Sat.Jan 11, 2020 - Fri.Jan 17, 2020

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5 Thoughts on How to Transition into Data Science from Different Backgrounds

Analytics Vidhya

Overview Looking to transition into data science? Here are 5 paths for a non-data science person to land a role in this space The. The post 5 Thoughts on How to Transition into Data Science from Different Backgrounds appeared first on Analytics Vidhya.

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Reinforcement learning for the real world

O'Reilly on Data

Roger Magoulas recently sat down with Edward Jezierski, reinforcement learning AI principal program manager at Microsoft, to talk about reinforcement learning (RL). They discuss why RL’s role in AI is so important, challenges of applying RL in a business environment, and how to approach ethical and responsible use questions. Here are some highlights from their conversation: Reinforcement learning is different than simply trying to detect something in an image or extract something from a da

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AI Poised to Disrupt the Insurance Industry

Corinium

AI is coming to the disrupt the insurance industry. From Ping An in China to Lemonade in the US, companies across the globe are harnessing AI technologies to drag the sector into the 21st century.

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Top 10 Technology Trends for 2020

KDnuggets

With integrations of multiple emerging technologies just in the past year, AI development continues at a fast pace. Following the blueprint of science and technology advancements in 2019, we predict 10 trends we expect to see in 2020 and beyond.

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From Developer Experience to Product Experience: How a Shared Focus Fuels Product Success

Speaker: Anne Steiner and David Laribee

As a concept, Developer Experience (DX) has gained significant attention in the tech industry. It emphasizes engineers’ efficiency and satisfaction during the product development process. As product managers, we need to understand how a good DX can contribute not only to the well-being of our development teams but also to the broader objectives of product success and customer satisfaction.

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Building Machine Learning Pipelines and AI in Retail – A Powerful Interview with Rossella Blatt Vital

Analytics Vidhya

Overview How do machine learning pipelines work? What’s the role of AI in retail? How important is ethics in this field? We are thrilled. The post Building Machine Learning Pipelines and AI in Retail – A Powerful Interview with Rossella Blatt Vital appeared first on Analytics Vidhya.

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What Is Data Modeling? Data Modeling Best Practices for Data-Driven Organizations

erwin

What is Data Modeling? Data modeling is a process that enables organizations to discover, design, visualize, standardize and deploy high-quality data assets through an intuitive, graphical interface. Data models provide visualization, create additional metadata and standardize data design across the enterprise. As the value of data and the way it is used by organizations has changed over the years, so too has data modeling.

More Trending

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Top 9 Mobile Apps for Learning and Practicing Data Science

KDnuggets

This article will tell you about the top 9 mobile apps that help the user in learning and practicing data science and hence is improving their productivity.

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What are Generative Models and GANs? The Magic of Computer Vision

Analytics Vidhya

Overview Generative models and GANs are at the core of recent progress in computer vision applications This article will introduce you to the world. The post What are Generative Models and GANs? The Magic of Computer Vision appeared first on Analytics Vidhya.

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Cloud Data Science News 3

Data Science 101

Some news this week out of Microsoft and Amazon. News. Azure is now ISO/IEC 27701 Certified Azure becomes the first public cloud to receive this certification for Privacy and Information Management Python in Visual Studio Code Visual Studio Code now allows a user to select which version of python should be used for the Jupyter Notebook AWS Quick Start now deploys Matillion ETL for Amazon Redshift Title says it all, but if you use Matillion and Redshift, this is a big win for you.

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Data Quality in Financial Institutions

Corinium

In the last decade regulatory requirements in financial services increased significantly.

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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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The Future of Machine Learning

KDnuggets

This summary overviews the keynote at TensorFlow World by Jeff Dean, Head of AI at Google, that considered the advancements of computer vision and language models and predicted the direction machine learning model building should follow for the future.

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A Guide to Link Prediction – How to Predict your Future Connections on Facebook

Analytics Vidhya

Overview An introduction to link prediction, how it works, and where you can use it in the real-world Learn about the importance of Link. The post A Guide to Link Prediction – How to Predict your Future Connections on Facebook appeared first on Analytics Vidhya.

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10 Fascinating Examples of Big Data In Healthcare

Smart Data Collective

Big Data has a lot of great uses in the work of consumer marketing. Experts recognize that its benefits go well beyond the needs of individual consumers. In fact, Big Data has many uses in helping patient lives in the world of healthcare. The market for big data in healthcare is growing 22% a year. From predicting risk factors to helping cure disease, Big Data in healthcare is multi-faceted.

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Aussie Data Stories #1 Improving Financial Well-Being with CBA

Corinium

Bringing together a collection of stories to celebrate success and to further drive and inspire data innovation in Australia. With a unique data story around financial wellbeing, Andrea Nicastro, Data Scientist at Commonwealth Bank took us through their journey. Through listening to customers to understand what is important to them, working with community groups, experts in economic and social studies, and policy makers, CBA were able to better understand Australia’s most pressing financial chal

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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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Software commodities are eating interesting data science work

Data Science and Beyond

The passage of time makes wizards of us all. Today, any dullard can make bells ring across the ocean by tapping out phone numbers, cause inanimate toys to march by barking an order, or activate remote devices by touching a wireless screen. Thomas Edison couldn’t have managed any of this at his peak—and shortly before his time, such powers would have been considered the unique realm of God. – Rob Reid, After On.

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Math for Programmers!

KDnuggets

Math for Programmers teaches you the math you need to know for a career in programming, concentrating on what you need to know as a developer.

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How to integrate third party applications with Microsoft Dynamics 365

BizAcuity

Microsoft Dynamics 365 can easily be integrated with other Microsoft solutions as well as a myriad of third-party applications such as web portals, BI applications, and ERP systems. Such integration allows B2B companies and enterprises to leverage all tools and resources of Microsoft to get their business done. Managing end-to-end business processes becomes easier with the power of Dynamics 365 coupled with the required business applications that can be integrated.

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A Brief History of Our Future

Corinium

Paul Morley.

Analytics 377
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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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Methods of Study Design – Experiments

Data Science 101

We all are familiar with experiments , we read about them in books or newspapers. Researchers/ scientists perform experiments to validate their hypothesis/ statements or to test a new product. Unlike observational studies, experiments are performed in a controlled environment so that the effect of other external factors/variables can be eliminated from it.

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Handling Trees in Data Science Algorithmic Interview

KDnuggets

This post is about fast-tracking the study and explanation of tree concepts for the data scientists so that you breeze through the next time you get asked these in an interview.

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Sisense Hackathon 2020: Make Something Awesome With AWS

Sisense

Blog. In our Event Spotlight series, we cover the industry events of all kinds to help builders learn about the latest tech, trends, and people innovating in the data and analytics space. Sisense Hackathon 2020 continues our annual tradition of pushing the envelope for innovation, creativity, and collaboration, company-wide. A diverse array of Sisensers with different backgrounds brainstorm wild ideas to build new products and solve problems. .

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Requirements for Data Governance

TDAN

Recording requirements for success is an important first step toward demonstrating the value of a Data Governance program. Practitioners know that Data Governance requires planning, resources, money and time and that several of these objects are in short supply. Data Governance requirements are instrumental to 1) planning for Data Governance, 2) the definition of Data […].

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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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Why Operationalizing Machine Learning Requires a Shrewd Business Perspective

Decision Management Solutions

The Machine Learning Times (previously Predictive Analytics Times) is the only full-scale content portal devoted exclusively to predictive analytics. It has become a standard must-read and machine learning professionals’ premier resource, delivering timely, relevant industry-leading articles, videos, events, white papers, and community. In this month’s featured article, Eric Siegel, Ph.D., executive editor of The Machine Learning Times and founder of the Predictive Analytics World and Deep Learn

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Idiot’s Guide to Precision, Recall, and Confusion Matrix

KDnuggets

Building Machine Learning models is fun, but making sure we build the best ones is what makes a difference. Follow this quick guide to appreciate how to effectively evaluate a classification model, especially for projects where accuracy alone is not enough.

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Our Top 20 Most-Read Data and Analytics Research Last Week (to Jan 12)

Andrew White

Click here for an interactive PDF to connect to the most read data and analytics research directly. This list excludes our branded research such as Magic Quadrants etc. Pieter den Hamer storms into top spot last week with this new note on migrating your data and analytics platform. Its a must-read for those of you looking at analytics, BI and data science, data management, and emerging data and analytics governance platforms.

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Data Professional Introspective: The Perennial Question

TDAN

Recently, I’ve encountered many client staff, course students, and conference attendees who are grappling with the basic question: “What is the difference between Data Managementand Data Governance?” It seems that the more our industry expands, the more frequently this question is asked. ———- I attribute this to a few factors: The increasing volume of data […].

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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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Data Science for Social Good, Summer 2020, Applications are Open

Data Science 101

The Data Science for Social Good Summer Fellowship , now hosted at Carnegie Mellon University, is accepting applications. This is a 12-week program to train data scientists about working on projects which positively impact society. There are a number of roles available. Fellows Mentors Project Managers. Most applications are due January 31, 2020. They are also still accepting applications for projects, so if you are an organization working on social problems, this could be a great opportunity to

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Geovisualization with Open Data

KDnuggets

In this post I want to show how to use public available (open) data to create geo visualizations in python. Maps are a great way to communicate and compare information when working with geolocation data. There are many frameworks to plot maps, here I focus on matplotlib and geopandas (and give a glimpse of mplleaflet).

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Beyond Single Version of the Truth

Andrew White

The single version of the truth can be anathema. It does not really exist and it’s not really worth the effort. But then again, what we mean by that phrase has changed. The original idea of a perfectly defined, universally agreed and accepted piece of data, or golden record, proved too expensive to maintain; if it was ever even achieved. Costs were saved with silver copies (somewhat less agreed; candidates for golden level etc.) and other variants.

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10 Exciting Analytics Trends to Explore

DataRobot Blog

by Jen Underwood. New year. New decade. Time to prepare for what comes next. As organizations continue modernizing and moving to the cloud, the analytics world we once knew continues to change. Here’s. Read More.

Analytics 103
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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.