Big Data Influence on Data Driven Advertising
More and more companies relying on big data for their data driven initiatives. In a survey conducted by BlueKai, we are trying to capture what its impact on advertising strategies.
on Aug 31, 2015 in Advertising, Big Data, Kaushik Pal
How to become a Data Scientist for Free
Here are the most required skills for a data scientist position based on ReSkill’s analyses of thousands of job posts and free resources to learn each skill.
on Aug 28, 2015 in Data Science Education, Data Scientist, Java, Online Education, Python, R, SQL, Statistics
Gartner 2015 Hype Cycle: Big Data is Out, Machine Learning is in
Which are the most hyped technologies today? Check out Gartner's latest 2015 Hype Cycle Report. Autonomous cars & IoT stay at the peak while big data is losing its prominence. Smart Dust is a new cool technology for the next decade!
on Aug 28, 2015 in Big Data, Citizen Data Scientist, Gartner, Machine Learning
Data Hierarchy of Needs
Data Hierarchy of Needs helps understand the steps in Big Data processing. Before going to advanced data modeling (top of the pyramid), organizations need to fill huge holes they frequently have in the base of the pyramid, lacking reliable complete data flow.
on Aug 28, 2015 in Data Management, Data-Driven Business, Yanir Seroussi
Paradoxes of Data Science
There are many paradoxes, ironies and disconnects in today’s world of data science: pain points, things ignored, shoved under the rug, denied or paid lip.
on Aug 21, 2015 in Data Science, Data Science Skills, Myths, Thomas Ball
Poll Results: Where is Big Data? For most, Largest Dataset Analyzed is in laptop-size GB range
A majority of data scientists (56%) work in Gigabyte dataset range. We note a small increase in Petabyte (web-scale) data miners, and a decline in Megabyte data miners. US, Australia/NZ, and Asia lead in percentage of Terabyte and Petabyte analysts.
on Aug 18, 2015 in Asia, Australia, Big Data, Datasets, Europe, Largest, Poll, USA
Big Idea To Avoid Overfitting: Reusable Holdout to Preserve Validity in Adaptive Data Analysis
Big Data makes it all too easy find spurious "patterns" in data. A new approach helps avoid overfitting by using 2 key ideas: validation should not reveal any information about the holdout data, and adding of a small amount of noise to any validation result.
on Aug 17, 2015 in Holdout, Model Performance, Overfitting, P-value, Vitaly Feldman
Recycling Deep Learning Models with Transfer Learning
Deep learning exploits gigantic datasets to produce powerful models. But what can we do when our datasets are comparatively small? Transfer learning by fine-tuning deep nets offers a way to leverage existing datasets to perform well on new tasks.
on Aug 14, 2015 in Deep Learning, Image Recognition, ImageNet, Machine Learning, Neural Networks, Transfer Learning, Zachary Lipton
11 things to know about Sentiment Analysis
Seth Grimes, a text analytics guru, shares 11 key observations on what works, what is past, what is coming, and what to keep in mind while doing sentiment analysis.
on Aug 13, 2015 in Affective Computing, Emoji, Sentiment Analysis, Text Analytics
3D Data Sculptures: a New Way to Visualize Data
3D printing can go beyond printing products like iPod cases, or butterfly earrings, and can offer a sustainable way to understand strategic DATA by printing decision support landscapes.
on Aug 11, 2015 in 3D, China, Data Visualization, Sculpture
R Programming: Who, Where and What
The “sexiest job” has the sexiest demand, and R is one of their leading weapons. Here, we are trying to capture how these unicorns are distributed, and also where you can move if you want to have great opportunities.
on Aug 11, 2015 in India, Programming, R, Salary, USA
Three Essential Components of a Successful Data Science Team
A Data Science team, carefully constructed with the right set of dedicated professionals, can prove to be an asset to any organization,
on Aug 10, 2015 in Business Analyst, Data Engineer, Data Science Team, Machine Learning, Team
Understanding Basic Concepts and Dispersion
In analytics it is a common practice to understand the basic statistical properties of its variables viz. range, mean and deviation. Centrality measures are the most important to them, explore how to use these measures.
on Aug 10, 2015 in Dispersion, RideOnData, Statistics
Five Steps to Implement an Enterprise Data Lake
This guide helps you to initiate a new IT culture mapped to your business goals, and shows how do create an efficient data reservoir, what makes data more useful, and what are the cutting-edge tools/devices/applications you need.
on Aug 10, 2015 in Data Lake, Impetus
How Long Should You Stay at Your Analytics Job?
Considering the huge demand for the data scientists many are pondering to switch for a better profile and salary. But, there some things to be pondered about like what should be the interval between two switches, acquiring new skills and your loyalty.
on Aug 7, 2015 in Analytics, Burtch Works, Data Scientist, Hiring
Patterns for Streaming Realtime Analytics
Design patterns are well-known for solving the recurrent problems in software engineering, on similar lines we can have Streaming Realtime Analytics patterns and avoid reinventing the wheel. Here, you can see the major patterns we found out for it.
on Aug 5, 2015 in Frequent Pattern Mining, Realtime Analytics, Streaming Analytics
Cartoon: Big Data and the dog question
It used to be that nobody on the internet knew that I was a dog ... New KDnuggets cartoon examines the dog question in the era of Big Data.
on Aug 3, 2015 in Anonymity, Big Data, Cartoon, Dogs, Privacy
New Standard Methodology for Analytical Models
Traditional methods for the analytical modelling like CRISP-DM have several shortcomings. Here we describe these friction points in CRISP-DM and introduce a new approach of Standard Methodology for Analytics Models which overcomes them.
on Aug 3, 2015 in CRISP-DM, Data Mining, Modeling, Olav Laudy, ROI
Data is Ugly – Tales of Data Cleaning
Whether you want to do business analytics or build the deep learning models, getting correct data and cleansing it appropriately remains the major task. Find out experts opinions on how you can make efficient data cleansing and collection efforts.
on Aug 1, 2015 in Big Data, Data Cleaning, Data Preparation, Data-Driven Business
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