2016 Sep Opinions, Interviews
All (104) | Courses, Education (13) | Meetings (18) | News, Features (23) | Opinions, Interviews (19) | Software (8) | Tutorials, Overviews (20) | Webcasts & Webinars (3)
- Embedded Analytics: The Future of Business Intelligence - Sep 30, 2016.
An overview of the evolution of Business Intelligence, and some insight into where its future lie: embedded analytics.
- Predicting Future Human Behavior with Deep Learning - Sep 30, 2016.
Carl Vondrick, MIT researcher, who studies computer vision and machine learning, discusses how to use Big Data with minimal annotations and applications to predictive vision and scene understanding.
- Data Science of Sales Calls: The Surprising Words That Signal Trouble or Success - Sep 29, 2016.
While not as profound a problem as uncovering the secrets of the universe, how to conduct a successful sales conversation is an age-old problem, impacting millions of people every day.
- Top Data Scientist Claudia Perlich on Biggest Issues in Data Science - Sep 29, 2016.
Find out what top data scientist Claudia Perlich believes are - and are not - the biggest issues in data science today, and why spending 80% of their time with data preparation is not a problem.
- Information Spectrum of Diffusion – What Analysts Need to Know - Sep 28, 2016.
Quandl, which is a source of financial, economic, and alternative data, created a graphic called The Spectrum of Diffusion, describing the stages of information diffusion (accessibility) from "untapped" to "fully commoditized."
- Data Science Basics: Data Mining vs. Statistics - Sep 28, 2016.
As a beginner I was confused at the relationship between data mining and statistics. This is my attempt to help straighten out this connection for others who may now be in my old shoes.
- Why Not So Hadoop? - Sep 27, 2016.
Are Big Data and Hadoop synonymous? Not really, but they are often conflated. Has Hadoop lived up to its hype? In this article, we will look at a brief history of Hadoop and see where it stands today.
- Top Data Scientist Claudia Perlich’s Favorite Machine Learning Algorithm - Sep 27, 2016.
Interested in the reasons why a top data scientist is partial to one particular algorithm over others? Read on to find out.
- The Trump Phenomenon: A Twitter Based Recount - Sep 26, 2016.
This analysis uses Twitter data to perform a sentiment analysis to help determine how people truly feel about Trump. We found that while his fans have supported him throughout his entire campaign, more and more Twitter users have started to grow tired of Trump’s attitude.
- Data Science for Internet of Things (IoT): Ten Differences From Traditional Data Science - Sep 26, 2016.
The connected devices (The Internet of Things) generate more than 2.5 quintillion bytes of data daily. All this data will significantly impact business processes and the Data Science for IoT will take increasingly central role. Here we outline 10 main differences between Data Science for IoT and traditional Data Science.
- 7 Ways How Data Science Fuels The FinTech Revolution - Sep 16, 2016.
Here are 7 ways how data science is at the core of the current transformation of the financial sector.
- The Deception of Supervised Learning - Sep 13, 2016.
Do models or offline datasets ever really tell us what to do? Most application of supervised learning is predicated on this deception.
- Big Data and The Internet of Things: A Match Made in Heaven - Sep 12, 2016.
Think of data as the fuel that helps the Internet of Things run. Big data and the IoT basically go hand in hand -- a match made in heaven, so to speak.
- The (Not So) New Data Scientist Venn Diagram - Sep 12, 2016.
This post outlines a (relatively) new(er) Data Science-related Venn diagram, giving an update to Conway's classic, and providing further fuel for flame wars and heated disagreement.
- Big Data is Too Big to Die - Sep 9, 2016.
As the traditionalist data analytics professionals dig their heels in and refuse to give in to the Big Data deluge, it is fast becoming clear that the volume of evidence for the new movement is too substantial to deny.
- Big Data Dilemma: Save Me Money Versus Make Me Money - Sep 7, 2016.
Does your organization see Big Data as an opportunity to “Save Me More Money”, or does your organization see Big Data as an opportunity to “Make Me More Money”?
- Introducing Dask for Parallel Programming: An Interview with Project Lead Developer - Sep 7, 2016.
Introducing Dask, a flexible parallel computing library for analytics. Learn more about this project built with interactive data science in mind in an interview with its lead developer.
- How to Become a Data Scientist – Part 3 - Sep 6, 2016.
This is the third and final part of a thorough, in-depth overview of becoming a data scientist, written by a recruiter in the field. This part focuses on the job market.
- 7 Big Data Steps in Health Science - Sep 1, 2016.
Our doctors are now getting help from Big Data, which is becoming more entrenched and more crucial to reducing the investment needed to keep us healthy. But, how does Big Data actually do this?