Sat.Oct 26, 2019 - Fri.Nov 01, 2019

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‘Phase Two’ of Europe’s Data Transformation

Corinium

For the UK and Europe’s most data-led companies, phase one of the data transformation is now complete. The strategies have been agreed, the foundations have been laid and the real work is well underway.

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Fundamentals of Data Mining

Data Science 101

Today we are generating data more than ever before. Over the last two years, 90 percent of the data in the world was generated. This data alone does not make any sense unless it’s identified to be related in some pattern. Data mining is the process of discovering these patterns among the data and is therefore also known as Knowledge Discovery from Data (KDD).

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How To Create Data Reports That Will Skyrocket Your Business Performance

datapine

Historically, the terms data report or business report haven’t got the crowds excited. Data reports have always been important for businesses. However, they have been a necessary evil, created by analysts and consultants. The term usually conjures up images of static PDFs, old-school PowerPoint slides and big tables. Usually created with past data without the possibility to generate real-time or future insights, these reports were obsolete, comprised of numerous external and internal files, with

Reporting 127
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Data Sources 101

KDnuggets

Data collection is one of the first steps of the data lifecycle — you need to get all the data you require in the first place. To collect the right data, you need to know where to find it and determine the effort involved in collecting it. This article answers the most basic question: where does all the data you need (or might need) come from?

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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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How to Make an Agile Team Work for Big Data Analytics

KDnuggets

Learn how to approach the challenges when merging an agile methodology into a data science team to bring out the best value your Big Data products.

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PySpark for Beginners – Take your First Steps into Big Data Analytics (with Code)

Analytics Vidhya

Overview Big Data is becoming bigger by the day, and at an unprecedented pace How do you store, process and use this amount of. The post PySpark for Beginners – Take your First Steps into Big Data Analytics (with Code) appeared first on Analytics Vidhya.

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Highlights from TensorFlow World in Santa Clara, California 2019

O'Reilly on Data

People from across the TensorFlow community came together in Santa Clara, California for TensorFlow World. Below you’ll find links to highlights from the event. Opening keynote. Jeff Dean explains why Google open-sourced TensorFlow and discusses its progress. Watch “ Opening keynote “ Accelerating ML at Twitter. Theodore Summe offers a glimpse into how Twitter employs machine learning throughout its product.

IoT 195
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Data Governance Makes Data Security Less Scary

erwin

Happy Halloween! Do you know where your data is? What data you have? Who has had access to it? These can be frightening questions for an organization to answer. Add to the mix the potential for a data breach followed by non-compliance, reputational damage and financial penalties and a real horror story could unfold. In fact, we’ve seen some frightening ones play out already: Google’s record GDPR fine – France’s data privacy enforcement agency hit the tech giant with a $57 million penalty in earl

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Build Better and Accurate Clusters with Gaussian Mixture Models

Analytics Vidhya

Overview Gaussian Mixture Models are a powerful clustering algorithm Understand how Gaussian Mixture Models work and how to implement them in Python We’ll also. The post Build Better and Accurate Clusters with Gaussian Mixture Models appeared first on Analytics Vidhya.

Modeling 265
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‘Phase Two’ of Europe’s Data Transformation

Corinium

For the UK and Europe’s most data-led companies, phase one of the data transformation is now complete. The strategies have been agreed, the foundations have been laid and the real work is well underway.

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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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“Human error”: How can we help people build models that do what they expect

O'Reilly on Data

This is a keynote from TensorFlow World in Santa Clara, California 2019. See more keynotes from this event.

Modeling 135
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5 Statistical Traps Data Scientists Should Avoid

KDnuggets

Here are five statistical fallacies — data traps — which data scientists should be aware of and definitely avoid.

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Here are 8 Powerful Sessions to Learn the Latest Computer Vision Techniques

Analytics Vidhya

Do you want to build your own smart city? Picture it – self-driving cars strolling around, traffic lights optimised to maintain a smooth flow, The post Here are 8 Powerful Sessions to Learn the Latest Computer Vision Techniques appeared first on Analytics Vidhya.

Analytics 260
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Interview with: Amaresh Tripathy, Business Leader of Analytics at Genpact

Corinium

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

Analytics 150
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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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TensorFlow Lite: ML for mobile and IoT devices

O'Reilly on Data

This is a keynote from TensorFlow World in Santa Clara, California 2019. See more keynotes from this event.

IoT 132
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Predictions 2020: Distributed Ledger Technology Moves Beyond Proof Of Concept

Martha Bennett

In 2020 users of blockchain and distributed ledger technology will focus on operational matters, deployment flexibility and, interconnectivity. Read our 2020 predictions to learn more.

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Top Machine Learning Software Tools for Developers

KDnuggets

As a developer who is excited about leveraging machine learning for faster and more effective development, these software tools are worth trying out.

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eLearning Learning Blog Post

Corinium

We are thrilled to partner with eLearning Learning for our upcoming Chief Learning Officer, APEX Fall 2019 conference!

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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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TensorFlow World 2019 opening keynote

O'Reilly on Data

This is a keynote from TensorFlow World in Santa Clara, California 2019. See more keynotes from this event.

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How Low Conversion Data Seriously Hinders Machine Learning

Smart Data Collective

Machine learning is changing the future of marketing in many beneficial ways. The Digital Marketing Institute reports that 97% of decision makers believe it is the future of marketing. There are a number of tactics that marketers can pursue to optimize campaigns with machine learning algorithms. However, some of these strategies are more limited then marketers would like to think.

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How to Build Your Own Logistic Regression Model in Python

KDnuggets

A hands on guide to Logistic Regression for aspiring data scientist and machine learning engineer.

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HR Tech Central Blog Post

Corinium

We are thrilled to partner with HR Tech Central for our upcoming Chief Learning Officer, APEX Fall 2019 conference!

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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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TensorFlow Hub: The platform to share and discover pretrained models for TensorFlow

O'Reilly on Data

This is a keynote from TensorFlow World in Santa Clara, California 2019. See more keynotes from this event.

Modeling 131
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How to Streamline Report Management to Enhance Your Data-Driven Business

Sisense

Blog. A critical part of effectively exploring your data, transforming it into actionable insights, and enhancing decision-making for your business is being empowered to slice and dice your data, and be less dependent on technical resources for new updates. Improved visibility into insights will enable you to get more out of them. And as the business users, you’re best placed to identify which of them most closely meet your organization’s needs.

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How Bayes’ Theorem is Applied in Machine Learning

KDnuggets

Learn how Bayes Theorem is in Machine Learning for classification and regression!

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Sustainable Behavior Change: Metrics, Measurements and Methods

Corinium

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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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MLIR: Accelerating AI

O'Reilly on Data

This is a keynote from TensorFlow World in Santa Clara, California 2019. See more keynotes from this event.

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6 Best Reporting Tools for Social Media

FineReport

Why We Need Reporting Tools for Social Media. More and more people are working on social media operations recently. Facebook, Twitter, Instagram… Massive traffic data and user behaviors data is too large for administrators to handle. At this time, you can’t help but ask if there is a good reporting tool, which can help process massive amounts of data and visualize the processed data to help make decisions?

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Build an Artificial Neural Network From Scratch: Part 1

KDnuggets

This article focused on building an Artificial Neural Network using the Numpy Python library.

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Keeping your operationsat a healthy weight

Corinium

The problem with dieting. The problem with dieting is this; it’s really easy to eat and drink far more calories than is recommended and really hard (at least for me) to eat and drink less. So, unless you are one of those lucky people who don’t mind hunger, the dice are loaded against you. If you are really good for six days a week and a bit bad on the seventh then that’s still no good; across the week, calories in are more than calories out.

IT 60
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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.