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10 use-cases for privacy-preserving synthetic data
Fast-evolving data protection laws are constantly reshaping the data landscape. The organizational ability to overcome sensitive data usage restrictions while safeguarding customer privacy will be a key driver of tomorrow’s successful businesses. This blog presents ten concrete applications for privacy-preserving synthetic…Continue
Added by Elise Devaux on August 5, 2020 at 6:59am — No Comments
This blog takes a closer look at the concept of privacy-preserving synthetic data. It answers the question “what is synthetic data” and looks at the origin of synthetic data in the context of data privacy. It also presents one way of generating privacy-preserving synthetic data and its benefits for organizations.…Continue
Added by Elise Devaux on July 2, 2020 at 11:30am — No Comments
As I learn about data privacy, I’m starting to realize how large the ecosystem is. I focused here on a category that spans across the data privacy landscape, Privacy Enhancing Technologies (PETs). In the post, I cover:…
Added by Elise Devaux on June 12, 2020 at 2:14am — No Comments
Added by Elise Devaux on May 23, 2020 at 1:00pm — No Comments
Python is an extremely popular programming language. It is not just apt for generic purposes but it is extremely easy to read and use as well. The main reason why Python is used by a majority of people these days is the fact that it allows the programmers to save their time by using only limited lines of codes. In order to accomplish tasks, the developers do not have to spend a lot of time on coding, unlike the other languages. Rather, all they can do is, spend time on…Continue
Added by Divyesh Aegis on July 25, 2019 at 12:53am — No Comments
From detecting anomalies to understanding what are the key elements in a network, or highlighting communities, graph analytics reveal information that would otherwise remain hidden in your data. We will see how to integrate your graph analytics with Linkurious Enterprise to detect and investigate insights in your connected data.
Added by Elise Devaux on October 4, 2018 at 9:30am — No Comments
There is no need to get confused with multiple linear regression, generalized linear model or general linear methods. The general linear model or multivariate regression model is a statistical linear model and is written as Y = XB + U.
Usually, a linear model includes a number of different statistical models such as ANOVA, ANCOVA, MANOVA, MANCOVA, ordinary linear regression, t-test and F-test. The GLM is a generalization of multiple…
Companies and enterprises are facing a daily grind, while they are also required to see to it that their customers are happy & satisfied, operations is efficient and employees are satisfied; and all this makes running the business – a real challenge. “Audience is the new business model”, and if any organization is struggling miserably to communicate with customers or their audience, there certainly is a negative impact of it across the business plan,…Continue
Added by Chirag Shivalker on September 26, 2017 at 12:30pm — No Comments
ECommerce fraud is growing quickly, creating new challenges in terms of prevention and detection. As merchants gather more and more information about customers and their behaviors, the key element in the fight against fraud is now to draw on the connections within the data collected to uncover fraudulent behaviors. In this post we explain why and how graph technologies are crucial in the detection of eCommerce fraud.…
Added by Elise Devaux on August 9, 2017 at 9:30am — No Comments
Businesses across the globe are facing the brunt, one of huge data influx and second of increasing data complexity and of course the market volatility. To address these challenges, companies and all their verticals are turning to data-driven analytics and insights as a means to better understand their organization’s customer bases and to grow their businesses; and manage the increasing uncertainty up to a certain extent.
The shift from conventional…Continue
Added by Chirag Shivalker on June 22, 2017 at 1:30pm — No Comments
It has been a practice followed religiously by companies and organizations to analyze how they have performed over a period of time. It is mandatory for them to do so; just that some do it to survive and some do it to thrive in concurrent…Continue
Added by Chirag Shivalker on May 12, 2017 at 7:30am — No Comments
Many businesses, especially small businesses, underestimate the danger their company’s data is in. They have the idea that because they’re fairly small, no one would want to try to steal the customer information they collect. After all, why go after a few thousand customer records when you could attack a large corporation and potentially walk away with tens of…Continue
Added by Peter Davidson on April 17, 2017 at 6:00am — No Comments
Traditional computer systems and software applications don’t have what it takes to support big data. If you want to collect, store, refine, or analyze big data, you have to have the right tools. Check out the following ten tools that are specifically designed with big data in mind.
If you know, or are willing to…Continue
One of my favorite things over the year was starting a personal blog. (You can find my website here if you are curious.) How did it happen? Well, I was reading an article and one quote in particular really struck me: "it's not what you know it's who you know".
That quote really resonates with me. Throughout my life I’ve learned a lot, and…Continue
Added by Olga on November 2, 2016 at 9:03pm — No Comments
Most people think data science is smart people doing very smart stuff. Well that’s not it. Data science is just another subject involving its own bit of subtle complexities that has to be handled with knowledge and an innovative approach. JUST LIKE COOKING.
Cooking is art and science. So is Analytics. Both start from getting the right ingredients. No matter how many spices and cooking techniques you apply, the dish won’t…Continue
Added by Vivek Kalyanarangan on November 1, 2016 at 10:00am — No Comments
"Information is the oil of the 21st century, and analytics is the combustion engine" Peter Sondergaard, SVP, Gartner Research
In analytics, we retrieve information from various data sources; it can be structured or unstructured. The biggest challenge here is to retrieve information from unstructured data mainly texts. Here machine learning comes into the picture to overcome this challenge. Different algorithms have been designed in different platforms…Continue
Added by Vivek Kalyanarangan on September 9, 2016 at 8:30am — No Comments
R is widely used among scientists and statisticians to perform statistical analysis while Salesforce.com is one of the leading CRM software packages used for Marketing and Salesforce automation. Salesforce.com contains vital information regarding Leads, Customers, Contacts, Opportunities and Cases. Currently this data is mainly used for operational purposes by Sales and Marketing professionals.
How about using Salesforce CRM data for predictive analysis or…
Added by Pradip Banerjee on August 3, 2016 at 8:00pm — No Comments
By Dan Kellett, Director of Data Science, Capital One UK
Disclaimer: This is my attempt to explain some of the ‘Big Data’ concepts using basic analogies. There are inevitably nuances my analogy misses.
What is HDFS?
When people talk about ‘Hadoop’ they are usually referring to either the efficient storing or processing of large amounts of data. MapReduce is a framework for efficient processing using a parallel, distributed algorithm…Continue
Added by Dan Kellett on July 21, 2016 at 2:00am — No Comments
Telecommunications is complex business. As users we are mostly faced with one question- how to get the best deal out of the many telecom players in the market? But as an analyst in a Telecom company one may be faced with any one or more of the following question -
Added by Ivy Pro School on July 15, 2016 at 3:00am — No Comments
The big data blast has given rise to a host of information technology software and tools…Continue
Added by marry tho on June 17, 2016 at 6:00am — No Comments