Summary: Over 80% of companies are not yet using advanced analytics. Here’s a step-by-step plan to implement a brand new predictive analytics program getting the biggest bang for your buck from the most cost effective investment.
Are You (Your Company) Part of the 80% or the 20%?…Continue
We have published many articles on this subject, for instance:Continue
The year was 2006. LinkedIn had done the improbable. People started to notice them. They had momentum on their side edging towards 10 million users. There was one problem. The holy grail of networking is connecting with other folks and expanding networks. People were inviting other people but not connecting with people already there on LinkedIn.
A LinkedIn employee, Jonathan loved playing with data. He did…Continue
Added by Sudhanshu Ahuja on October 5, 2015 at 7:00am — No Comments
Opportunities for talented data scientists – both seasoned professionals and enthusiastic newcomers -are everywhere. Long gone are the days when a desire to work with statistics, analytics or predictive modelling would restrict you to the IT or financial sectors when searching for work. Recently I’ve written about innovative data strategies being undertaken by companies in…Continue
Added by Bernard Marr on October 4, 2015 at 5:30pm — No Comments
Time Series IoT applications in Railroads
Authors: Vinay Mehendiratta, PhD, Director of Research and Analytics at Eka Software
and Ajit Jaokar, Data Science for IoT course
This blog post is part of a series of blogs exploring Time Series data and IoT.
The content and approach are part of the Data Science for Internet of…Continue
Added by ajit jaokar on October 4, 2015 at 12:18pm — No Comments
When discussing the use of algorithms, the issue of durability or portability has to be considered. For example, a stock trading algorithm might be used in a missile guidance system. The algorithm would have to operate on an abstract kinetic level rather than for a specific application. I have written in the past about using the same algorithm to study stocks, earthquakes, hurricanes, electro-cardiograms, and attempts at evasion - using my mouse in a game environment. Wouldn't an abstraction…Continue
Added by Don Philip Faithful on October 4, 2015 at 7:17am — No Comments
I spent way too much time sorting through all the information collected on Data Science. All I knew in the beginning is that it had something to do with math and statistics and algorithms (which are love), and computers (which are hate not so much love). It's finally starting to fall into place. I made a preliminary list of all the things I should learn. In the process, I stumbled upon Clare Corthell's "Open Source Data Science Master's…Continue
Added by Elma Bratovic on October 3, 2015 at 9:37pm — No Comments
Cluster Analysis is a common technique to group a set of objects in the way that the objects in the same group share certain attributes. It’s commonly used in marketing and sales planning to define market segmentations.
Here at BigObject we adopt a simple approach to exploring the similarities between…
Added by Yuanjen Chen on October 2, 2015 at 1:21pm — No Comments
Last week we discussed the importance of data scientists prioritizing client confidentiality and the concern of exposing high-value information to…Continue
Added by Michael Walker on October 1, 2015 at 8:07pm — No Comments
The Data Science Association (DSA) and Google is interested in learning more about your experience with tools and training. Click on the link below to take a 10-minute survey for data scientists.…Continue
Added by Michael Walker on October 1, 2015 at 7:53pm — No Comments
Econometrics is fundamental to many of the problems that data scientists care about, and it requires many skills. There's philosophical skill, for thinking about whether fixed effects or random effects models are more appropriate, for example, or what the direction of causality in a particular problem is. There's some coding, including knowing the right commands to interact with statistical programs like Stata or R, and how to interpret their output. There's the intuition to know which…Continue
Named after a kid’s toy elephant and initially recognized as a technical problem, today it drives a market that’s expected to be worth $50 billion by 2020. It is the most talked about technology since its inception as it allows some of the world’s largest companies to store and process data sets on clusters of commodity hardware.…Continue
Added by Durgesh Kaushik on October 1, 2015 at 9:00am — No Comments
Most data scientists and statisticians agree that predictive modeling is both art and science yet, relatively little to no air time is given to describing the art. This post describes one piece of the art of modeling called feature engineering which expands the number of variables you have to build a model. I offer six ways to implement feature engineering and provide…Continue
"In that darkness the White Walkers came for the first time. They swept through cities and kingdoms, riding their dead horses, hunting with their packs of pale spiders big as hounds." - Old Nan
Like millions of other people, last night’s sleep was a little harder to come by after watching the latest episode of "Game Of Thrones". The final 20 minutes of the episode entitled…Continue
Added by Shubhi Gupta on October 1, 2015 at 1:00am — No Comments
The weekly digest now has 6 sections: (1) Featured Articles and Case Studies, (2) Featured Resources and Technical Contributions, (3) From our Sponsors, (4) News, Events, Books, Training, Forum Questions, (5) Picture of the Week, and (6) Syndicated Content.
The full version is always published Monday. Starred articles are new additions or updated content, posted between Thursday and Sunday.…Continue
Added by Vincent Granville on September 30, 2015 at 10:30am — No Comments
Are you a data scientist or a decision scientist? Interesting question recently raised by Deepinder Dhingra, head of products and strategy at Mu Sigma in his article ‘Data science’ misses half the equation: an argument for ‘decision…Continue
The importance of gathering and managing the right consumer data, applying analytics to generate valuable insights and translating those insights into effective front-line action cannot be emphasized enough for companies, especially those that are directly consumer-facing.
Every day, retailers and FMCG companies have access to vast amounts of data, capturing information on every transaction at every store.…Continue
Added by Aureus Analytics on September 30, 2015 at 3:01am — No Comments
Data is everywhere. We generate data when using an ATM, browsing the Internet, calling our friends, buying shoes in our favourite e-shop or posting on Facebook. Companies collect this data en masse in order to make more informed business decisions, such as:
The first one is about the difference between Data Science, Data Analysis, Big Data, Data Analytics, and Data Mining:
The source for this one is, according to a tweet, onthe.io. I could not find the article in question, though this website is very interesting, but anyway, I love the above picture,…Continue
Are you trying to overcome the challenges of taking your big data projects from pilot to production? Do you want to harness big data to increase customer loyalty, reduce fraud, and improve operational efficiency? Or are you using big data to solve some of the biggest challenges in your industry, whether that’s healthcare, financial services, insurance, retail, manufacturing, media and entertainment, or government services? Then Informatica can help. We are already working with customers…Continue