2020

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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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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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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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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

Marketing 362
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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.

More Trending

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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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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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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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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.

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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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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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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.

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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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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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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

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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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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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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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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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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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.

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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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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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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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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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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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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 – 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
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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.

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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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How to Deliver a Modern Data Experience Your Customers Will Love

In embedded analytics, keeping up with the pace of innovation is challenging. Download Qrvey's guide to ensure your analytics keep pace so you can solve your user's biggest challenges, delight them, and set your product apart from the competition. The guide outlines how to use embedded analytics to: Increase user satisfaction Go to market faster Create additional opportunities to monetize your product It also shares what to look for to ensure your embedded analytics are keeping up with the lates