Sat.Nov 23, 2019 - Fri.Nov 29, 2019

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The Four Types of Chief Data Officers

Teradata

Every organization, in every industry, needs a Chief Data Officer. Use these guidelines to choose the one with the optimal skills and background for success.

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A Doomed Marriage of Machine Learning and Agile

KDnuggets

Sebastian Thrun, the founder of Udacity, ruined my machine learning project and wedding.

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Need a New Topic for Thanksgiving Dinner? How to Explain Artificial Intelligence (AI) to Anyone.and Make it Fun!

Bruno Aziza

Need a New Topic for Thanksgiving Dinner? How to Explain Artificial Intelligence (AI) to Anyone.and Make it Fun!

IT 85
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Top 10 Analytics And Business Intelligence Trends For 2020

datapine

Over the past decade, business intelligence has been revolutionized. Data exploded and became big. We all gained access to the cloud. Spreadsheets finally took a backseat to actionable and insightful data visualizations and interactive business dashboards. The rise of self-service analytics democratized the data product chain. Suddenly advanced analytics wasn’t just for the analysts. 2019 was a particularly major year for the business intelligence industry.

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The Path to Product Excellence: Avoiding Common Pitfalls and Enhancing Communication

Speaker: David Bard, Principal at VP Product Coaching

In the fast-paced world of digital innovation, success is often accompanied by a multitude of challenges - like the pitfalls lurking at every turn, threatening to derail the most promising projects. But fret not, this webinar is your key to effective product development! Join us for an enlightening session to empower you to lead your team to greater heights.

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Data Ethics, Culture and Advanced Analytics Top the Agenda at CDAO Fall

Corinium

Data ethics, data culture and the future of advanced analytics were the three themes that dominated CDAO Fall in Boston, MA last week. As Corinium’s flagship conference, the annual event showcases how the world’s top data and analytics leaders are driving change within their organizations and reveals the key talking points for the year ahead.

Analytics 221
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Challenges of Data Science Projects

Data Science 101

It is no secret that data science is difficult. Companies struggle to succeed with data science projects. Even Gartner predicts that by 2022 only 20% of analytics projects will deliver business value. That means about 80% will fail to deliver value. Thus, companies need to be very careful about running data analytics projects. There are many reasons for the failure of data science projects.

More Trending

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What is the Chi-Square Test and How Does it Work? An Intuitive Explanation with R Code

Analytics Vidhya

Overview What is the chi-square test? How does it work? Learn about the different types of Chi-Square tests and where and when you should. The post What is the Chi-Square Test and How Does it Work? An Intuitive Explanation with R Code appeared first on Analytics Vidhya.

Testing 313
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The Age of the Customer: How companies transform their customer experience

Corinium

Time and time again, we hear from our contacts asking to hear about real use cases. We have partnered with Zendesk to share their customer stories. "The customer is always right" is a phrase that Zendesk takes very seriously. What a customer needs and wants? That's at the heart and soul of everything Zendesk does. Whether it’s small companies trying to rapidly grow and build their brand, or large companies trying to maintain customer loyalty, Zendesk aims to empower businesses to seamlessly serv

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Moving AI and ML from research into production

O'Reilly on Data

In this interview from O’Reilly Foo Camp 2019, Dean Wampler, head of evangelism at Anyscale.io, talks about moving AI and machine learning into real-time production environments. Highlights from the interview include: Facilitating the transition from research to production in a robust way introduces a number of complications, Wampler says, including governance, GDPR, and traceability rules.

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Open Source Projects by Google, Uber and Facebook for Data Science and AI

KDnuggets

Open source is becoming the standard for sharing and improving technology. Some of the largest organizations in the world namely: Google, Facebook and Uber are open sourcing their own technologies that they use in their workflow to the public.

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Peak Performance: Continuous Testing & Evaluation of LLM-Based Applications

Speaker: Aarushi Kansal, AI Leader & Author and Tony Karrer, Founder & CTO at Aggregage

Software leaders who are building applications based on Large Language Models (LLMs) often find it a challenge to achieve reliability. It’s no surprise given the non-deterministic nature of LLMs. To effectively create reliable LLM-based (often with RAG) applications, extensive testing and evaluation processes are crucial. This often ends up involving meticulous adjustments to prompts.

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A Unique Method for Machine Learning Interpretability: Game Theory & Shapley Values!

Analytics Vidhya

Overview Learn how to use Shapley values in game theory for machine learning interpretability It’s a unique and different perspective to interpret black-box machine. The post A Unique Method for Machine Learning Interpretability: Game Theory & Shapley Values! appeared first on Analytics Vidhya.

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Speaker Spotlight Day Manuet, Data Analyst – Analytics, Epworth HealthCare

Corinium

Joining us at the Data and Analytics in Healthcare (4-5 March | Melbourne), we are pleased to welcome Day Manuet, Data Analyst – Analytics at Epworth HealthCare. She shares her thoughts on the use of data within Australia's healthcare system, and what steps need to be taken to improve patient outcomes through data analytics and much more.

Analytics 195
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Enterprise Architecture Tools and the Changing Role of the Enterprise Architect

erwin

Enterprise architecture tools are becoming more important than ever. The International Enterprise Architecture Institute (IEAI) defines enterprise architecture (EA) as “the analysis and documentation of an enterprise in its current and future states from an integrated strategy, business and technology perspective.”. In the era of data-driven business, such perspective is critical.

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Getting Started with Automated Text Summarization

KDnuggets

This article will walk through an extractive text summarization process, using a simple word frequency approach, implemented in Python.

Analytics 159
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How to Build an Experimentation Culture for Data-Driven Product Development

Speaker: Margaret-Ann Seger, Head of Product, Statsig

Experimentation is often seen as an aspirational practice, especially at smaller, fast-moving companies who are strapped for time and resources. So, how can you get your team making decisions in a more data-driven way while continuing to remain lean and maintaining ship velocity? In this webinar, Margaret-Ann Seger, Head of Product at Statsig, will teach you how to build an experimentation culture from the ground-up, graduating from just getting started with data-driven development to operating

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IADSS Talk – Who can be a Data Scientist?

Data Science 101

Initiative for Analytics and Data Science Standards (IADSS) is an organization working to develop standards around the roles in data science. They did a large survey earlier this year and they are starting to role out some of their results. Below is a video with some early results. Great Stuff! Data Science 101 is proud to be an IADSS Digital Community Partner.

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7 Ways To Use Big Data To Your Advantage On Social Media

Smart Data Collective

Businesses can use big data in many capacities, but those who use it for social media are at a huge advantage. It enables you as a social media marketer to get a closer look at your customer base, understand what drives purchasing decisions , and encourage consumers to pull the trigger. Using big data to augment your social media strategy provides a wealth of opportunities simply because social media is such an integral part of people’s lives.

Big Data 110
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Top 6 Data Analytics Tools in 2019

FineReport

When it comes to data analytics tools, we always have questions. What is the difference between so many data analysis tools? Which is better? Which one should I study? Although this is a commonplace topic, it is really important, and I have been working hard to pursue the answer to this ultimate problem. If you go online to search for relevant information in this area, it is difficult to see a fair point of view.

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Top KDnuggets tweets, Nov 20-26: How to Speed up Pandas by 4x with one line of code

KDnuggets

Also: Deep Learning for Image Classification with Less Data; How to Speed up Pandas by 4x with one line of code; 25 Useful #Python Snippets to Help in Your Day-to-Day Work; Automated Machine Learning Project Implementation Complexities.

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Entity Resolution Checklist: What to Consider When Evaluating Options

Are you trying to decide which entity resolution capabilities you need? It can be confusing to determine which features are most important for your project. And sometimes key features are overlooked. Get the Entity Resolution Evaluation Checklist to make sure you’ve thought of everything to make your project a success! The list was created by Senzing’s team of leading entity resolution experts, based on their real-world experience.

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Cloud Data Science News – Beta #4

Data Science 101

In the United States, it is a holiday week, so the news is pretty limited from many of the big cloud providers. Luckily, Amazon has come through with a flurry of machine learning announcements. Amazon is holding their annual re:Invent Conference next week, so maybe these announcements are precursors to some bigger news next week. We will have to wait and see.

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Get Ready For These Six 2020 Business Intelligence Trends

Smart Data Collective

More and more often, businesses are using data to drive their decisions — which makes cutting-edge analytics and business intelligence strategies one of the best advantages a company can have. New technologies, especially those driven by artificial intelligence (or AI), are changing how businesses collect and extract usable insights from data. Here are the six trends you should be aware of that will reshape business intelligence in 2020 and throughout the new decade. 1.

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Build cognitive solutions with Financial Services Workbench and Cloud Pak for Data

IBM Big Data Hub

The future of banking is transforming. From changing customer behavior and expectations, rapid innovation in digital technology, burgeoning regulatory requirements, and the macroeconomic environment, the very definition of financial services is changing.

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Two Years In The Life of AI, Machine Learning, Deep Learning and Java

KDnuggets

Where does Java stand in the world of artificial intelligence, machine learning, and deep learning? Learn more about how to do these things in Java, and the libraries and frameworks to use.

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Driving Business Impact for PMs

Speaker: Jon Harmer, Product Manager for Google Cloud

Move from feature factory to customer outcomes and drive impact in your business! This session will provide you with a comprehensive set of tools to help you develop impactful products by shifting from output-based thinking to outcome-based thinking. You will deepen your understanding of your customers and their needs as well as identifying and de-risking the different kinds of hypotheses built into your roadmap.

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Hey Data People, The NIH needs your help

Data Science 101

The National Institutes of Health (NIH) is creating a Data Management and Sharing Policy. They are currently looking for input on the policy. The NIH funds a lot of research, and this policy will affect the data and results researchers produce. NIH’s DRAFT Data Management and Sharing Policy: We Need to Hear From You! This is the time to share your thoughts.

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Challenges in replacing SSRS with Microsoft Power BI

BizAcuity

There are several reporting tools and platforms available today, and enterprises usually choose the one that is best suited for their business needs. Two popular options for reporting platforms are SQL Server Reporting Services (SSRS) and Microsoft Power BI. SSRS and Microsoft Power BI. SSRS is a server-based reporting platform that comes free with SQL Server 2012.

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Without data, the journey to AI is like a trek through the desert, sans compass

IBM Big Data Hub

James Fisher & Sons had hearty ambitions to build predictive maintenance capabilities for its customers' subsea cables -- but lacked the right data to do so. In a creative pivot, the IBM Data Science and AI Elite team delivered more than what the heritage engineering company bartered for -- with an entire roadmap for their data science strategy.

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Lit BERT: NLP Transfer Learning In 3 Steps

KDnuggets

PyTorch Lightning is a lightweight framework which allows anyone using PyTorch to scale deep learning code easily while making it reproducible. In this tutorial we’ll use Huggingface's implementation of BERT to do a finetuning task in Lightning.

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Reimagined: Building Products with Generative AI

“Reimagined: Building Products with Generative AI” is an extensive guide for integrating generative AI into product strategy and careers featuring over 150 real-world examples, 30 case studies, and 20+ frameworks, and endorsed by over 20 leading AI and product executives, inventors, entrepreneurs, and researchers.

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Your Complete Guide to a Cloud Data Warehouse

ScienceSoft

Learn what a cloud data warehouse is and what distinguishes it from traditional DWHs. Explore what market leaders offer and check how-to-mitigate-the-risk recommendations.

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Can Big Data Eliminate Shortcomings of Team Extension Models?

Smart Data Collective

Big data is becoming more essential in the arena of employee collaboration. A growing number of teams are finding that big data can be very beneficial when it comes to forging stronger relationships between their participants. Dr. Mark van Rijmenam, the founder of DataFlaq is one of the world’s leading experts on the intersection between big data and interpersonal communication.

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Top 5 Cities for Data Scientists to Thrive In

MLWhiz

Technological developments have paved the way for new niche industries, where professions like data science have appeared. Data scientists have the knowledge and expertise to perform the work that data analysts do, and then some. They analyze and interpret complex data sets of varying structures, and are able to solve obscure problems with codes, models, and machine-learning algorithms.

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Probability Learning: Naive Bayes

KDnuggets

This post will describe various simplifications of Bayes' Theorem, that make it more practical and applicable to real world problems: these simplifications are known by the name of Naive Bayes. Also, to clarify everything we will see a very illustrative example of how Naive Bayes can be applied for classification.

IT 100
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Strategic CX: A Deep Dive into Voice of the Customer Insights for Clarity

Speaker: Nicholas Zeisler, CX Strategist & Fractional CXO

The first step in a successful Customer Experience endeavor (or for that matter, any business proposition) is to find out what’s wrong. If you can’t identify it, you can’t fix it! 💡 That’s where the Voice of the Customer (VoC) comes in. Today, far too many brands do VoC simply because that’s what they think they’re supposed to do; that’s what all their competitors do.