"Half the money I spend on advertising is wasted; the trouble is I don't know which half."
John Wanamaker, a department store merchant and marketing pioneer in the late 19th and early 20th century (as well as Postmaster General from 1889 to 1893), is reputed to have made this statement and advertisers have been wrestling with the question ever since.
Enter the science of marketing measurement. In the early days the questions revolved around the…Continue
Added by Gregory Thompson on October 23, 2015 at 1:00pm — No Comments
Added by Neuza Nunes on October 23, 2015 at 11:57am — No Comments
The information and technology age in which we find ourselves has created many great conveniences and opportunities for innovation. Buying items has become incredibly easy, for example – a swipe of your debit card and you’re good to go! You don’t have to worry about the risks of carrying around cash, and the whole process takes just a moment. Everything is automatically updated to your bank account, even, which takes away the necessity of balancing checkbooks and manually updating your…Continue
NJF Global Holdings Ltd (31…Continue
Added by Kodig on October 22, 2015 at 11:56pm — No Comments
The cloud, end-user, and data center are all evolving. In the recent past, the manner in which information has been processed and computed has totally changed. Today, with more consumerization and plenty of data centers, and cloud computing, it is important for administrators to adapt to the new demands. Because of the movement towards…Continue
Added by Jack Dowson on October 22, 2015 at 11:26pm — No Comments
Does human intelligence have any connection to the type of music a person listens to? Can we define human intelligence? How do you measure human intelligence? Do SAT scores accurately measure human intelligence? Is there any evidence that SAT scores accurately predict educational or workplace performance?
I am skeptical 1) that we…
In this post, we will look at how data science can be used to improve mechanical and materials engineering in the semiconductor manufacturing industry by summarizing the work that Pivotal’s Data Science team did for a real-world customer.
As in any manufacturing or…Continue
Added by Anirudh Kondaveeti on October 22, 2015 at 11:13am — No Comments
Capacity planning is an arduous, ongoing task for many operations teams, especially for those who rely on Virtual Machines (VMs) to power their business. At Pivotal, we have developed a data science model capable of forecasting hundreds of thousands of models to automate this task using a multivariate time series approach. Open to reuse for other areas such as industrial equipment or vehicles engines, this technique can be applied broadly to anything where regular monitoring…Continue
Added by Anirudh Kondaveeti on October 22, 2015 at 11:11am — No Comments
In this blog post, we take a look at some work I did with my colleague Jin Yu to explain how data science techniques such as sequential pattern mining can detect coordinated network threats such as watering hole attacks. Watering hole attacks target a group of users in an organization by attacking the most popular websites among these users.
By examining outbound network traffic data, researchers can analyze potential threats after…Continue
Originally published by in 2013, it still is a goldmine for all machine learning professionals. The algorithms are broken down in several categories. Here we provide a high-level summary, a much longer and detailed version…Continue
I recently completed a full iron-distance triathlon (2.4 mile swim, 112 mile bike, 26.2 mile run) after 9 months of training. There are similarities between my experience as a triathlete and as a Big Data Analytics team member at SAP. Elements of success are the same in both venues.
(1) The first similarity is acquisition of multiple skill sets. While triathlons involve swimming, biking and running, the Big Data Analytics team must have skills in machine…Continue
Added by Patti Tillotson on October 22, 2015 at 7:49am — No Comments
You may think that all big data experts are created equal, but nothing could be further from the truth. However, the terms “data scientist” and “business analyst” are often used interchangeably. It’s a common and confusing use of terminology, which is why [email protected], a masters in business analytics, created this infographic to help create further clarity about the two…Continue
Added by Taylor Meadows on October 22, 2015 at 4:30am — No Comments
We created a Data Science position test.
This short test includes few multiple choice questions to check your knowledge.
This test includes the following skills:
More than 50 years ago, John Tukey called for a reformation of academic statistics. In ‘The Future of Data Analysis’, he pointed to the existence of an as-yet unrecognized science, whose subject of interest was learning from data, or ‘data analysis’. Ten to twenty years ago, John Chambers, Bill Cleveland and Leo Breiman independently once again urged academic statistics to expand its boundaries beyond the classical domain of theoretical statistics; Chambers called for more emphasis on…Continue
Added by Vikram Jayaram on October 22, 2015 at 4:21am — No Comments
As a hiring manager for data analytics positions, I often complain that there are not enough qualified resumes. Most of the resumes that do get passed on to me from recruiters quickly get filed away. Those job candidates belong to one of five high-risk types that I have identified over the years.
These high-risk candidates do not make the cut despite having technical degrees or technical work experience. If your resume falls into one of these types, you should make an effort to remove…Continue
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 October 21, 2015 at 9:30am — No Comments
Summary: Stream Processing and In-Stream Analytics are two rapidly emerging and widely misunderstood data science technologies. In this article we’ll focus on their basic characteristics and some business cases where they are useful.
There are five relatively new technologies in data science…Continue
Added by Algolytics on October 21, 2015 at 5:29am — No Comments