2021

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Tech workers warned they were going to quit. Now, the problem is spiralling out of control

DataKitchen

The post Tech workers warned they were going to quit. Now, the problem is spiralling out of control first appeared on DataKitchen.

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10 Power BI mistakes to avoid

CIO Business Intelligence

As a leading business intelligence tool, Power BI offers business users power and flexibility in dealing with data. The Microsoft tool provides everything from Excel integration to enterprise reporting and an increasing number of AI features that simplify getting deeper insights. In fact, the latest Forrester Wave report on augmented BI goes as far as to say “it is hard not to consider Power BI as your top choice for an enterprise BI platform.

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5 hot new IT jobs — and why they just might stick

DataKitchen

The post 5 hot new IT jobs — and why they just might stick first appeared on DataKitchen.

IT 356
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Data-driven 2021: Predictions for a new year in data, analytics and AI

DataKitchen

The post Data-driven 2021: Predictions for a new year in data, analytics and AI first appeared on DataKitchen.

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From Developer Experience to Product Experience: How a Shared Focus Fuels Product Success

Speaker: Anne Steiner and David Laribee

As a concept, Developer Experience (DX) has gained significant attention in the tech industry. It emphasizes engineers’ efficiency and satisfaction during the product development process. As product managers, we need to understand how a good DX can contribute not only to the well-being of our development teams but also to the broader objectives of product success and customer satisfaction.

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The Fundamentals of Exploratory Data Analysis

Analytics Vidhya

This article was published as a part of the Data Science Blogathon Table of Contents Introduction About the Dataset Let’s Go 2D Scatter Plot 3D Scatter Plot Pair Plot Histogram Univariate Analysis using PDF CDF Mean, Variance, and Standard Deviation Median, Percentile, Quantile, IQR, MAD Box Plot Violin Plot Multivariate Probability Density Contour Plot Final Note […].

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2021 Data/AI Salary Survey

O'Reilly on Data

In June 2021, we asked the recipients of our Data & AI Newsletter to respond to a survey about compensation. The results gave us insight into what our subscribers are paid, where they’re located, what industries they work for, what their concerns are, and what sorts of career development opportunities they’re pursuing. While it’s sadly premature to say that the survey took place at the end of the COVID-19 pandemic (though we can all hope), it took place at a time when restrictions were loose

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Isn’t it time community speakers were all treated reasonably and equally by organizers?

Jen Stirrup

TL; DR Yes. Let me tell you about the second-worst conference experience I’ve ever had as a speaker. The worst experience happened a few years ago and it is at the foot of the post so you will have to read down – note, it does have a trigger warning. Speakers from diverse backgrounds want equality not preferential treatment. Let me explain a scenario and let me make it clear that I’m not asking for preferential treatment.

IT 145
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Anima Anandkumar: What’s in the Future for AI?

DataRobot

Anima Anandkumar joined Ben Taylor, Chief AI Evangelist at DataRobot, on the More Intelligent Tomorrow podcast to discuss the future direction of AI technology and its possible enhancement by the addition of more human capabilities. Bren Professor of Technology at California Institute of Technology (CalTech), Anima joined Nvidia three years ago as the Director of Machine Learning Research.

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3 Practices to Help Build a Strong Data Culture

Dataiku

Let’s be frank — creating a lasting data culture in your company isn’t going to happen overnight. No technology you install or datasets you gather will do that for you. You need time and, as we’ve seen across pop culture, it usually takes a new idea or innovation (or an old idea packaged as new) to change culture. This change usually falls on data leaders to drive because they have a unique perspective across data, technology, and the organization.

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The Role of Model Governance in Machine Learning and Artificial Intelligence

Domino Data Lab

In the world of machine learning (ML) and artificial intelligence (AI), governance is a lifelong pursuit. All models require testing and auditing throughout their deployment and, because models are continually learning, there is always an element of risk that they will drift from their original standards. As such, model governance needs to be applied to each model for as long as it’s being used.

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Peak Performance: Continuous Testing & Evaluation of LLM-Based Applications

Speaker: Aarushi Kansal, AI Leader & Author and Tony Karrer, Founder & CTO at Aggregage

Software leaders who are building applications based on Large Language Models (LLMs) often find it a challenge to achieve reliability. It’s no surprise given the non-deterministic nature of LLMs. To effectively create reliable LLM-based (often with RAG) applications, extensive testing and evaluation processes are crucial. This often ends up involving meticulous adjustments to prompts.

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Converting Big Data into Actionable Intelligence

TDAN

In today’s world, access to data is no longer a problem. There are such huge volumes of data generated in real-time that several businesses don’t know what to do with all of it. Unless big data is converted to actionable insights, there is nothing much an enterprise can do. And outdated data models no longer […].

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A Developer’s Guide to Creating Bad Power BI Projects – Part 1

Paul Turley

It occurred to me that we have put so much effort into promoting best practices and proper design that there is far less information about how to create bad reports and data models. In that light, the purpose of this article is to talk about what to do if you want things to go poorly and make sure your projects fail - if not immediately, then sometime in the future - and if not for you then for whoever inherits the work that you have done.

Reporting 145
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What Mature Data Infrastructure Looks Like

DataCamp

Unlocking the value of data in an organization starts with having the right data infrastructure and tooling foundations. Here’s a look at the current state and future trends of data infrastructure.

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Turning the page

Cloudera

Today marks the beginning of an exciting new chapter for Cloudera. Cloudera will become a private company with the flexibility and resources to accelerate product innovation, cloud transformation and customer growth. Cloudera will benefit from the operating capabilities, capital support and expertise of Clayton, Dubilier & Rice (CD&R) and KKR – two of the most experienced and successful global investment firms in the world recognized for supporting the growth strategies of the businesses

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How to Build an Experimentation Culture for Data-Driven Product Development

Speaker: Margaret-Ann Seger, Head of Product, Statsig

Experimentation is often seen as an aspirational practice, especially at smaller, fast-moving companies who are strapped for time and resources. So, how can you get your team making decisions in a more data-driven way while continuing to remain lean and maintaining ship velocity? In this webinar, Margaret-Ann Seger, Head of Product at Statsig, will teach you how to build an experimentation culture from the ground-up, graduating from just getting started with data-driven development to operating

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How to Extract Tabular Data from Doc files Using Python?

Analytics Vidhya

This article was published as a part of the Data Science Blogathon Introduction Data is present everywhere. Any action we perform generates some or the other form of data. But this data might not be present in a structured form. A beginner starting with the data field is often trained for datasets in standard formats like […]. The post How to Extract Tabular Data from Doc files Using Python?

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The Next Generation of AI

O'Reilly on Data

Programs like AlphaZero and GPT-3 are massive accomplishments: they represent years of sustained work solving a difficult problem. But these problems are squarely within the domain of traditional AI. Playing Chess and Go or building ever-better language models have been AI projects for decades. The following projects have a different flavor: In February, PLOS Genetics published an article by researchers who are using GANs (Generative Adversarial Networks) to create artificial human genomes.

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Why Machine Learning Engineers are Replacing Data Scientists

KDnuggets

The hiring run for data scientists continues along at a strong clip around the world. But, there are other emerging roles that are demonstrating key value to organizations that you should consider based on your existing or desired skill sets.

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How to Utilize Artificial Intelligence in Your eCommerce SEO Strategy

Smart Data Collective

If you have not lived under a rock for several years, you have undoubtedly heard about artificial intelligence (AI). However, how might artificial intelligence be used in e-commerce operations? Artificial intelligence (AI) is starting to fill every facet of our daily lives. For example, self-checkout cash registers, airport security checks, and other automated processes all use artificial intelligence to some degree.

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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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Humans and AI: Bargaining Power

DataRobot

I have a confession to make—I’m a back-seat driver! When sitting in a taxi, I can’t help but grumble when the ride isn’t smooth, or the driver chooses the slowest lane of traffic. I have to fight the urge to take control. When it comes to shopping, I passively accept what is offered for sale. But my wife, who grew up in Asia where haggling is part of the culture, is different.

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Medical Data and Advanced Robotics: Saving Lives, Improving Care

Sisense

Blog. Imagine a future where a wide range of surgeries, no matter how complex, could be conducted remotely, a future where a patient in dire need of help could access the most highly regarded specialists in any area of medicine regardless of where on the globe that person may be. In 2019, Dr. Ryan Madder from Spectrum Health performed a series of simulated remote percutaneous coronary interventions (PCIs) via a control station outside of Boston.

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10 Tips to Visualize Data Like a Pro

Juice Analytics

Have you nailed all the data visualization basics? Stuff like… ?You know pie charts are bad except in certain specific use cases; ?You can spot chartjunk from a mile away; ?You confidently pick the right kind of chart based on what you want to emphasize in the data; ?You use just the right amount of color to bring meaning, but not so much as to distract; ?

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How to Win New Business with External Data

TDAN

Increasingly, external data (alternative data, public data, open data – call it what you want) is being called the “secret sauce” of driving advanced analytics, developing machine learning and AI capabilities, enriching existing models, and delivering unrealized insights to every part of your organization. The difficulty in connecting to this data is top of mind for […].

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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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Drill-through from Power BI to Paginated Report – Report Recipe #4

Paul Turley

Navigation between reports is the hallmark of an interactive reporting solution, enabling the ability to drill-through and see relevant details and contextual filtered information in a target report.

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Choosing the right Machine Learning Framework

Domino Data Lab

Machine learning (ML) frameworks are interfaces that allow data scientists and developers to build and deploy machine learning models faster and easier. Machine learning is used in almost every industry, notably finance , insurance , healthcare , and marketing. Using these tools, businesses can scale their machine learning efforts while maintaining an efficient ML lifecycle.

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Data Intelligence in the Next Normal; Why, Who and When?

erwin

While many believe that the dawn of a new year represents a clean slate or a blank canvas, we simply don’t leave the past behind by merely flipping over a page in the calendar. As we enter 2021, we will also be building off the events of 2020 – both positive and negative – including the acceleration of digital transformation as the next normal begins to be defined.

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Effective Data Visualization Techniques in Data Science Using Python

Analytics Vidhya

ArticleVideo Book This article was published as a part of the Data Science Blogathon Data Visualization Data Visualization techniques involve the generation of graphical or. The post Effective Data Visualization Techniques in Data Science Using Python appeared first on Analytics Vidhya.

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Reimagining CX: How to Implement Effective AI-Driven Transformations

Speaker: Steve Pappas, Chief Strategist, Startup and Early Stage Growth Advisor, Keynote Speaker, CX Podcaster

As businesses strive for success in an increasingly digitized world, delivering an exceptional customer experience has become paramount. To meet this demand, enterprises are embracing innovative approaches that captivate customers and fuel their loyalty. 💥 Enter conversational AI - an absolute game-changer (if done right) in redefining CX norms.

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AI Adoption in the Enterprise 2021

O'Reilly on Data

During the first weeks of February, we asked recipients of our Data & AI Newsletter to participate in a survey on AI adoption in the enterprise. We were interested in answering two questions. First, we wanted to understand how the use of AI grew in the past year. We were also interested in the practice of AI: how developers work, what techniques and tools they use, what their concerns are, and what development practices are in place.

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3 Differences Between Coding in Data Science and Machine Learning

KDnuggets

The terms ‘data science’ and ‘machine learning’ are often used interchangeably. But while they are related, there are some glaring differences, so let’s take a look at the differences between the two disciplines, specifically as it relates to programming.

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Data Strategy: The Missing Link in Your Digital Transformation Plan

Smart Data Collective

On the surface, digital transformation is a simple process. You take a business activity that is old and ineffective/inefficient and re-energize it with new technology. This could be moving your spreadsheets to cloud software or even going so far as to move up from paper to digital. There is nothing inherently wrong with this process, but the way digital transformation is deployed often misses powerful opportunities ripe for the taking.

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Humans and AI: Should We Describe AI as Autonomous?

DataRobot

Beware the hype about AI systems. Although AI is powerful and generates trillions of dollars of economic value across the world, what you see in science fiction movies remains pure fiction. In this blog post, I will focus on the use of the word autonomous , the dangers of using it with stakeholders, and, in the context of customer experience, the inaccurate perception that all things can be automated, eliminating the need for interactions between employees and customers.

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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.