they must spell out as…Continue
Added by Ryan Williamson on January 30, 2020 at 1:30am — No Comments
If you ever have some moments…Continue
Added by Vincenzo Parrilla on January 27, 2020 at 11:10am — No Comments