2020

article thumbnail

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.

Data Lake 352
article thumbnail

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.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

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.

Analytics 363
article thumbnail

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.

article thumbnail

The Definitive Entity Resolution Buyer’s Guide

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.

article thumbnail

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.

article thumbnail

18 Examples Of Big Data Analytics In Healthcare That Can Save People

datapine

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.

More Trending

article thumbnail

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.

Modeling 145
article thumbnail

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 […].

article thumbnail

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?

article thumbnail

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

article thumbnail

How Intent Data Helps Marketers Convert A-List Accounts

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.

article thumbnail

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.

article thumbnail

Keeping Small Queries Fast – Short query optimizations in Apache Impala

Cloudera

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

article thumbnail

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.

article thumbnail

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.

article thumbnail

The Essential Guide to Analytic Applications

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.

article thumbnail

A Super Useful Month-by-Month Plan to Master Data Science in 2021

Analytics Vidhya

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.

article thumbnail

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.

article thumbnail

Practical Skills for The AI Product Manager

O'Reilly on Data

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

article thumbnail

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

Modeling 145
article thumbnail

Why Modern Data Challenges Require a New Approach to Governance

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.

article thumbnail

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 […].

article thumbnail

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.

article thumbnail

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.

article thumbnail

What Is Data Governance? (And Why Your Organization Needs It)

erwin

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?

article thumbnail

Value-Driven AI: Applying Lessons Learned from Predictive AI to Generative

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.

article thumbnail

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 […].

article thumbnail

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.

article thumbnail

Insight everywhere: The state of analytics in 2020

CIO Business Intelligence

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
article thumbnail

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.

Analytics 364
article thumbnail

From Complexity to Clarity: Strategies for Effective Compliance and Security Measures

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.

article thumbnail

How Leading Businesses Organize and Make Sense of Data

Smart Data Collective

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.

article thumbnail

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.

Modeling 360
article thumbnail

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

Modeling 143
article thumbnail

The Most Complete Guide to PyTorch for Data Scientists

MLWhiz

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.

article thumbnail

From Hadoop to Data Lakehouse

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.