August, 2022

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Metaverse: The time for CIOs to experiment is now

CIO Business Intelligence

For the past forty years CIOs have labored to retrofit, rearchitect, and ultimately replace underfunded and underappreciated legacy infrastructures in hopes of delivering the full benefits associated with periodically occurring waves of transformative emerging technologies. Debate now rages in IT and digital communities regarding what will be the seismic technological shift of the 2020s.

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On Technique

O'Reilly on Data

In a previous article , I wrote about how models like DALL-E and Imagen disassociate ideas from technique. In the past, if you had a good idea in any field, you could only realize that idea if you had the craftsmanship and technique to back it up. With DALL-E, that’s no longer true. You can say, “Make me a picture of a lion attacking a horse,” and it will happily generate one.

Software 265
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Building a simple Flask App using Docker vs Code

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction More often than not, developers run into issues of an application running on one machine versus not running on another. Dockers help prevent this by ensuring the application runs on any machine if it works on yours. Simply put, if your job as […]. The post Building a simple Flask App using Docker vs Code appeared first on Analytics Vidhya.

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7 Techniques to Handle Imbalanced Data

KDnuggets

This blog post introduces seven techniques that are commonly applied in domains like intrusion detection or real-time bidding, because the datasets are often extremely imbalanced.

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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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7 Enterprise Applications for Companies Using Cloud Technology

Smart Data Collective

The market for cloud technology is booming. Companies spent over $405 billion on cloud services last year. The sudden growth is not surprising, because the benefits of the cloud are incredible. Enterprise cloud technology applications are the future industry standard for corporations. Cloud computing has found its way into many business scenarios and is a relatively new concept for businesses.

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AI for Time Travel? Well, Almost

Dataiku

AI predictions compress time: Reduce credit card refunds from 60 to 30 days or time to detect chip manufacturing problems from 36 hours to zero. Increasingly, companies are going beyond zero and using AI to detect things before they happen: identifying the purchase of a large screen TV with a stolen credit card hours before it happens, replacing an airplane valve days before it fails, and flagging semiconductor manufacturing defects an hour before they’re produced.

More Trending

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Incremental Strategies to Move Your Data Strategy Forward Remove Obstacles to Unlock Possibilities in Financial Services

Cloudera

Firms are burdened with tech debt and endless regulatory compliance, often leaving innovation last to receive the necessary budgets. Data-fuelled innovation requires a pragmatic strategy. This blog lays out some steps to help you incrementally advance efforts to be a more data-driven, customer-centric organization. Embrace incremental progress. The financial sector’s evolution is unleashing myriad demands on firms operating in the market.

Strategy 108
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Introduction to Requests Library in Python

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction Requests in Python is a module that can be used to send all kinds of HTTP requests. It is straightforward to use and is a human-friendly HTTP Library. Using the requests library; we do not need to manually add the query string […]. The post Introduction to Requests Library in Python appeared first on Analytics Vidhya.

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What Does ETL Have to Do with Machine Learning?

KDnuggets

ETL during the process of producing effective machine learning algorithms is found at the base - the foundation. Let’s go through the steps on how ETL is important to machine learning.

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5 Ways B2B Companies Can Use Analytics for Pricing

Smart Data Collective

Analytics technology is very important for modern business. Companies spent over $240 billion on big data analytics last year. That figure is expected to grow as more businesses discover its benefits. There are many important applications of data analytics technology. One of the most important is with helping companies set their prices correctly. Analytics Can Be Essential for Helping Companies with their Pricing Strategies.

B2B 134
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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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Text AI Updates Drive Faster Business Value

DataRobot Blog

How can you save time in understanding the impact of language when working with text in ML models ? With tens of thousands of Text AI projects, DataRobot has helped organizations unlock insights from text and generate predictions with text models—from assisting with customer support ticket triage to predicting real estate sale prices. Continuing to build on previously released Text AI capabilities, DataRobot AI Cloud introduces new features to help with language detection, blueprint optimization

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5 forces shaping the future IT workforce

CIO Business Intelligence

Concerns about a looming recession have not derailed the booming IT job market. In the first half of this year, 115,000 new IT jobs were added, according to consulting firm Janco Associates. Even with inflation, high energy costs, and the invasion of Ukraine, IT hiring continued at a record-setting pace, but keeping tech talent still poses challenges.

IT 139
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Visualizing 24 School Divisions’ Submissions with a Dashboard in Microsoft Excel

Depict Data Studio

This guest post comes from Amadu Sidi Bah, who’s graduated from our Simple Spreadsheets, Great Graphs, Report Redesign, and Dashboard Design courses. Great work, Amadu! — Ann K. Emery. All too often, written submissions from stakeholders come in dense, long reports. That’s what happened in our project when stakeholders were consulted on the changes that would be required to improve student outcomes.

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The Ultimate Guide To Pandas For Data Science!

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction 2.5 quintillion bytes of data are produced every day! Consider how much we can deduce from that and what conclusions we can draw. Wait! But, how do we deal with such a massive amount of data? Not to worry; the Pandas library […]. The post The Ultimate Guide To Pandas For Data Science!

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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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Data Transformation: Standardization vs Normalization

KDnuggets

Increasing accuracy in your models is often obtained through the first steps of data transformations. This guide explains the difference between the key feature scaling methods of standardization and normalization, and demonstrates when and how to apply each approach.

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The Future of AI: High Quality, Human Powered Data

Smart Data Collective

Artificial Intelligence (AI) has significantly altered how work is done. However, AI even has a bigger impact by enhancing human capabilities. Research conducted by the Harvard Business Review found that the interaction between machines and humans significantly improves firms’ performance. Successful collaboration between humans and machines enhances each other’s strengths, including teamwork, leadership, creativity, speed, scalability, and quantitative capabilities.

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What Can WordleBot Teach Us About Actionable Data Insights?

Juice Analytics

I’m a Wordle obsessive. Which is to say, every morning I find myself staring deep into my coffee in search of an elusive 5-letter word. The New York Times (who bought the word game from software developer Josh Wardle for $3 million) knows their audience. We may be playing with words, but the analytical nature of this game is the appeal. It is a game of odds and mathematical deduction as we try to reduce the potential options available.

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Why every IT leader should avoid ‘best practices’

CIO Business Intelligence

The nonsense was tucked away in a PowerPoint slide, as so much nonsense is. “We’ll help you institute best practices, followed by a program of continuous improvement,” the offending bullet said. Now, I’m willing to shrug at a bit of harmless puffery from time to time. And maybe this puffery was harmless. But I don’t think so. As my pappy used to say, ‘If someone sells this and someone else buys it, they have something in common: They’re both schmucks.

IT 133
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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.

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Teradata VantageCloud Lake and ClearScape Analytics: Empowering Enterprise Analytical Innovation

Teradata

Teradata's new offerings, VantageCloud Lake and ClearScape Analytics, make it the complete cloud analytics & data platform, with cloud-native deployment and expanded analytics capabilities.

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Multi-variate Time Series Forecasting using Kats Model

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction Kats model-which is also developed by Facebook Research Team-supports the functionality of multi-variate time-series forecasting in addition to univariate time-series forecasting. Often we need to forecast a time series where we have input variables in addition to ‘time’; this is where the […].

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The Importance of Experiment Design in Data Science

KDnuggets

Do you feel overwhelmed by the sheer number of ideas that you could try while building a machine learning pipeline? You can not take the liberty of trying all possible ways to arrive at a solution - hence we discuss the importance of experiment design in data science projects.

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What is the IoT and How is it Changing the World?

Smart Data Collective

The Internet of Things (IoT) has been on the rise in recent years, and it’s becoming more and more common among consumers, businesses, and governments alike. IoT refers to any connected physical device that can send or receive data over the internet, including smartphones, computers, speakers, security cameras, thermostats, door locks, vehicles—the list goes on and on.

IoT 130
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The Big Payoff of Application Analytics

Outdated or absent analytics won’t cut it in today’s data-driven applications – not for your end users, your development team, or your business. That’s what drove the five companies in this e-book to change their approach to analytics. Download this e-book to learn about the unique problems each company faced and how they achieved huge returns beyond expectation by embedding analytics into applications.

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Ray Co-creator Robert Nishihara: How Easy Distributed Computing Changes Everything in Data Science

Domino Data Lab

What if you wanted to do something really ambitious in data science–something like designing an innovative new search engine? Today, that would be a daunting task, and you’d probably need a big, highly qualified team of data scientists and programmers to bring your innovation to life. And you’d need months, if not years, to finish it.

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Securing the ever-evolving hybrid work environment

CIO Business Intelligence

Even as many business leaders debate the boundaries of remote work styles and schedules, there is little doubt that hybrid work will persist for most enterprises. Yet, how hybrid work takes shape for any given business will likely evolve as business needs and employee expectations change over time. For IT and network security teams, the challenge is to secure their environments, regardless of where people are working.

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AI in Supply Chain — A Trillion Dollar Opportunity

DataRobot Blog

Supply chain and logistics industries worldwide lose over $1 trillion a year due to out-of-stock or overstocked items 1. Shifting demands and shipping difficulties make the situation worse. Challenges in inventory management, demand forecasting, price optimization, and more can result in missed opportunities and lost revenue. The retail marketplace has become increasingly complex and competitive.

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Most Frequently Asked NLP Interview Questions

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction Natural language processing (NLP) is the branch of computer science and, more specifically, the domain of artificial intelligence (AI) that focuses on providing computers the ability to understand written and spoken language in a way similar to that of humans. Combining computational linguistics […].

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Addressing Top Enterprise Challenges in Generative AI with DataRobot

The buzz around generative AI shows no sign of abating in the foreseeable future. Enterprise interest in the technology is high, and the market is expected to gain momentum as organizations move from prototypes to actual project deployments. Ultimately, the market will demand an extensive ecosystem, and tools will need to streamline data and model utilization and management across multiple environments.

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How Do Data Scientists and Data Engineers Work Together?

KDnuggets

If you’re considering a career in data science, it’s important to understand how these two fields differ, and which one might be more appropriate for someone with your skills and interests.

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How AI Software is Changing the Future of the Automotive Industry

Smart Data Collective

Artificial intelligence technology is changing the future of many industries. Global companies spent over $328 billion on AI last year. This figure is expected to grow as more companies recognize the potential and decide to increase the resources they dedicate to machine learning and predictive analytics tools. The automotive industry is among those investing in AI the most.

Software 129
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Data Speaks for Itself: What Could Possibly Go Wrong?

TDAN

I had a great experience attending the MIT Chief Data Officer and Information Quality Symposium in Cambridge this July. It was truly enlightening to hear from so many experienced data leaders. This year, there were 2,855 registered attendees from 63 countries, including 1,218 Chief Data Officers. I always learn so much at these symposia. In […].

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The Right Stuff: The Role of MLOps in AI Success

CIO Business Intelligence

Great teams incorporate a variety of skill sets. For example, a football team consisting of 11 quarterbacks would get crushed in a game against talented linemen, running backs and receivers. It’s no different when building a team for an enterprise AI project; you can’t just throw a bunch of data scientists into a room and expect them to come up with a revenue-generating or efficiency-improving project without support from other members of the enterprise.

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The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data and AI

Speaker: Aindra Misra, Sr. Staff Product Manager of Data & AI at BILL (Previously PM Lead at Twitter/X)

Embark on a transformation journey into the heart of the data ecosystem! This webinar is your gateway to a deeper comprehension of the foundations that drive the data industry and will equip you with the knowledge needed to navigate the evolving landscape. Delve into the diverse use cases where data analytics plays a pivotal role. We’ll explore how these applications are transforming with the introduction of Gen AI, and discuss the anticipated use cases for 2024 and beyond.