Sat.Sep 07, 2019 - Fri.Sep 13, 2019

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13 Analytics & Business Intelligence Examples Illustrating The Value of BI

datapine

Digital data, by its very nature, paints a clear, concise, and panoramic picture of a number of vital areas of business performance, offering a window of insight that often leads to creating an enhanced business intelligence strategy and, ultimately, an ongoing commercial success. Business intelligence steps up into this process by creating a comprehensive perspective of data, enabling teams to generate actionable insights on their own.

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There is No Free Lunch in Data Science

KDnuggets

There is no such thing as a free lunch in life or data science. Here, we'll explore some science philosophy and discuss the No Free Lunch theorems to find out what they mean for the field of data science.

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A Data Scientist’s Guide to 8 Types of Sampling Techniques

Analytics Vidhya

Overview Sampling is a popular statistical concept – learn how it works in this article We will also talk about eight different types of. The post A Data Scientist’s Guide to 8 Types of Sampling Techniques appeared first on Analytics Vidhya.

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Understanding deep neural networks

O'Reilly on Data

The O’Reilly Data Show Podcast: Michael Mahoney on developing a practical theory for deep learning. In this episode of the Data Show , I speak with Michael Mahoney , a member of RISELab , the International Computer Science Institute , and the Department of Statistics at UC Berkeley. A physicist by training, Mahoney has been at the forefront of many important problems in large-scale data analysis.

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State of AI in Sales & Marketing 2025

AI adoption is reshaping sales and marketing. But is it delivering real results? We surveyed 1,000+ GTM professionals to find out. The data is clear: AI users report 47% higher productivity and an average of 12 hours saved per week. But leaders say mainstream AI tools still fall short on accuracy and business impact. Download the full report today to see how AI is being used — and where go-to-market professionals think there are gaps and opportunities.

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Interview with: Nadeem Asghar and Cindy Maike at Cloudera

Corinium

Tell us about your experience in working with the data analytics community at Cloudera? Why do you like working in this space?

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Many Heads Are Better Than One: The Case For Ensemble Learning

KDnuggets

While ensembling techniques are notoriously hard to set up, operate, and explain, with the latest modeling, explainability and monitoring tools, they can produce more accurate and stable predictions. And better predictions can be better for business.

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Conversational Computing and More News from Oracle Analytics Summit 2019

David Menninger's Analyst Perspectives

The Oracle Analytics Summit 2019 was the inaugural user event for Oracle Analytics customers, and they also broadcast the video for thousands of others. You can watch the keynote at [link]. Executives talked about some big organizational changes, including Bruno Aziza joining last year to lead the analytics organization. This event marked a transition and "a new beginning" for the Oracle Analytics portfolio, as the company announced three new analytics products.

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Interview with: Dr. Mike Kim, CTO and Co-Founder of Outlier.ai

Corinium

Tell us about your experience in working with the data analytics community at Outlier? Why do you like working in this space? From the very beginning of Outlier, I have really enjoyed collaborating with others in the data analytics community. Data.

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Train sklearn 100x Faster

KDnuggets

As compute gets cheaper and time to market for machine learning solutions becomes more critical, we’ve explored options for speeding up model training. One of those solutions is to combine elements from Spark and scikit-learn into our own hybrid solution.

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4 Key Aspects of a Data Science Project Every Data Scientist and Leader Should Know

Analytics Vidhya

Overview A data-science-driven product consists of multiple aspects every leader needs to be aware of Machine learning algorithms are one part of a whole. The post 4 Key Aspects of a Data Science Project Every Data Scientist and Leader Should Know appeared first on Analytics Vidhya.

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How to Achieve High-Accuracy Results When Using LLMs

Speaker: Ben Epstein, Stealth Founder & CTO | Tony Karrer, Founder & CTO, Aggregage

When tasked with building a fundamentally new product line with deeper insights than previously achievable for a high-value client, Ben Epstein and his team faced a significant challenge: how to harness LLMs to produce consistent, high-accuracy outputs at scale. In this new session, Ben will share how he and his team engineered a system (based on proven software engineering approaches) that employs reproducible test variations (via temperature 0 and fixed seeds), and enables non-LLM evaluation m

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Topics to watch at the Strata Data Conference in New York 2019

O'Reilly on Data

Machine learning, artificial intelligence, data engineering, and architecture are driving the data space. The Strata Data Conferences helped chronicle the birth of big data, as well as the emergence of data science, streaming, and machine learning (ML) as disruptive phenomena. Strata attracts the leading names in the fields of data management, data engineering, analytics, ML, and artificial intelligence (AI).

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The Role of Big Data In The Maintenance Industry

Smart Data Collective

As industry buzzwords, “Big Data” is one of those phrases that has become seemingly ubiquitous. Everyone wants to be using big data to better their operation. The maintenance department is no exception to this trend. Accordingly, maintenance teams are beginning to embrace the use of big data and analytics to improve performance. In emphasizing the use of “big data”, maintenance can establish predictive maintenance programs, which reduce downtime and save on maintenance costs.

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Classification vs Prediction

KDnuggets

It is important to distinguish prediction and classification. In many decision-making contexts, classification represents a premature decision, because classification combines prediction and decision making and usurps the decision maker in specifying costs of wrong decisions.

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WNS Analytics Wizard 2019: Top 3 Winners’ Solutions from our Biggest Data Science Hackathon

Analytics Vidhya

Overview Here’s a unique data science challenge we don’t come across often – a marketing analytics hackathon! We bring you the top 3 inspiring. The post WNS Analytics Wizard 2019: Top 3 Winners’ Solutions from our Biggest Data Science Hackathon appeared first on Analytics Vidhya.

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Zero Trust Mandate: The Realities, Requirements and Roadmap

The DHS compliance audit clock is ticking on Zero Trust. Government agencies can no longer ignore or delay their Zero Trust initiatives. During this virtual panel discussion—featuring Kelly Fuller Gordon, Founder and CEO of RisX, Chris Wild, Zero Trust subject matter expert at Zermount, Inc., and Principal of Cybersecurity Practice at Eliassen Group, Trey Gannon—you’ll gain a detailed understanding of the Federal Zero Trust mandate, its requirements, milestones, and deadlines.

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How a Baby Boomer Became a Data Scientist at 60

DataCamp

You can become a data scientist at any age if you’re willing to put in the work.

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How Big Data Is Transforming Social Media Marketing

Smart Data Collective

Big Data is among one of the most impressive tech advancements that have hit the marketing world in recent memory. While it has been tossed around as a buzzword in certain circles, Big Data is so much more than just a phrase. For a definition , Oracle recommends Gartner’s 2001 description of Big Data, which describes it as data containing a greater variety, getting to the source in increasing volume and at ever-higher velocity.

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The 5 Graph Algorithms That Data Scientists Should Know

KDnuggets

In this post, I am going to be talking about some of the most important graph algorithms you should know and how to implement them using Python.

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Power BI vs. Tableau: Self-service analytics tools compared

CIO Business Intelligence

Business intelligence (BI) and analytics platforms have long been a staple for business, but thanks to the rise of self-service BI tools, responsibility for analytics has shifted from IT to business analysts, with support from data scientists and database administrators.

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Revolutionize QA: GAPs AI-Driven Accelerators for Smarter, Faster Testing

GAP's AI-Driven QA Accelerators revolutionize software testing by automating repetitive tasks and enhancing test coverage. From generating test cases and Cypress code to AI-powered code reviews and detailed defect reports, our platform streamlines QA processes, saving time and resources. Accelerate API testing with Pytest-based cases and boost accuracy while reducing human error.

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Exponential Organizations Start with Internal Business Process Modeling

erwin

Strong internal business process modeling and management helps data-driven organizations compete and lead. In short, an internal business process is a documented account of how things should be done to maximize efficiency and achieve a particular goal. In the book “Exponential Organizations” by Salim Ismail, Michael S. Malone and Yuri van Geest , the authors, examine how every company is or will evolve into an information-based entity in which costs fall to nearly zero, abundance replaces scarci

Modeling 101
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5 Reasons Why You Should Store Big Data In The Cloud

Smart Data Collective

Gone are the days when storage of information can only be done with the traditional remote servers which are located in a secluded location. Today, the in-thing is cloud data storage where information and data are stored electronically online. With this approach, you can store unlimited data online (in the cloud) and access it anywhere. Several essays and many articles have been written on storage clouds and benefits of the cloud , but this piece puts forward five of the biggest benefits that yo

Big Data 100
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10 Great Python Resources for Aspiring Data Scientists

KDnuggets

This is a collection of 10 interesting resources in the form of articles and tutorials for the aspiring data scientist new to Python, meant to provide both insight and practical instruction when starting on your journey.

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Semantic Search or Knowing Your Customers So Well, You Can Finish Their Sentences For Them

Ontotext

My great grandparents were married for more than sixty years. They had a telepathy that comes from living together that long. They could finish each other’s sentences or anticipate when one wanted a coffee or lunch. An exchange could go like this. ‘Remember those figs?’ Grandma said. ‘You’re talking about that holiday to France in ’76 and it wasn’t figs, it was apples.’.

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The GTM Intelligence Era: ZoomInfo 2025 Customer Impact Report

ZoomInfo customers aren’t just selling — they’re winning. Revenue teams using our Go-To-Market Intelligence platform grew pipeline by 32%, increased deal sizes by 40%, and booked 55% more meetings. Download this report to see what 11,000+ customers say about our Go-To-Market Intelligence platform and how it impacts their bottom line. The data speaks for itself!

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How Artificial Intelligence & Deep Learning Change the Game

Teradata

AI & Deep Learning allow organizations to maximize player performance while minimizing player risk through better insights from performance and wellness data.

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AI Drives The Inception Of Three Cutting-Edge Smart Home Products

Smart Data Collective

Artificial intelligence is coming to our homes. A growing number of people use smart devices that are developed with state-of-the-art AI technology. The market for smart homes is going to rise as new AI advances bring big changes to the industry. One survey from last year found that only 12-16% of homes in the United States are equipped with smart devices.

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Scikit-Learn vs mlr for Machine Learning

KDnuggets

How does the scikit-learn machine learning library for Python compare to the mlr package for R? Following along with a machine learning workflow through each approach, and see if you can gain a competitive advantage by knowing both frameworks.

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The Perceptual and Cognitive Limits of Multivariate Data Visualization

Perceptual Edge

[Note: To make it easy for you to read this article offline and to share it with others, I’ve made a PDF version available as well.]. Almost all data visualizations are multivariate (i.e., they display more than one variable), but there are practical limits to the number of variables that a single graph can display. These limits vary depending on the approach that’s used.

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Optimizing The Modern Developer Experience with Coder

Many software teams have migrated their testing and production workloads to the cloud, yet development environments often remain tied to outdated local setups, limiting efficiency and growth. This is where Coder comes in. In our 101 Coder webinar, you’ll explore how cloud-based development environments can unlock new levels of productivity. Discover how to transition from local setups to a secure, cloud-powered ecosystem with ease.

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How to overcome the top 3 AI challenges using data management

IBM Big Data Hub

Artificial intelligence and machine learning (ML) have become very popular recently due to their ability to both optimize processes and provide the deep insights that push enterprises and industries forward.

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Deep Learning Tools Could Compound Returns on Technical Analysis Trading

Smart Data Collective

. Artificial intelligence is upending the financial management industry in spectacular ways. The majority of machine learning and deep learning solutions have focused on fundamental analysis of securities. However, deep learning and other artificial intelligence technologies will also change the future of technical analysis as well. A number of experts have started analyzing the role of AI in technical analysis.

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Common Machine Learning Obstacles

KDnuggets

In this blog, Seth DeLand of MathWorks discusses two of the most common obstacles relate to choosing the right classification model and eliminating data overfitting.

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The Insights Beat: Plan For New Data & Analytics Supplies

Srividya Sridharan

Summer’s lease hath all too short a date. It always seems to pass by in the blink of an eye, and this year was no exception. Though I am excited for cooler temperatures and the prismatic colors of New England in the fall, I am sorry to see summer come to an end. The end […].

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15 Modern Use Cases for Enterprise Business Intelligence

Large enterprises face unique challenges in optimizing their Business Intelligence (BI) output due to the sheer scale and complexity of their operations. Unlike smaller organizations, where basic BI features and simple dashboards might suffice, enterprises must manage vast amounts of data from diverse sources. What are the top modern BI use cases for enterprise businesses to help you get a leg up on the competition?