David Menninger's Analyst Perspectives

NOVEMBER 13, 2020

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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Analytics Vidhya

DECEMBER 29, 2020

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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Analytics Vidhya

DECEMBER 24, 2020

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.

Analytics 363

Analytics 363

Analytics Vidhya

DECEMBER 18, 2020

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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Are you thinking of adding enhanced data matching and relationship detection to your product or service? Do you need to know more about what to look for when assessing your options? Our Entity Resolution Buyer’s Guide gives you step-by-step details about everything you should consider when evaluating entity resolution technologies. We discuss use cases, technology, and deployment options, top ten evaluation criteria and more.

Technology

Analytics Vidhya

JULY 26, 2020

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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datapine

OCTOBER 21, 2020

Big data has changed the way we manage, analyze, and leverage data across industries. One of the most notable areas where data analytics is making big changes is healthcare. In fact, healthcare analytics has the potential to reduce costs of treatment, predict outbreaks of epidemics, avoid preventable diseases, and improve the quality of life in general.

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Paul Turley

DECEMBER 25, 2020

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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TDAN

NOVEMBER 17, 2020

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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Dataiku

NOVEMBER 4, 2020

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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Domino Data Lab

OCTOBER 7, 2020

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

Statistics 145

Statistics Predictive Modeling Testing Modeling 145

Advertiser: ZoomInfo

One of the biggest challenges for any B2B marketer is understanding your prospects’ next move — who is most likely to buy and when. Without these insights, marketing campaigns can feel more like guesswork, with high investment and little return. We’re here to tell you there’s a better way. By tracking buyers’ digital footprints and online activity, such as website visits, product reviews, and spikes in content consumption, you can engage prospects with a message that really resonates.

Marketing

erwin

OCTOBER 8, 2020

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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Cloudera

NOVEMBER 13, 2020

This is part of our series of blog posts on recent enhancements to Impala. The entire collection is available here. Apache Impala is synonymous with high-performance processing of extremely large datasets, but what if our data isn’t huge? What if our queries are very selective? The reality is that data warehousing contains a large variety of queries both small and large; there are many circumstances where Impala queries small amounts of data; when end users are iterating on a use case, filterin

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MLWhiz

SEPTEMBER 1, 2020

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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Business Over Broadway

AUGUST 4, 2020

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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Embedding dashboards, reports and analytics in your application presents unique opportunities and poses unique challenges. We interviewed 16 experts across business intelligence, UI/UX, security and more to find out what it takes to build an application with analytics at its core. No matter where you are in your analytics journey, you will learn about emerging trends and gather best practices from product experts.

Analytics

Analytics Vidhya

DECEMBER 22, 2020

Overview Your quest to understand how to become a data scientist in 2021 ends here Here’s a month on month plan that you can. The post A Super Useful Month-by-Month Plan to Master Data Science in 2021 appeared first on Analytics Vidhya.

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Smart Data Collective

NOVEMBER 25, 2020

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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O'Reilly on Data

MAY 14, 2020

In our previous article, What You Need to Know About Product Management for AI , we discussed the need for an AI Product Manager. This role includes everything a traditional PM does, but also requires an operational understanding of machine learning software development, along with a realistic view of its capabilities and limitations. In this article, we shift our focus to the AI Product Manager’s skill set, as it is applied to day to day work in the design, development, and maintenance of AI p

Management 364

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Paul Turley

OCTOBER 10, 2020

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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A healthy data-driven culture minimizes knowledge debt while maximizing analytics productivity. Agile Data Governance is the process of creating and improving data assets by iteratively capturing knowledge as data producers and consumers work together so that everyone can benefit. It adapts the deeply proven best practices of Agile and Open software development to data and analytics.

Data-driven

TDAN

JUNE 30, 2020

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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Dataiku

APRIL 22, 2020

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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IBM Big Data Hub

MARCH 20, 2020

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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Statistics 145

erwin

AUGUST 28, 2020

Organizations with a solid understanding of data governance (DG) are better equipped to keep pace with the speed of modern business. In this post, the erwin Experts address: What Is Data Governance? Why Is Data Governance Important? What Is Good Data Governance? What Are the Key Benefits of Data Governance? What Is the Best Data Governance Solution?

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Speaker: Data Robot

Enterprise AI maturity has evolved dramatically over the past 5 years. Most enterprises have now experienced their first successes with predictive AI, but the pace and scale of impact have too often been underwhelming. Now generative AI has emerged and captivated the minds and imaginations of leaders and innovators everywhere. Join our DataRobot experts to reflect on lessons learned from helping hundreds of enterprises grow their AI maturity over the past 5 years.

Enterprise

Boris Evelson

SEPTEMBER 21, 2020

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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KDnuggets

JANUARY 16, 2020

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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CIO Business Intelligence

JULY 13, 2020

The world bought into the idea of continual improvement ever since Japanese car companies embraced it in the ‘70s and ate Detroit's lunch. But you can’t improve what you can’t measure – which is why analytics now envelops the entire enterprise, crunching every data set it can find to get a clear view of current reality and suggest a better road ahead.

Analytics 142

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Analytics Vidhya

DECEMBER 15, 2020

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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Speaker: Erika R. Bales, Esq.

When we talk about “compliance and security," most companies want to ensure that steps are being taken to protect what they value most – people, data, real or personal property, intellectual property, digital assets, or any other number of other things - and it’s more important than ever that safeguards are in place. Let’s step back and focus on the idea that no matter how complicated the compliance and security regime, it should be able to be distilled down to a checklist.

Measurement

Smart Data Collective

SEPTEMBER 21, 2020

Two or three decades ago, gathering data was the biggest challenge businesses faced. Leaders craved more information and access. Today, these same companies are drowning in data. The challenge of today is organizing and making sense of the data. 4 Tips to Help You Make Sense of Your Data. With so much emphasis on collecting and accessing data, it’s easy to become so paralyzed by information that you fail to do anything with it.

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O'Reilly on Data

MAY 18, 2020

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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Modeling Risk Software Data Processing 360

Paul Turley

OCTOBER 18, 2020

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

Modeling 143

Modeling Dashboards Analytics Visualization 143

MLWhiz

SEPTEMBER 7, 2020

PyTorch has sort of became one of the de facto standards for creating Neural Networks now, and I love its interface. Yet, it is somehow a little difficult for beginners to get a hold of. I remember picking PyTorch up only after some extensive experimentation a couple of years back. To tell you the truth, it took me a lot of time to pick it up but am I glad that I moved from Keras to PyTorch.

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Getting off of Hadoop is a critical objective for organizations, with data executives well aware of the significant benefits of doing so. The problem is, there are few options available that minimize the risk to the business during the migration process and that’s one of the reasons why many organizations are still using Hadoop today. By migrating to the data lakehouse, you can get immediate benefits from day one using Dremio’s phased migration approach.

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