Over the past year, as the head of analytics at a tech startup, I've had many conversations with analysts about what they want to learn from their data. Perhaps unsurprisingly, a lot of companies have similar questions—What drives retention? How do customers interact with products? How do we better understand sales pipelines? What's the lifetime value of a user?
These questions were familiar to us and we'd worked on many of them ourselves. To find answers in our own data, we wrote… Continue
Added by Benn Stancil on December 17, 2014 at 7:00am —
Guest blog past by Rohit Yadav, from BRIDGEi2i Analytics Solution
The Net (Part 1)
The plot goes something like this – Sandra Bullock plays a computer expert Angela Benett, her life changes when she is sent a program with a crazy glitch to ‘de-bug’. Soon she finds out some vital government information on the disk, things gets nutty as fruitcake, her life becomes a nightmare with her records getting erased and she is given a new identity of some chick with a… Continue
Added by Vincent Granville on December 16, 2014 at 7:30pm —
In my consulting work in the Enterprise IT space, I am seeing a definite trend of growing interest in Data Product/Advanced Analytics Design and Development which is becoming increasingly mainstream. Even as I view this a positive, it comes with its own set of perils and pitfalls that will need to be avoided.
Enterprise IT Application Development is often bureaucratic and involves multiple and redundant levels of management through the design, development and testing phases.… Continue
Added by Mark Sharma on December 16, 2014 at 8:30am —
The top tech companies by market capitalization are IBM, HP , Oracle , Microsoft , Cisco , SAP , EMC , Apple , Amazon and Google
All of the top tech companies are selected based on their current market capitalization with the exception of Yahoo. The year 2014 is not included as part of this analysis.
Data: The source of this data is from the public financial records from SEC.gov
All the sales figures are normalized and reported in USD… Continue
Added by Nilesh Jethwa on December 15, 2014 at 11:01am —
The definition of 'best' depends on which school you follow. Data science and classic statistical science are at the opposite ends of the spectrum. So let's clarify what 'best solution' means in these two opposite contexts:
'Best', according to statistical science:
- It usually means the global maximum of a mathematical optimization problem
- The objective function involved is usually a maximum likelihood function, KS, c-statistics, or some function…
Added by Vincent Granville on December 14, 2014 at 8:30pm —
We all know that calculating error bounds on metrics derived from very large data sets has been problematic for a number of reasons. In more traditional statistics one can put a confidence interval or error bound on most metrics (e.g., mean), parameters (e.g., slope in a regression), or classifications (e.g., confusion matrix and the Kappa statistic).
For many machine learning applications, an error bound could be very important.… Continue
Added by Anna Anisin on December 14, 2014 at 3:33pm —
Guest blog post by Bernard Marr, first published here.
The field of Big Data requires more clarity and I am a big fan of simple explanations. This is why I have attempted to provide simple explanations for some of the most important technologies and terms you will come across if you’re looking at getting into big… Continue
Added by Vincent Granville on December 12, 2014 at 12:00pm —
These predictions were published by the International Institute for Analytics (IIA). They produced a nice infographics, featured below, and re-tweeted many times by various bloggers, using the hash tag #2015Analytics. Other interesting predictions include… Continue
Added by Vincent Granville on December 12, 2014 at 10:30am —
On the face of it, John Kotter’s seminal book “Our iceberg is melting” is a simple tale of a group of penguins who are scared about losing their home, their iceberg, and yes, even more scared of the changes that could entail. But through that simple story and their struggle for finding their new home, the story delivers a more powerful message that… Continue
Added by Debleena Roy on December 10, 2014 at 8:30am —
The full version is always published Monday. Starred articles or sections are new additions or updated content, posted between Thursday and Sunday. Articles marked with a + have interesting visualizations.
Added by Vincent Granville on December 9, 2014 at 9:30pm —
While we aren’t exactly “following the yellow brick road” these days, you may be feeling a bit like Dorothy from the “Wizard of Oz” when it comes to these topics. No my friend, you aren’t in Kansas… Continue
Added by Carla Gentry on December 8, 2014 at 6:30pm —
The purpose of this article is to clarify a few misconceptions about data and statistical science.
I will start with a controversial statement: data science barely uses statistical science and techniques. The truth is actually more nuanced, as explained below.
1. Data science heavily uses new statistical… Continue
Added by Vincent Granville on December 8, 2014 at 5:00pm —
When you use Twitter, how do you know when you are being presented with something credible instead of something totally bogus? The answer is, unless you spend a lot of time researching each tweet, you probably don’t. However, one thing is for certain, we rely on what we read on Twitter to be true.
Twitter is one of the fastest and most effective ways we disseminate news across our world. If this… Continue
Added by Renette Youssef on December 8, 2014 at 4:00pm —
This article is not about any futuristic "Iron Man style dashboard/data visualization product" where you are combing through holographic cubic chunks from your ultra fast Big Data pipeline.
From time to time I keep pondering on what could be the future and I am sure lot of us get this science fiction imagery where… Continue
Added by Nilesh Jethwa on December 8, 2014 at 7:30am —
Hello Data Science,
Thanks for allowing me the opportunity to be a part of Data Science Central!
Recently, I have embarked on a journey to become a Data Scientist! In doing so, I have begun to write an article about my findings to help those interested in becoming a Data Scientist as well, but don’t know where to start.
One thing I would love to include in my article is the backgrounds, opinions and teachings of real Data Scientists. In order to capture this, I have put… Continue
Added by Anthony Dutra on December 8, 2014 at 7:00am —
Here I compare these 5 rules published in 1999, with the new 2014 version. Data has changed so much that the opposite rules are now followed. Yet many statisticians and big businesses still stick to the outdated rules.
These rules were initially published in the featured book (see picture) first published in 1999, when software (e.g. SPSS) could not adapt to… Continue
Added by Vincent Granville on December 7, 2014 at 4:00pm —
Like many students about to finish their undergraduate degree, I decided to artificially inflate my grades by taking some "bird courses." These are not courses about birds. Other students assured me that the courses were designed to bolster my marks and to help me complete my program requirements. Considering the many bird courses available, I decided to take introductory music, which was essentially a history course focused on music. It required a lot of… Continue
Added by Don Philip Faithful on December 6, 2014 at 8:52am —
We all know models are always an illusion of reality - yet may or may not be useful. Data scientists should build both simple and complex models with reasonable and testable assumptions that capture what is… Continue
Added by Michael Walker on December 5, 2014 at 6:31pm —
Often in conversations with clients I’m asked the question “where do we begin?”
The costs of measurement, storage, and computation have collapsed. Now organizations are faced with the often daunting challenge of uncovering value from their data assets. How do we put those data assets to work? In nearly every industry, rapid experimentation is no longer a competitive advantage, but rather a market…
Added by Cameron Turner on December 5, 2014 at 9:30am —
According to Deloitte’s 2014 Human Capital Trends survey, 86% of companies do not have any analytics capabilities in HR, and 67% are ‘weak’ at using HR data to predict workforce performance and improvement.
At the Workforce and HR Analytics Summit West 2015 (March 9-10, San Diego) experienced HR leaders will share how they have reshaped their HR analytics from reporting factories to strategic solution providers that answer some of the most important business… Continue
Added by Alesia on December 4, 2014 at 2:00pm —