2020

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Diving Deeper into the Data Lake

David Menninger's Analyst Perspectives

A data lake is a centralized repository designed to house big data in structured, semi-structured and unstructured form. I have been covering the data lake topic for several years and encourage you to check out an earlier perspective called Data Lakes: Safe Way to Swim in Big Data? for background. Our data lake research has uncovered some points to consider in your efforts, and I’d like to offer a deeper dive into our findings.

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10 Data Science Projects Every Beginner should add to their Portfolio

Analytics Vidhya

ArticleVideos Overview The projects are a way to enhance and improve your knowledge in the data science domain. To boost your resume, here we. The post 10 Data Science Projects Every Beginner should add to their Portfolio appeared first on Analytics Vidhya.

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Top 14 Artificial Intelligence Startups to watch out for in 2021!

Analytics Vidhya

Overview Reducing company costs, generating customer insights & intelligence, and improving customer experiences are the three most popular ML and AI use cases Here. The post Top 14 Artificial Intelligence Startups to watch out for in 2021! appeared first on Analytics Vidhya.

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A Review of 2020 and Trends in 2021 – A Technical Overview of Machine Learning and Deep Learning!

Analytics Vidhya

Introduction Data science is not a choice anymore. It is a necessity. 2020 is almost in the books now. What a crazy year from. The post A Review of 2020 and Trends in 2021 – A Technical Overview of Machine Learning and Deep Learning! appeared first on Analytics Vidhya.

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Generative AI Deep Dive: Advancing from Proof of Concept to Production

Speaker: Maher Hanafi, VP of Engineering at Betterworks & Tony Karrer, CTO at Aggregage

Executive leaders and board members are pushing their teams to adopt Generative AI to gain a competitive edge, save money, and otherwise take advantage of the promise of this new era of artificial intelligence. There's no question that it is challenging to figure out where to focus and how to advance when it’s a new field that is evolving everyday. 💡 This new webinar featuring Maher Hanafi, VP of Engineering at Betterworks, will explore a practical framework to transform Generative AI pr

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8 Data Visualization Tips to Improve Data Stories

Analytics Vidhya

Overview Get to know the essential data visualization tips and techniques to improve your data stories Understand the effects of these data visualization tips. The post 8 Data Visualization Tips to Improve Data Stories appeared first on Analytics Vidhya.

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Bringing an AI Product to Market

O'Reilly on Data

The Core Responsibilities of the AI Product Manager. Product Managers are responsible for the successful development, testing, release, and adoption of a product, and for leading the team that implements those milestones. Product managers for AI must satisfy these same responsibilities, tuned for the AI lifecycle. In the first two articles in this series, we suggest that AI Product Managers (AI PMs) are responsible for: Deciding on the core function, audience, and desired use of the AI product E

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Doing Power BI the Right Way: 6. Data Modeling Essentials & Best Practices (1 of 2)

Paul Turley

Part of the the series: Doing Power BI the Right Way Data Modeling 101: part 1 (more advanced techniques coming in part 2) A data model is the foundation of analytic reporting. It provides structure and order over information that might otherwise be chaotic and untrustworthy.

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Why Your Startup Needs Data Science

TDAN

Top-quality data currently represents one of the most important resources for any company. This is especially true for young businesses that don’t have much experience in their market and that still don’t know enough about their customers. Startups that lack familiarity with important tendencies and trends in their industry need to have this crucial data […].

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Predictive vs. Prescriptive Analytics: What’s the Difference?

Dataiku

The bulk of an organization’s data science, machine learning, and AI conquests come down to improving decision-making capabilities. Teams may aim to achieve new levels of agility, expedite the time to insights, or refine the process leading up to the business value extraction so that it’s more efficient. When during this process, though, should data executives get either predictive or prescriptive?

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The curse of Dimensionality

Domino Data Lab

Guest Post by Bill Shannon, Founder and Managing Partner of BioRankings. Danger of Big Data. Big data is the rage. This could be lots of rows (samples) and few columns (variables) like credit card transaction data, or lots of columns (variables) and few rows (samples) like genomic sequencing in life sciences research. The Curse of Dimensionality , or Large P, Small N, ((P >> N)) , problem applies to the latter case of lots of variables measured on a relatively few number of sampl

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Leading the Development of Profitable and Sustainable Products

Speaker: Jason Tanner

While growth of software-enabled solutions generates momentum, growth alone is not enough to ensure sustainability. The probability of success dramatically improves with early planning for profitability. A sustainable business model contains a system of interrelated choices made not once but over time. Join this webinar for an iterative approach to ensuring solution, economic and relationship sustainability.

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Doing Cloud Migration and Data Governance Right the First Time

erwin

More and more companies are looking at cloud migration. Migrating legacy data to public, private or hybrid clouds provide creative and sustainable ways for organizations to increase their speed to insights for digital transformation, modernize and scale their processing and storage capabilities, better manage and reduce costs, encourage remote collaboration, and enhance security, support and disaster recovery.

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Will COVID-19 Show the Adaptability of Machine Learning in Loan Underwriting?

Smart Data Collective

Machine learning is transforming the financial sector more than anybody could have ever predicted. This technology might be more important than ever during the pandemic, as financial institutions discover that many traditional protocols aren’t nearly as effective. One of the most significant changes brought by advances in machine learning is with the loan underwriting process.

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Maker Tools for Information Workers

Juice Analytics

We are makers in our work. Whether designing a marketing campaign, creating a presentation, or building a spreadsheet, information workers spend a lot of time creating stuff. And we want better tools to do all that making. How far have these tools come? In some cases, the complex desktop tools have been replaced by nimble, web-based (and often less feature-rich) options.

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Create an Awesome Development Setup for Data Science using Atom

MLWhiz

Before I even begin this article, let me just say that I love iPython Notebooks, and Atom is not an alternative to Jupyter in any way. Notebooks provide me an interface where I have to think of “Coding one code block at a time,” as I like to call it, and it helps me to think more clearly while helping me make my code more modular. Yet, Jupyter is not suitable for some tasks in its present form.

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The Role of Data Products in Maximizing ROI from AI Initiatives

Stuck with stalled AI projects? Data silos and fragmented processes are likely culprits. This IDC report unveils Data Products as the secret weapon. By treating data as a product, organizations can overcome these challenges and unlock the true potential of AI. Learn how a unified data control plane empowers Data Products to deliver trusted, high-quality data for faster insights and improved decision-making.

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Top 15 Open-Source Datasets of 2020 that every Data Scientist Should add to their Portfolio!

Analytics Vidhya

Overview Here is a list of Top 15 Datasets for 2020 that we feel every data scientist should practice on The article contains 5. The post Top 15 Open-Source Datasets of 2020 that every Data Scientist Should add to their Portfolio! appeared first on Analytics Vidhya.

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What to Do When AI Fails

O'Reilly on Data

These are unprecedented times, at least by information age standards. Much of the U.S. economy has ground to a halt, and social norms about our data and our privacy have been thrown out the window throughout much of the world. Moreover, things seem likely to keep changing until a vaccine or effective treatment for COVID-19 becomes available. All this change could wreak havoc on artificial intelligence (AI) systems.

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Top 5 must-have Data Science skills for 2020

KDnuggets

The standard job description for a Data Scientist has long highlighted skills in R, Python, SQL, and Machine Learning. With the field evolving, these core competencies are no longer enough to stay competitive in the job market.

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Doing Power BI the Right Way: 7. Validating data model results

Paul Turley

Moving important business data into a data model for analytic reporting can often be a two-edge sword. Data retrieval is fast and can support all kinds of analytic trending and comparisons. But, data in the model may be one or two layers away from the original source data, making it more challenging to compare with familiar user reports. Often the first validation effort after transforming and loading data into the model and then visualizing the initial results is having a business user say " ye

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Embedding BI: Architectural Considerations and Technical Requirements

While data platforms, artificial intelligence (AI), machine learning (ML), and programming platforms have evolved to leverage big data and streaming data, the front-end user experience has not kept up. Holding onto old BI technology while everything else moves forward is holding back organizations. Traditional Business Intelligence (BI) aren’t built for modern data platforms and don’t work on modern architectures.

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Who Does the Machine Learning and Data Science Work?

Business Over Broadway

A survey of over 19,000 data professionals showed that nearly 2/3rds of respondents said they analyze data to influence product/business decisions. Only 1/4 of respondents said they do research to advance the state of the art of machine learning. Different data roles have different work activity profiles with Data Scientists engaging in more different work activities than other data professionals.

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An Introduction to Key Data Science Concepts

Dataiku

Here at Dataiku, we frequently stress the importance of collaboration in building a successful data team. In short, successful data science and analytics are just as much about creativity as they are about crunching numbers, and creativity flourishes in a collaborative environment.

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How to Convince Stakeholders That Data Governance is Necessary

TDAN

You may already have a formal Data Governance program in place. Then again, perhaps you don’t. Or … you are presently going through the process of trying to convince your Senior Leadership or stakeholders that a formal Data Governance program is necessary. Maybe you are going through the process of convincing the stakeholders that Data […].

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Surviving Radical Disruption with Data Intelligence

erwin

It’s certainly no secret that data has been growing in volume, variety and velocity, and most companies are overwhelmed by managing it, let alone harnessing it to put it to work. We’re now generating 2.5 quintillion bytes of data every day, and 90% of the world’s data volume has been created in the past two years alone. With this absolute data explosion, it’s nearly impossible to filter out the time-sensitive data, the information that has immediate relevance and impact o

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Get Better Network Graphs & Save Analysts Time

Many organizations today are unlocking the power of their data by using graph databases to feed downstream analytics, enahance visualizations, and more. Yet, when different graph nodes represent the same entity, graphs get messy. Watch this essential video with Senzing CEO Jeff Jonas on how adding entity resolution to a graph database condenses network graphs to improve analytics and save your analysts time.

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4 Incredible Benefits Of IoT-Based Indoor Mapping

Smart Data Collective

The IoT is becoming increasingly commercialized. IDC estimates that there will be 41.6 billion IoT devices online by 2025. As the IoT continues to expand, companies across the world are looking for new ways to embrace its potential. One of the most overlooked benefits of the IoT is with indoor mapping. Companies can find a number of useful IoT approaches to achieve this goal.

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IBM SPSS Statistics free trial extended through June 15 due to pandemic

IBM Big Data Hub

We recognize that these are difficult times. In response to the worldwide pandemic, IBM will be extending the SPSS Statistics Subscription trial for active and new accounts through June 15. This will allow our users time to adjust to this dynamic and unprecedented situation.

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Azure has the most Cloud Regions, and it's not even close

Data Science 101

The big cloud providers are expanding globally by adding more Global Regions. Google recently announced a new mountain west region. Plus, all the other providers have plans to expand globally. This got me wondering, which provider has the most global regions. I went to all the big cloud provider websites, and I was a bit surprised with the results. Google Cloud Regions.

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14 Free Data Science Books to Add your list in 2020 to Upgrade Your Data Science Journey!

Analytics Vidhya

Overview Here are 14 free data science books to get started and upgrade yourself on various fronts By no means is this an exhaustive. The post 14 Free Data Science Books to Add your list in 2020 to Upgrade Your Data Science Journey! appeared first on Analytics Vidhya.

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Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You Need to Know

Speaker: Timothy Chan, PhD., Head of Data Science

Are you ready to move beyond the basics and take a deep dive into the cutting-edge techniques that are reshaping the landscape of experimentation? 🌐 From Sequential Testing to Multi-Armed Bandits, Switchback Experiments to Stratified Sampling, Timothy Chan, Data Science Lead, is here to unravel the mysteries of these powerful methodologies that are revolutionizing how we approach testing.

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Why Best-of-Breed is a Better Choice than All-in-One Platforms for Data Science

O'Reilly on Data

So you need to redesign your company’s data infrastructure. Do you buy a solution from a big integration company like IBM, Cloudera, or Amazon? Do you engage many small startups, each focused on one part of the problem? A little of both? We see trends shifting towards focused best-of-breed platforms. That is, products that are laser-focused on one aspect of the data science and machine learning workflows, in contrast to all-in-one platforms that attempt to solve the entire space of data workflow

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20+ Machine Learning Datasets & Project Ideas

KDnuggets

Upgrading your machine learning, AI, and Data Science skills requires practice. To practice, you need to develop models with a large amount of data. Finding good datasets to work with can be challenging, so this article discusses more than 20 great datasets along with machine learning project ideas for you to tackle today.

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Doing Power BI the Right Way: 7. Validating data model results – Part 2

Paul Turley

Moving important business data into a data model for analytic reporting can often be a two-edge sword. Data retrieval is fast and can support all kinds of analytic trending and comparisons. But, data in the model may be one or two layers away from the original source data, making it more challenging to compare with familiar user reports. Often the first validation effort after transforming and loading data into the model and then visualizing the initial results is having a business user say " ye

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BI Is Dead; Long Live BI

Boris Evelson

The perception of legacy enterprise business intelligence (BI) platforms comes with some legitimate stigma and baggage. It’s technology first, not business-led; the graphical user interface (GUI)-based user experience (UX) doesn’t address ease of use for all business decision-makers; there are too many underutilized reports and dashboards floating around in the enterprise; and signals produced by […].

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The Key to Sustainable Energy Optimization: A Data-Driven Approach for Manufacturing

Speaker: Kevin Kai Wong, President of Emergent Energy Solutions

In today's industrial landscape, the pursuit of sustainable energy optimization and decarbonization has become paramount. ♻️ Manufacturing corporations across the U.S. are facing the urgent need to align with decarbonization goals while enhancing efficiency and productivity. Unfortunately, the lack of comprehensive energy data poses a significant challenge for manufacturing managers striving to meet their targets. 📊 Join us for a practical webinar hosted by Kevin Kai Wong of Emergent Ene