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.

In our last several articles we’ve taken a tour of the four major…

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…

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…

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…

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 technology…

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…

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 Contributions

• New book:…

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:

> x

[1] 1 4 6 7 9 7 1 6

> cox.stuart.test(x)

Cox Stuart…

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…

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…

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…

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…

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…

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…

Guest blog by Yoel Zeldes.

This post describes:

• what the Gumbel distribution is
• how it is used for sampling from a discrete distribution
• how the weights that affect the distribution's parameters can be trained
• how to use all of that in a toy example (with code)

In this post you will learn what the Gumbel-softmax trick is. Using this trick, you can sample from a discrete…

Nowadays data proves to be a powerful pushing force of the industry. Big companies representing diverse trade spheres seek to make use of the beneficial value of the data. 

Thus, data has become of great importance for those willing to take profitable decisions concerning business. Moreover, a…

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…

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…

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…

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…