Sign up for our newsletter and get the latest big data news and analysis.

Anaconda Enterprise 5 Introduces Secure Collaboration to Amplify the Impact of Enterprise Data Scientists

Anaconda, the Python data science leader, introduced Anaconda Enterprise 5 software to help organizations respond to customers and stakeholders faster, deliver strategic insight for rapid decision-making and take advantage of cutting edge machine learning.

AI Enables Banks to Identify and Prevent Money Laundering While Surpassing Regulatory Demands

In this special guest feature, David McLaughlin, CEO and Founder of QuantaVerse, discusses how advancements in data science, including artificial intelligence (AI), machine learning and big data, promise to stifle money laundering and change outcomes for victims around the globe. Financial institutions have begun working smarter through the use of AI and machine learning to help banks dramatically improve the efficiency and effectiveness of money laundering investigations.

From the Editor’s Bookshelf: My Favorite Titles for Data Science and Machine Learning

As a practicing data scientist, I’ve spent years building up my library of academic and practical resources that I routinely draw upon for helping me do my work. Although my library is vast, I have a select group of books that occupy a prominent position on my desk. I’ve been asked enough times about my “favorite titles” list, I thought I’d write this article for my readers.

The Benefits of Having a Data Scientist Career

Our friends over at Simplilearn provided us the infographic below which explores the advantages of the a data science career and shows you the various roles available in this career path, along with projected salaries from around the globe.

Why Technology is the Next Frontier in Mental Health

In this special guest feature, Kouris Kalligas, CEO and co-founder at Therachat, highlights how different data technologies, such as artificial intelligence, machine learning, digital journaling, data analytics and data visualization are being integrated into our everyday lives via smartphones and modernizing how mental health services are delivered.

Interview: Vipin Kumar, Regents Professor and William Norris Chair in Large Scale Computing at University of Minnesota

The following is a discussion with Vipin Kumar, Regents Professor and William Norris Chair in Large Scale Computing at University of Minnesota; ACM Fellow 2015. The Association of Computing Machinery (ACM) just concluded a celebration of 50 years of the ACM A.M. Turing Award (commonly known as the “Nobel Prize of computing”) with a two-day conference in San Francisco. The conference brought together some of the brightest minds in computing to explore how computing has evolved and where the field is headed.

Interview: Mary Cameron, Data Scientist at Tophatter

I recently caught up with Mary Cameron, Data Scientist at Tophatter, to get her compelling insights into how Tophatter uses the principles of data science. She also delves into her life as a data scientist at a dynamic and growing company.

Defining the Data Science Landscape

In this contributed article, Manny Bernabe who leads and develops strategic relationships for Uptake’s Data Science team, discusses how it is important to note the distinctions in terminology in the data science landscape. Perhaps most notably, people must be aware of the differences between data science, machine learning and artificial intelligence. The three shouldn’t be used interchangeably due to fundamental differences in their definitions and in what they deliver.

Using Python to Drive New Insights and Innovation from Big Data

In a recent white paper “Management’s Guide – Unlocking the Power of Data Science & Machine Learning with Python,” ActiveState – the Open Source Language Company – provides a summary of Python’s attributes in a number of important areas, as well as considerations for implementing Python to drive new insights and innovation from big data.

Book Review: Statistical Learning with Sparsity – The Lasso and Generalizations

As a data scientist, I have a handful of books that serve as important resources for my work in the field – “Statistical Learning with Sparsity – The Lasso and Generalizations” by Trevor Hastie, Robert Tibshirani, and Martin Wainwright is one of them. This book earned a prominent position on my desk for a number of reasons.