Summary: Now that we’ve detailed the four main AI-first strategies: Data Dominance, Vertical, Horizontal, and Systems of Intelligence, it’s time to pick. Here we provide side-by-side comparison and our opinion on the winner(s) for your own AI-first startup.
Added by William Vorhies on July 31, 2018 at 8:20am — No Comments
Social media has exploded. And it is rapidly becoming the choice of marketers who want to gain a large audience, spread their brands and develop relationships that will result in trust and ultimately sales.
But competition is fierce, and marketers who must craft content are finding it increasingly difficult to cover all of the social media platforms they want to. One of the solutions is to use AI-powered tools to save manpower and to improve efficiency.
Use of AI Tools…Continue
Added by Kristin Savage on July 31, 2018 at 1:00am — No Comments
Forrester published a report titled “The Sorry State of Digital Transformation in 2018” (love the brashness of the title) that found that 21% of 1,559 business and IT decision makers consider their digital transformations complete. Complete? Say what?!
The concept of “Digital Transformation” is confusing because many CIO’s (at least 21%) and their…Continue
This article was written by Bob Hayes
A recent survey of over 16,000 data professionals showed that the most common challenges to data science included dirty data (36%), lack of data science talent (30%) and lack of management support (27%). Also, data professionals reported experiencing around three challenges in…Continue
Added by Kelly Quintana on July 30, 2018 at 12:15pm — No Comments
Spark is a powerful tool which can be applied to solve many interesting problems. Some of them have been discussed in our previous posts. Today we will consider another important application, namely streaming. Streaming data is the data which continuously comes as small records from different sources. There are many use cases for streaming…Continue
Added by Igor Bobriakov on July 30, 2018 at 3:53am — No Comments
Shark-ML is an open-source machine learning library which offers a wide range of machine learning algorithms together with nice documentation, tutorials and samples. In this post I will show how to use this library for solving classification problem, with two different algorithms SVM and Random Forest. This post will tell you about how to use API for:
1. Loading data
2. Performing normalization and dimension…Continue
Added by Kyrylo Kolodiazhnyi on July 30, 2018 at 2:40am — 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.
Featured Resources and Technical ContributionsContinue
Added by Vincent Granville on July 29, 2018 at 3:30am — No Comments
Below is an R code for Cox & Stuart Test for Trend Analysis. Simply, copy and paste the code into R workspace and use it. Unlike cox.stuart.test in R package named "randtests", this version of the test does not return a p-value greater than one. This phenomenon occurs when the test statistic, T is half of the number of untied pairs, N.
Here is a simple example that reveals the situtaion:
 1 4 6 7 9 7 1 6
Added by Okan OYMAK on July 29, 2018 at 3:00am — No Comments
Target audience: Marketers, analysts, campaign managers, and decision makers.
Preface: I teach multiple tools under Adobe's experience cloud and I often get to have a look at the shape of digital marketing in multiple companies and across various business domains. This post is a summary of the most common problems and ways of resolving them at early stages before they become blunders.
1. The accuracy (and single…Continue
Added by Abhishek Srivastava on July 28, 2018 at 7:30pm — No Comments
I picked up a little book called “Finance Basics” published by Harvard Business Review Press, for a short in-flight reading. This tiny book isn't going to make someone a finance expert but I did find a few things useful for data scientists and business analysts whose background is not finance or economics. Data science is truly a multi-disciplinary area with people coming from many different background and areas of expertise, often with little to no exposure…Continue
Added by Mab Alam on July 28, 2018 at 10:07am — No Comments
Guest blog by Seth Dobrin and Daniel Hernandez.
Companies have been sold on the alchemy of data science. They have been promised transformative results. They modeled their expectations after their favorite digital-born companies. They have piled a ton of…Continue
These are a bulk of people pondering the same question and exploring different answers but at a hanging stage not knowing which one is correct and which one should they follow. The answers are very conflicting some share it on the basis of the research while some share their personal experience which confuses the newbies as hell. Well, the answer to this question depends on what you are trying to…Continue
Wondering how the words, fashion, weather and predictive analytics are connected?
Here’s a poser – what is one of the biggest challenges before the global fashion industry today? Weather. You wouldn’t have guessed it, right?
Pick up any fashion magazine, read any fashion portal, white paper…. you name it, unpredictable weather is on the Top-5…Continue
Added by Hemant Warudkar on July 27, 2018 at 4:13am — No Comments
With Beginning Data Science with Python and Jupyter, get to grips with the skills you need for entry-level Data Science. You'll learn about some of the most commonly…Continue
Added by Packt Publishing on July 26, 2018 at 11:33pm — No Comments
Guest blog by Yoel Zeldes.
This post describes:
In this post you will learn what the Gumbel-softmax trick is. Using this trick, you can sample from a discrete…Continue
Added by Vincent Granville on July 26, 2018 at 10:00am — No Comments
Thus, data has become of great importance for those willing to take profitable decisions concerning business. Moreover, a…Continue
Added by Igor Bobriakov on July 26, 2018 at 8:00am — No Comments
Insights and Advice from Data Science Leaders and Key Influencers. Paperback – July 13, 2018. By Matt Corey.
The Data Scientist’s Book of Quotes includes over 300 insightful and inspiring quotes from the world’s leading Data Science thought leaders and key influencers across the world, including Andrew Ng, Bernard Marr, Vincent Granville, Carla Gentry, Cathy O’Neil and Hilary Mason. The Data Scientist role is one of the most pivotal and disruptive roles in today’s global…Continue
Autonomous driving is the way to go forward.
Here is my article based on some of the work we have done in this field.
- Python-tensorflow based deep learning model for object classification trained on a novel data-set
- Trained and deployed on an embedded computing platform for real-time object detection…Continue
Added by Rajshekhar Mukherjee on July 25, 2018 at 3:00pm — No Comments
The vast possibilities of artificial intelligence are of increasing interest in the field of modern information technologies. One of its most promising and evolving directions is machine learning (ML), which becomes the essential part in various aspects of our life. ML has found successful applications in Natural Languages Processing, Face…Continue
Added by Igor Bobriakov on July 24, 2018 at 10:12pm — No Comments
Bill is the Editorial Director for Data Science Central, and President and Chief Data Scientist at Data-Magnum, providing predictive analytics and big data infrastructure projects as a service. Bill has been an active commercial predictive modeler since 2001.
In this series consisting of six…Continue
Added by Vincent Granville on July 24, 2018 at 7:00pm — No Comments