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How Data Warehousing Support in Faster Decision Making For Rapid Growth of Organisation?

The role of data is changing significantly with advanced methodologies used for upgrading sales and integrating business intelligence in the organisational schema for better outcomes. But in the absence of proper tools and techniques to manage and organise such a large pool of data, results might appear too far-fetched. So, what is the way out to handle bundles of unstructured data and harness its true potential for catalysing the growth? Data warehousing is the solution that…

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Added by PS Dhillon on January 21, 2020 at 10:30pm — No Comments

3 Reasons To Connect Data Silos To A CDP

In the current digital age in which we live, population data is of exceptional value. And so, when the data of a company's customers are in unconnected silos, they can be a barrier to success. Without the connection between this data, marketing experts can't offer customers what they expect. Do you want to know more about the reasons for connecting data silos to a CDP? Then, keep reading! 

Need for connected…

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Added by Sandeepkumar on January 21, 2020 at 1:00pm — No Comments

Data Science: Do We Really Need Math?

It is sometimes said that you don't need to know math to be a data scientist. Sometimes the opposite is said, after all, data science is supposed to be a science! Regardless, below are a few of my articles featuring how data science and math can benefit from each other - not just math to solve data science problems, but also data science to solve math problems.

Articles…

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Added by Vincent Granville on January 21, 2020 at 12:58pm — 1 Comment

Kubernetes in 10 minutes

Kubernetes is a technology that allows us to isolate an application. That is good but how can we scale this? Of course, we should create new containers.

But, how many containers should we have at the same time? How many…

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Added by Igor Bobriakov on January 21, 2020 at 7:53am — No Comments

Does "All models are wrong, but some are useful" quote apply to Machine learning models?

Models are simplification or approximation of reality and hence they will not capture all of reality. “All models are wrong, but some are useful” is a famous quote by George Edward Pelham Box (1919–2013). George Box was a British mathematician and professor of statistics at the University of Wisconsin. Statisticians develop theoretical models to predict the behaviour of certain process. The meaning of this quote is that every single model will be wrong and it never represents the exact…

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Added by Janardhanan PS on January 20, 2020 at 11:30pm — No Comments

Deploying machine learning models using Agile

In the previous post, ten strategies to implement ai on the cloud and edge, I discussed strategies for end to end deployment for machine learning modules.

 

How this relates to Agile?

 

Deployment of AI comes within the scope the normal SDLC (software development lifecycle)

So, normal Agile techniques like scrum, sprints, backlog…

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Added by ajit jaokar on January 20, 2020 at 1:06pm — No Comments

Just How Much Do You Trust Your AI?

Summary:  Just how much should you trust your AI systems?  Best practice points to constant review, strong governance, and the willingness to override results that seem illogical.

 

Just how much do you trust your AI?  This is not intended to be the skeptical consumer view about bias or black box outcomes. …

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Added by William Vorhies on January 20, 2020 at 8:26am — No Comments

Weekly Digest, January 20

Monday newsletter published by Data Science Central. Previous editions can be found here. The contribution flagged with a + is our selection for the picture of the week. To subscribe, follow this link.  …

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Added by Vincent Granville on January 19, 2020 at 4:30pm — No Comments

How Can Companies Get The Most Value Out Of Their Data

Having spent the better part of the last decade helping organisations in the U.S. and the U.K. use data to drive profit, efficiency, and performance improvements, I thought it would be helpful to jot down best practices for organizations that are looking to get the most value out of their enterprise-level financial and operational data sets.

Every organisation entering the roaring 20s knows that they must use their data as a strategic differentiator to gain a competitive advantage and…

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Added by Fahad Zaidi on January 19, 2020 at 6:00am — No Comments

Business Analytics vs Data Analytics in One Picture

In my previous post, I discussed the differences between Business Intelligence and Business Analytics. Two other terms that are often confused are Business Analytics and Data Analytics, but they are actually quite separate entities. This one picture highlights the differences between the two…

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Added by Stephanie Glen on January 18, 2020 at 7:12am — No Comments

Why the Concept of “Citizen Data Scientist” Terrifies Me

Imagine this scenario:

You enter your dentist’s office as a follow-up visit to the 6-month checkup you had last week. You’re nervous because the checkup revealed bleeding in your gums and cracks in your fillings, all things that require your dentist’s repair on this occasion.

So, you’re there nervously fidgeting in the lobby waiting for your name to be called, envisioning all sorts of dentistry implements of torture that will soon be…

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Added by Bill Schmarzo on January 17, 2020 at 12:30pm — No Comments

AI in Transportation: Top 3 Real-World Cases

Artificial Intelligence is already impacting Manufacturing, Retail, Marketing, Healthcare, Food industries and more. Today we will take an in-depth look at another industry, that with proper AI expertise from development companies could be disrupted. 

Transportation is an industry that helps humanity with moving people their belongings from one location to the other.…

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Added by Roman Chuprina on January 17, 2020 at 3:30am — No Comments

HandWiki encyclopedia of datascience

In 2020, HandWiki has become the largest online wiki encyclopedia for major science topics (physics, math etc.) and computing. It has more than 105,000 scholarly articles, incorporating the current Wikipedia articles, scholarly articles submitted to the Wikipedia foundation (but later…

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Added by jwork.ORG on January 16, 2020 at 6:30pm — No Comments

So which is better for portfolio optimization, a NISQ Quantum Computer, or Fujitsu's "quantum inspired digital annealer"?

Quantum computers come in 2 varieties, quantum annealers and quantum gate models. So far, DWave is the only company selling quantum annealers.

A third type of device is now available from Fujitsu. Fujitsu calls its device a "quantum inspired digital annealer". The Fujitsu device does not have any quantum correlation (quantum entanglement)…

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Added by Robert R. Tucci on January 16, 2020 at 1:30pm — No Comments

Ten strategies to implement AI on the Cloud and Edge

Introduction

The deployment of Machine Learning and Deep Learning algorithms on Edge devices is a complex undertaking. In this post, I list the strategies for deploying AI to Edge devices end-to-end i.e. for the full pipeline covering machine learning (building modules) and deployment (devops)

I welcome your comments on additional ideas that could be included. In subsequent posts, I will elaborate these ideas in detail and…

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Added by ajit jaokar on January 16, 2020 at 11:30am — No Comments

Thursday News, January 16

Here is our selection of featured articles and technical resources posted since Monday:

Resources

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Added by Vincent Granville on January 16, 2020 at 11:00am — No Comments

Top Data Science Use Cases in HR

Data Science methods and techniques allow new approaching the solution of complex tasks in terms of mathematics, and statistics for the various aspects and areas of our life, work, and business. Therefore, this makes it possible to produce the most unobvious…

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Added by Igor Bobriakov on January 16, 2020 at 10:30am — No Comments

Importance of Hyper-parameters in Model development

Machine Learning (ML) development is an iterative process in which the accuracy of predictions made by the models is continuously improved by repeating the training and evaluation phases. In each of these iterations, certain parameters are tweaked continuously by developers. Any parameter manually selected based on learning from previous experiments qualify to be called a model hyper-parameter. These parameters represent intuitive decisions whose value cannot be estimated from data or from…

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Added by Janardhanan PS on January 16, 2020 at 12:00am — 1 Comment

Does Big Data Impact Business Mobile App Development?

This is a major contribution of analytics and big data when you talk about the success of mobile app development. Big Data is large data sets that can be analyzed to know more about the user’s interests, demographics, trends and interactions. We rely on mobile apps present on our phone for most of the tasks like a reminder, planner, health and fitness, etc.…

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Added by Veronica Hanks on January 15, 2020 at 8:43pm — No Comments

Blockchain for Fintech: now and tomorrow

Hello! This is my first post on Data Science Central, so please be patient. I've had a short Q&A session with our CTO Sergey Nemesh.

Q: What problems can Blockchain solve for the FinTech sector? 

A: The main global problem in the FinTech sector is trust providing as for financial operations it plays a crucial role. Blockchain provides trustless transactions which…

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Added by Valery Geldash on January 15, 2020 at 10:07am — No Comments

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