Great IoT, Sensor and other Data Sets Repositories via @DataScienceCtrl

This is an interesting resource for data scientists, especially for  those contemplating a move in IoT (Internet of things). Many of these modern, sensor-based data sets collected via Internet protocols and various apps and devices, are related to energy, urban planning, healthcare, engineering, weather, and transportation sectors.

Great resource posted by Vincent Granville on Data Science Central.

How to Use Data Visualizations to Win Over Your Audience via @KDnuggets

Any credible media services require a scientific insights, generated by the support of any case more than cold, hard, unbiased data. In this post, you will learn how to use data visualizations to win over your audience.

Interesting thoughts on how to use them to good effect from KDnuggets

WEBINAR: Starting the Workforce Analytics Journey: Your First 100 Days - 5 November 2015

Key ideas to be discussed:

• 
  Start with a Business Problem in mind - and an interested stakeholder 
• 
  Move beyond simply reporting, the real value in workforce analytics comes from the use of social, behavioural and predictive analytics
• 
  HR need not do this alone, there is often analytics expertise in other areas of organizations that can be applied to people data
• 
  Cloud developments mean that the cost of the technology needed to undertake analytics is much more affordable for HR
• 
  You don’t need to be fluent in the language of mathematics to succeed in analytics – cognitive computing now means you can use natural language to analyse and find insights in your data
• Given the rich seams of workforce data available in organisations today HR analytics has huge potential to deliver clear business benefits, but many HR leaders are left wondering where to start. In this webinar, Jonathan Ferrar, Dr. Nigel Guenole, and Sheri Feinzig will talk you through a new 100 day guide to setting up an analytically enabled HR function in clear, practical steps. Avoid the pitfalls, learn from what others have done and set your organisation up for success with insights from the extensive and successful experiences of IBM and its clients.

Jonathan Ferrar

Jonathan is responsible for leading the strategic direction, product strategy and research & development of HR solutions at IBM Smarter Workforce.  He is a Fellow of the Chartered Institute of Personnel and Development in the UK having been in the HR profession for 20 years with three different companies. Jonathan's LinkedIn Profile.

Dr. Nigel Guenole

Nigel Guenole is a researcher with the Smarter Workforce Institute and a Senior Lecturer in Management at Goldsmiths, University of London. He is known for his
work in workforce analytics and psychological measurement. Dr. Guenole’s work has appeared in leading scientific journals including Industrial Organizational Psychology: Perspectives on Science and Practice and Frontiers in Quantitative Psychology & Measurement, as well as in the popular press. Nigel's LinkedIn profile.

Register using the link from this page

13 Machine Learning Data Set Collections via @DataScienceCtrl

Here are 13 resources on Machine Learning data sets.

List from Data Science Central.

Why Self-Driving Cars Must Be Programmed to Kill via @TechReview

Self-driving cars are already cruising the streets. But before they can become widespread, car makers must solve an impossible ethical dilemma of algorithmic morality.

Interesting article from MIT Technology Review

Agile Development And Data Management Do Coexist via @infomgmt

Well, if you can't beat them, join them . . . and that's what your data management pros are doing, jumping into Agile development for data.

Interesting article from Information Management.

We need open and vendor-neutral metadata services via @radar

Joe Hellerstein outlines 6 reasons why the data industry should rally to develop open and vendor-neutral metadata services. He makes a solid case for how improvements in metadata collection and sharing can pave the way for many interesting applications and capabilities.

Interesting article from O'Reilly Radar.  It would be great if this were to come true but somehow I doubt it will ever happen.

How the C-Suite Can Create a High-Impact Big Data Culture via @Data_Informed

Ron Bodkin of Think Big Analytics discusses the importance of culture to the success of big data initiatives and the role of the C-suite in creating that culture.

Interesting article from Data Informed

Data Analytics: Is it Worth Outsourcing? via @InfinitDatum

Big data became more than a buzzword when industries discovered its huge potential. And with this finding, came a new science called data analytics.

The demand to understand all the data we’ve been creating and amassing has grown to a level, which not all businesses among different industries can cope with—making outsourcing a valuable option.

But, is it really worth outsourcing data analytics? More so, is data analytics even worth investing on? Read the great article from InfinitDatum to find an interesting view.

I have to say I agree with many of the points in it - however I'm not convinced on the transfer to knowledge of both the business the company operates in as well as the actual company to make the Analytics as good as they could be.  I'm moving more towards a hybrid approach that uses external resources to do a lot of the technical side of things, but still relies on internal resources to do much of the work on design and actual reporting.

WEBINAR: What’s New in Statistica 13 - 29 October 2015

Tech webcast: What’s New in Statistica 13

Check out the first major update of our predictive analytics platform since we added Statistica to our software portfolio in March 2014. You’ll see how new software integrations and customer-driven enhancements combine to make Statistica 13 an analytics powerhouse that addresses your practical, all-data analysis and data management needs.

Our analytics expert will show you how to take full advantage of:

• A completely modernized user interface with a visual analytic workflow
• In-Database Analytics that push analytics to where your data lives
• Powerful text analytics capabilities
• Statistica Interactive Visualization and Dashboards
• Enhanced Hadoop integration/HDFS

 

What you will learn

By the end of this session, you’ll understand how Statistica 13 lets you embed analytics everywhere, so you can analyze any data, anywhere. You’ll also discover how it empowers everyone, from data scientists to business users. With our expert’s tips, you’ll easily uncover hidden opportunities by analyzing all data, streaming or at rest.

Speakers

Dev Kannabiran is a senior data mining consultant with Dell Statistica. He holds a master’s degree in management information systems from Oklahoma State University and has years of experience in the application of predictive modeling and text mining. Dev has been involved with numerous Statistica deployment projects across various industries, including insurance, manufacturing, and financial services.

Event Details

Online

Oct.

29

• Date:Oct. 29, 2015
• Time:1:00 PM – 2:00 PM EST
• Duration:60 Minutes
• Location:WebEx
• Event:Online
  
  Register here

6 Benefits of Data Modeling in the Age of Big Data via @datafloq

In dealing with Big Data, you've probably heard the term “data modeling” being used as a core concept in dealing with large amounts of data. Data modeling is a branch of specialization in and of itself. It’s a term you should be familiar with, and in this column, we’ll explore what it is and, more critically, why it’s important and what the different benefits are of data modeling.

Read about it here on Datafloq.  I particularly like it helping with data integration.  Sometimes people call the same thing by many different names and may even have them in a different format. That doesn't mean it is not the same,you just need to find the key to integrating them.

Top Three Questions to Ask Hadoop Vendors via @BigData_Review

David M. Fishman, VP of Marketing at Arcadia Data outlined the top three questions you should ask Hadoop vendors at the Strata Conference.

Read about it here in Solutions Review

Predictive Analytics Business Case is Simpler Than You Think via @infomgmt

Predictive analytics offers significant returns for organisations willing pursue it, but establishing a solid business case is the first step for any organisation.

Interesting article from Information Management.

Two Obstacles Facing Retailers for Data Driven Marketing Success via @B2Community

Data Driven marketing is on its way to becoming the standard in all industries. A 2015 study by the Global Direct Marketing Association and Winterberry group found that 92% of surveyed marketing professionals believe that data will be an important factor in the future of marketing.

Read more here on Business 2 Community

A Step-by-Step Plan for Getting Your Company Started with Predictive Analytics via @DataScienceCtrl

Over 80% of companies are not yet using advanced analytics.  Here’s a step-by-step plan by William Vorhies to implement a brand new predictive analytics program getting the biggest bang for your buck from the most cost effective investment.

You can see part one here and part two here.

I strongly recommend you read this and take note of the steps - I think any organisation would be very pleased with the benefits that can be achieved at a fairly low cost by introducing predictive analytics into their mix.

What’s The Value Of Your Data? via @techcrunch

Whether you're an organization or an individual, there aren't any hard and fast rules for putting a price tag on your data. Here are a few ways to approach it.

Good to stop and think about it sometimes.

Simple Framework to crack a Kaggle problem statement via @AnalyticsVidhya

A Step by step process & framework of taking a simple shot on a Kaggle statement, sharing along tips & tricks to approach these competitions.

Useful article for any Data Science hopefuls.

Big Data’s Impact on Insurance Modeling via @infomgmt

Evolving big data and analytic technologies are putting pressure on insurance companies to mitigate the risks associated with their actuarial and business models.

Interesting article from Information Management.

WEBINAR: Data Analytics with Amazon Redshift: A Success Story - 27 October 2015

Overview

Title: Data Analytics with Amazon Redshift: A Success Story

Date: Tuesday, October 27, 2015

Time: 09:00 AM Pacific Daylight Time

Duration: 1 hour

Summary

Data Analytics with Amazon Redshift: A Success Story

Join us for our latest DSC Webinar series to learn how business intelligence and systems integration teams from Sager Creek have improved distribution management, demand planning , promotional pricing and more.  We will be discussing how they enabled:

-Multi-source and high volume data blending through a cloud data warehouse
-Consumable data workflows to internal customers
-Predictive analytics, through a system that requires no coding

You will learn how implementing a cloud data warehouse, data blending and visual analytics solution that spans multiple business groups has helped Sager Creek enhance business insight.

Speakers: 

Dan Boyce, Sr. Director Business Intelligence and S&OP -- Sager Creek Vegetable Company
Maimoona Block, Alliance Manager -- Alteryx 
​Joseph Portello, Sr. Manager Systems Integration -- Sager Creek Vegetable Company
Michael Ruiz, Solutions Architect -- Amazon Web Services

Hosted by: Bill Vorhies, Editorial Director -- Data Science Central

Register here

WEBINAR: Retailers: Increase Conversions – and Revenue – with Data-Driven Product Recommendations - 29 October 2015

Summary

Are your product recommendations only slightly better than a shot in the dark? Gartner predicts that, by 2018, organizations that excel in personalization will outsell companies that don’t by 20%.

Register for this webinar to learn how you can leverage advanced analytic techniques to create product recommendations and next-best-offers that personalize the shopping experience for your customers while driving increased conversions and revenue for your business.

You’ll learn how you can:

• Incorporate multiple sources of customer data, including in-store and online purchase histories.
• Make recommendations based on product affinity, customer purchase history, and purchases made by similar customers.
• Transparently leverage multiple analytic techniques – and even combinations of those techniques – with no coding.
• Operationalize insights and visualize results with Teradata Aster AppCenter, business intelligence tools, and other solutions.

Register today!

Speakers

Ryan Garrett
Sr. Business Development Manager, Field Applications
Teradata Aster

Ryan Garrett has nearly 10 years of experience in big data analytics and has worked on many analytics initiatives with a focus on customer acquisition and retention. As part of the Teradata Aster Center of Innovation (COI) team, he is responsible for the delivery of analytics applications that address real world use cases across a variety of industries. Ryan holds an MBA from Boston University.

Matt Mazzarell
Data Scientist, Field Applications
Teradata

Matt is a Data Scientist in the Teradata Big Data Practice with experience implementing analytic solutions for many clients in the Fortune 500 who seek to solve business problems using large and varied data sets. Matt first joined Teradata as a Professional Services consultant where he engaged in many successful projects with GE Healthcare, AT&T, Wells Fargo and others.

Leslie Dinham (Moderator)
Senior Retail Business Consultant, xCommerceBusiness Analytics Consulting COE
Teradata

Leslie has deep and diverse experience in retail operations including marketing, loyalty programs, ecommerce, merchandising, logistics and information technology and is focused on helping retailers improve their business through analytics. She works with retailers to solve omni-channel and digital business issues such as a single view of the customer, localized assortments, understanding the path of customer interaction across all channels of engagement (website, call center, store, and social), improving on-site search, optimizing marketing investments through attribution models, and creating personalized interactions with customers at the right time and in the right channel. In addition, she has extensive experience with business process design/improvement, transformation, change management and integrating new analytic capabilities.

Register here

Mainframes and Core Systems: Together Forever? via @infomgmt

Mainframe hardware, meanwhile, shows no signs of going away. SHARE, the mainframe user group founded back in 1955, is still going strong with more than 20,000 members from nearly 2,000 organizations. And nine of today’s 10 top life and health insurers currently process their high-volume transactions on mainframes.

Great article from Information Management.

Data Science for Losers Part 2 via @brakmic

In this part 2 and the associated addendum Harris is going into more analysis of the data using Python and Pandas using pivot tables.  Very useful although being more SQL than Python compliant I kind of mourn the loss of using SQL.  I guess this makes me pull my other skills more up to scratch.

You can find the blog article for part 2 here and the addendum here.

Data Science for Losers Part 1 via @brakmic

A great starting point on coding needed for Data Science - this time starting with iPython and Pandas. I have to agree with his secret - I was never that great at maths at school either ;-)  Find his blog post here.

Data Science Skills and the Improbable Unicorn via @bobehayes

Finding a data professional who is proficient in all data science skill areas is extremely difficult. As this study shows, data professionals rarely possess proficiency in all five skill areas at the level needed to be successful at work.

Great article by Bob Hayes.

WEBINAR: Don’t Be Frightened by Moving to NoSQL - 22 October 2015

Sponsored by

Don’t Be 

Frightened by Moving to 

NoSQL

Thursday, October 22, 2015
10:00am PT | Show Timezones...

Presented by: Peter Milne

Duration: Approximately 60 minutes.

Cost: Free

Register here

Top Three Best Practices for Data Integration Deployment via @BigData_Review

According to the IDC, data stored in enterprise applications is expected to grow by 50 percent each year to 40 zettabytes by 2020. Here are three best practices for Data Integration from Solutions Review.

A good reminder of what should be considered when thinking about your own data.

k-Fold Cross Validation made simple via @AnalyticsVidhya

Are you aware of the basic principle behind K-Fold cross validation - for reducing selection bias significantly and validating the model to be not over-fit. Here's a guide with codes in R & Python to help you with it which ties in nicely with Kaggle and competition entries on there.

Very useful to read and follow.

WEBINAR: How to Hook Up Your Event Data for Behavioural Insights 22 October 2015

How to Hook Up Your Event Data for Behavioral Insights

Tinder discusses strategies for behavioral analytics and how they can be applied at your company to increase conversion, improve engagement, and maximize retention.

---

 

Thursday, October 22nd at 10 am PST, 1 pm EST

Every day your customers are talking to you in the most dependable way, their behavior in your product or service. Understanding these behaviors takes a special approach, especially when looking at massive amounts of event data.

Tinder VP of Technology Dan Gould will cover how Tinder re-invented its behavioral analytics approach with Interana to tune matchmaking and business operations. ESG Senior Analyst Nik Rouda will discuss the broader benefits of behavioral analytics on event data with best practices and industry research.

The webinar will cover topics such as:

• Why a solution built for speed and scale (trillions of events in seconds) is so important to Tinder and the insights it's enabling.
• How Tinder uses behavioral analytics to understand their users to improve features and develop new ones such as Super Like.  
• Why simple and interactive access to behavioral insights is critical to a successful strategy - Tinder enabled departments like product and marketing to use data every day.
• Tinder's struggles with other solutions like Hadoop and why they couldn't support their scale or analytics needs.

Register here

Getting started with Julia – a high level, high performance language for computing via @AnalyticsVidhya

A complete package in making - exploring its potential & wide applications through this overview, its promising enough language to become most preferred by the Data Scientists/Analysts.

Great blog from AnalyticsVidhya.

The deception that lurks in our data-driven world via @thisisfusion

Think data doesn't lie? Here's a thoughtful exploration of why we should all rethink that.

Very interesting article which makes you stop and think from Fusion.  Well worth a read when you have the time to sit and think.

Cloud Startups Take On Amazon, Microsoft Azure, Google via @infomgmt

Cloud startups such as DigitalOcean and Backblaze are beginning to compete for customers with the likes of Amazon.com, Microsoft and Google.

It's great to see the market starting to open up - gives the customer more choice.  Interesting article from Information Management

9 Steps to Become a Data Scientist from Scratch via @DataScienceCtrl @BernardMarr

Great blog in Data Science Central from Bernard Marr giving some steps on how to become a Data Scientist.  I agree with him - everyone who wants to become one should join kaggle.com which in itself is a test as you have to pass a test to show you are a Data Scientist.  It's a great source of competitions and data to start to learn and grow as a Data Scientist.

How Big Data Slashed Raises for Most Employees via @datajustice1 @HuffPostBiz

The guaranteed annual raise is increasingly a thing of the past - one reason wages have stagnated overall in recent decades.  Read this great blog from the Huffington Post by Nathan Newman.

I can quite imagine that this is al true as it's quite believable.

Specialized and hybrid data management and processing engines via @OReillyMedia

Ben Lorica describes a new crop of interesting solutions for the complexity of operating multiple systems in a distributed computing setting.

Interesting summary from Ben which can be used to create a list of what you are going to go and find out more about.

Graph Databases and the Connections in Data via @Data_Informed @emileifrem

Neo Technology CEO Emil Eifrem discusses the ability of graph databases to identify relationships in data and how companies are using this technology to generate value.

Definitely something you should be looking at and investigating I think of them as a new generation of database that we all need to learn, know and eventually love as we see the benefits.

IT Spending on Mobile Goes Vertical via @infomgmt

When it comes to mobile IT budgets, the areas of greatest growth include personal and consumer services, media, and the banking industries. according to new research.

Interesting summary of an IDC report from Information Management.

WEBINAR: So Much Data, So Little Time: Easier Data Prep and Analysis for Data Scientists - 20 October 2015

10/20 – So Much Data, So Little Time: Easier Data Prep and Analysis for Data Scientists

Data is growing quickly and every organization struggles with information overload. To make sense of all of the data and manage all of the requests for analysis, data scientists need tools that make the process easier, repeatable, shareable and understandable by others within the organization. In this webinar, we will demonstrate tools from Rapid Insight that make the data preparation and analysis process significantly faster, without losing the flexibility of advanced programming or SQL tools.

Register here

Cheatsheet – 11 Steps for Data Exploration in R (with codes) via @AnalyticsVidhya

If you are aiming towards building an impeccable predictive model, then nothing but Data Exploration can be highly rewarding. Get accustomed to Data Exploration before Algorithms. And to do this, AnalyticsVidhya present to you a cheatsheet of 11-step to Data Exploration in R explained with codes.

Great cheatsheet and well worth having.

SLIDESHOW: Top 11 Business Intelligence Software Companies via @onfomgmt

What are the leading business intelligence (BI) software companies? Forrester Research evaluated vendors against 60 criteria – which were groups into three high-level buckets: (1) Strength of current offering; (2) overall strategy and (3) market presence. Here's a recap of the findings from Information Management.

Where Should I Work In Big Data? via @datafloq @bigcloudteam

As a recruiter, I get asked this question a lot, and it is a question that is on the lips of people across many industries at the moment. Should I work in a Big Data start-up or a more established Big Data vendor? The answer is not that easy, but there are five aspects you could take into consideration.

Great points to try and help navigate the Big Data job market by Matt Reaney of Big Cloud.

Where Is The Real Value in Big Data? via @datafloq @ianacepete

Taking a look at all the activity related to Big Data one should ask the question, how much of Big Data is actually useful. By applying just a little common sense we discover only a small amount of all the data we create today is actually useful. But what should organisations do to create real value with Big Data?

Very interesting article by Pete Ianace on Datafloq.  I kind of think of what he is describing as a XML structure for the data and his example at the end could be a query against the XML.  Either way it is vitally important that we do all this work for Big Data with the goal of making the data available and useful - we can then find ways to understand those uses and start to give value.

Topological Analysis and Machine Learning: Friends or Enemies? via @KDnuggets

What is the interaction between Topological Data Analysis and Machine Learning ? A case study shows how TDA decomposition of the data space provides useful features for improving Machine Learning results.

Interesting article and well worth a read by Harlan Sexton (Ayasdi) in KDnuggets

How to get the most out of Massive Open Online Courses (MOOCs)?via @AnalyticsVidhya

You MUST ask yourself these questions and set an agenda before picking up Massive Open Online Courses (MOOCs). With not the same strategy to go for, everyone must have their own way to get most out of these.

Read it here on AnalyticsVidhya.

I have to agree with some of the observations - having done some myself it's not enough to do just enough to pass. You also won't earn much let alone remember it.

Updated BI Buyers Matrix Report Now Includes Four New Vendors via @BigData_Review

Solutions Review is thrilled to announce our first major update to the Business Intelligence Buyers Matrix Report!

Read it here

Beginner’s guide to Design of Experiments (with case study on banner advertisement) via @AnalyticsVidhya

An interesting elaboration on how companies develop their "design of experiments" to analyse customer requirements and accordingly coming up with marketing strategies for maximum customer likings.

Brilliant article on AnalyticsVidhya explaining how marketing experiments are designed and how to measure them.  Well worth a read.

Big Data and Hadoop Essentials Online Course

Course Description

Are you interested in the world of Big data technologies, but find it a little cryptic and see the whole thing as a big puzzle.

Are you looking to understand how Big Data impact large and small business and people like you and me?

Do you feel many people talk about Big Data and Hadoop, and even do not know the basics like history of Hadoop, major players and vendors of Hadoop. Then this is the course just for you!

Join here

Where's the Money In Data (Part III) via @infomgmt

The value in the delivery of new data products and services must be so obvious and compelling that customers can’t afford not to take action.

Link to the article here The article contains links to parts 1 and 2 and 4 and 5 are still to come.

15 Mathematics MOOCs for Data Science via @KDnuggets

If you want to learn Data Science or want to brush up your maths this great list from KDnuggets points you to some courses available on MOOCs.

Apache Spark vs. Hadoop MapReduce via @Intersog

The new Apache Spark has raised a buzz in the world of Big Data. It promises to be more than 100 times faster than Hadoop MapReduce with more comfortable APIs, which begs the question: could this be the start of the end for MapReduce?

Great article by Jenny Richards on Intersog.  At the moment there is a place for both as it really does depend exactly what you want to do.  Over time that might change but for now there is definitely a place for them both.

Three recent articles from @infomgmt on the subject of Amazon and BI or Cloud

Amazon Will Disrupt Business Intelligence, Analytics Markets blog post.

Get ready for AWS business intelligence (BI): it's real and it packs a punch!

Amazon Expands Big Data Cloud Services with Quicksight article

Amazon Web Services unveiled Quicksight and other products that give businesses new ways to transfer, manipulate and derive insights from data they store in the company’s cloud.

Amazon-Accenture Deal Threatens IBM, HP in Tech Services article

Amazon.com Inc. and consulting giant Accenture Plc are teaming up to provide cloud-based technology services to businesses, catapulting the Web retailer further into territory long dominated by companies such as HP and IBM.

Interesting three articles which together show just how serious Amazon are at moving into the Big Data/BI/Cloud markets,  Some of it makes sense as they have to use those skills within their own business, but I do worry if this is a stretch too far.  I guess only time will tell.

Why Bad Data is Wasting Your Marketing Efforts via @Datafloq

Businesses are starting to realize that data quality is worthy of more serious investment. 81 per cent believe good data is key to marketing success. Marketers need high quality, complete, valid, and accurate data to make good quality decisions, in order to avoid the need to scrap a project and start again from scratch. The more data we have the more expensive it is to put off the data quality initiatives we need.

Read the article here on Datafloq.

I have to agree.  Garbage data gives garbage results.  Even worse - you waste valuable money on campaigns that do not give the return it should.  Also remember we've all felt annoyance at getting unwanted mail from an organisation. Why risk reputation damage with the recipient when if you only concentrated some effort on the quality of the data you could have saved all of that.

How to Retain Your Existing Customers with the Right Data via @Datafloq

Customer acquisition strategies get huge attention – after all, new customers are the lifeblood of any business. However, you have probably heard the stats before - it costs six to seven times more for an organization to acquire a new customer than to keep an existing one. So while you should never lose focus on acquiring new customers, don’t forget about the ones you currently have and Big Data can help you with that.

Read about it here on Datafloq.

WEBINAR: 5 Keys to a Winning Digital User Experience - 14 October 2015

5 Keys to a Winning Digital User Experience  Live Webinar Featuring Forrester Research DATE: Wednesday October 14, 2015 TIME: 11AM EST

Your users expect a high quality experience across all digital channels. Are you ready to develop and deliver it? Join guest speaker, Forrester Research’s Julie Ask (An author of The Mobile Mind Shift), and Perfecto president, Rainer Gawlick, discuss what it takes to deliver a winning digital user experience, including: How to align your organization's dev/test practices to deploy with confidence How to leverage quality practices to engage users How to inject real end-user conditions into your development process

Register here

WEBINAR: First in Class: Optimizing the Data Lake for Tighter Integration - 13 October 2015

Date and time:	Tuesday, October 13, 2015 3:00 pm Central Daylight Time (Chicago, GMT-05:00) Change time zone
	Tuesday, October 13, 2015 1:00 pm Pacific Daylight Time (San Francisco, GMT-07:00)
Duration:	1 hour
Description:	The Briefing Room with Dr. Robin Bloor and Teradata RainStor Hadoop data lakes are emerging as peers to corporate data warehouses. However, successful data management solutions require a fusion of all relevant data, new and old, which has proven challenging for many companies. With a data lake that’s been optimized for fast queries, solid governance and lifecycle management, users can take data management to a whole new level. Register for this episode of The Briefing Room to learn from veteran Analyst Dr. Robin Bloor as he discusses the relevance of data lakes in today’s information landscape. He’ll be briefed by Mark Cusack of Teradata, who will explain how his company’s archiving solution has developed into a storage point for raw data. He’ll show how the proven compression, scalability and governance of Teradata RainStor combined with Hadoop can enable an optimized data lake that serves as both reservoir for historical data and as a "system of record” for the enterprise. Host: Eric Kavanagh  CEO The Bloor Group Analyst: Robin Bloor  Chief Analyst The Bloor Group Guest: Mark Cusak  Chief Architect Teradata RainStor

Register here

WEBINAR: Data Science Driven Approaches to Malware Detection - 13 October 2015

Overview

Title: Data Science Driven Approaches to Malware Detection

Date: Tuesday, October 13, 2015

Time: 09:00 AM Pacific Daylight Time

Duration: 1 hour

Summary

Data Science Driven Approaches to Malware Detection

Malware detection within enterprise networks is a critical component of an effective information security strategy. A "watering hole" attack is one example of how legitimate websites can be stealthily injected with malware. The malware lies undetected, while redirecting traffic from a legitimate site to a malicious site, which hosts an exploit kit that can compromise users' machines. Instances of watering hole attacks are increasing rapidly -- making them especially important to detect. 

In this DSC webinar, one of Pivotal's principal data scientists will discuss data science driven approaches to finding domains that have time and user-based co-occurrence relationships. Developed to find low-support and high-confidence malicious domain associations, these methods aid in the detection of coordinated network intrusions, like watering hole attacks. The session will also demonstrate a scalable and operationalisable framework to detect domain associations by analysing the web traffic of users in any organization. 

Speaker: Anirudh Kondaveeti, Ph.D. and Principal Data Scientist -- Pivotal

Hosted by: Bill Vorhies, Editorial Director -- Data Science Central

Register here