Smart Data Collective

SEPTEMBER 15, 2020

Python has historically been the most popular language for big data applications. However, there is a growing need for JavaScript programmers that have a background in data science. is one of the JavaScript libraries that is becoming more popular for data science applications. However, when it comes to React.js

Cost-Benefit 87

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Data Science and Beyond

JANUARY 11, 2020

Being a data scientist can sometimes feel like a race against software innovations. What can one do to remain relevant in such an environment? Instead, I’d specify the model and let a probabilistic programming tool like pymc3 or Stan handle the rest. – Rob Reid, After On. Read this post to find out.

Software 103

Software Data Science Machine Learning Deep Learning 103

O'Reilly on Data

JULY 28, 2020

In this article, we turn our attention to the process itself: how do you bring a product to market? It’s often difficult for businesses without a mature data or machine learning practice to define and agree on metrics. Without clarity in metrics, it’s impossible to do meaningful experimentation. Identifying the problem.

Marketing 362

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DataRobot

DECEMBER 28, 2021

Yes, data created over the next three years will far exceed the amount created over the past 30 years ( Source : IDC Worldwide Global DataSphere Forecast, 2020-2024). But there’s only so much an organization can do, as it’s impossible to exponentially scale up the number of decisions a human can make. In DataRobot, starting with the 7.3

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Ontotext

DECEMBER 16, 2021

Why do I bring this up? Because without the right tools, searching for data in the life sciences is a lot like whale watching. Companies in the life sciences face data challenges on two fronts: Volume. Companies in the life sciences face data challenges on two fronts: Volume. Drug development.

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Domino Data Lab

JANUARY 11, 2021

Arming data science teams with the access and capabilities needed to establish a two-way flow of information is one critical challenge many organizations face when it comes to unlocking value from their modeling efforts. Domino Data Lab and Snowflake: Better Together. Introduction.

Recreation/Entertainment 103

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Juice Analytics

MARCH 11, 2021

At Juice, we’ve helped our clients launch dozens of data products that generate new revenue streams, differentiate their solutions in the market and build stronger customer relationships. In this series I’ll take you through what you need to know to design, build, launch, sell and support a data product.

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DataRobot Blog

APRIL 1, 2022

The Top Three Challenges in AI Preventing Your Organization from Getting to the Next Level (and How to Solve Them). Using AI is like using your treadmill (I’m a runner, so I’m a bit biased here) or any other sports equipment. Using AI is like using your treadmill (I’m a runner, so I’m a bit biased here) or any other sports equipment.

Machine Learning 59

Machine Learning Cost-Benefit Predictive Modeling Modeling 59

bridgei2i

APRIL 16, 2021

They talk about the buzzword that used to be AI and elaborate on the science behind Deep Neural Networks. Tune in to the podcast to know the differences between General AI and Applied AI, the science behind the black box that is AI, and whether AI is a function of datasets or a programmer’s intelligence. Divyansh: Absolutely.

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Jen Stirrup

SEPTEMBER 25, 2020

New and novice speakers – need help with your PASS Summit community event recordings? Certainly, if you take a look at some of the discussion on social media, it doesn’t take long to see that the SQL Family / Data Platform discussions online have become very heated. Get in touch and I can help. I have had enough.

Data Science 92

Data Science IT 92

DataRobot

JULY 29, 2021

In my experience, this happened most often when dealing with lawyers and academics, but more recently I’ve noticed the same behavior coming from data scientists. What was your most hated subject at school? That result resonates with me: while mathematics was my favorite subject, I absolutely hated doing long division!

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Alation

MARCH 8, 2022

When a data analyst exclaims, “I’ll just do it myself!”, They either can’t wait for someone else to find it or can’t explain exactly what they need, so they set off to search and dig and waste time looking for that data needle in the proverbial haystack. Business users gain a complete understanding of the available data.

Analytics 52

Analytics Data Governance Data-driven Data Science 52

bridgei2i

NOVEMBER 20, 2020

In the previous podcast, we heard Predeep Ghimrey, an expert in the CPG industry who talked about the use cases of Watchtower in the CPG domain. Ashwini has extensive experience in designing solutions for clients using advanced ML techniques to harness information from different forms of data. Ashwini: I am doing very well.

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Domino Data Lab

AUGUST 6, 2021

An enterprise starts by using a framework to formalize its processes and procedures, which gets increasingly difficult as data science programs grow. The complexity of models, and general limitations of expertise with data science among business leaders, creates an environment ripe for risk. Types of Model Risk.

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Smart Data Collective

MAY 20, 2021

Making a decision about your further education and career should not be done lightly. When completing a business analytics masters online, you will be taking a flexible course that works for you, letting you customize the degree to suit the industry you work in and allowing you to continue working alongside your studies.

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datapine

MAY 21, 2019

“BI is about providing the right data at the right time to the right people so that they can take the right decisions” – Nic Smith. Data analytics isn’t just for the Big Guys anymore; it’s accessible to ventures, organizations, and businesses of all shapes, sizes, and sectors. Let’s get started!

Business Intelligence 183

Business Intelligence Cost-Benefit Dashboards Slice and Dice 183

Insight

AUGUST 17, 2020

The Remote Experience While this was the first session for Insight in which all Fellows participated remotely, we’ve been offering a remote data science program since 2015. Kim Vo (Coaching & Development Lead, Interview Strategy Team) and Emily Kearney (Program Director, Data Science) during the fall session, September 2019.

Data Science 63

Data Science Data Processing Cost-Benefit Strategy 63

Ontotext

FEBRUARY 1, 2024

For example, the method for managing multilingual taxonomies became massively simpler (once concept could now hold multiple language labels), resulting in huge cost savings compared with the previous data model. The widespread adoption of RDF as a semantic data model transformed simple taxonomies into extensible and expressive ontologies.

Enterprise 59

Enterprise Management Metadata Modeling 59

Business Over Broadway

MARCH 13, 2019

Customer experience management (CXM) programs are necessarily a quantitative endeavor, requiring CX professionals to decipher insights from a sea of customer data. Customer Experience Management (CXM) programs rely on different types of data that come from a variety of sources. product, ease of doing business, technical support)?

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Domino Data Lab

JULY 22, 2019

Paco Nathan presented, “Data Science, Past & Future” , at Rev. At Rev’s “ Data Science, Past & Future” , Paco Nathan covered contextual insight into some common impactful themes over the decades that also provided a “lens” help data scientists, researchers, and leaders consider the future.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

Domino Data Lab

JANUARY 6, 2019

In Paco Nathan ‘s latest column, he explores the theme of “learning data science” by diving into education programs, learning materials, educational approaches, as well as perceptions about education. He is also the Co-Chair of the upcoming Data Science Leaders Summit, Rev. Learning Data Science.

Data Science 56

Data Science Machine Learning Reporting Visualization 56

O'Reilly on Data

APRIL 9, 2019

Or, why science and engineering are still different disciplines. "A A scientist can discover a new star, but he cannot make one. He would have to ask an engineer to do it for him.". A few months ago, I wrote about the differences between data engineers and data scientists. Let’s call this data scientist Bob.

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Insight

APRIL 9, 2019

NASA [Public domain] In this blog, I’ll discuss how I worked collaboratively with various domain experts, using reinforcement learning to develop innovative solutions in rocket engine development. In doing so, I’ll demonstrate the application of ML techniques to the manufacturing industry and the role of the Machine Learning Product Manager.

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Manufacturing Machine Learning Testing Software 75

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 this article, we’re going to address the need for big data in healthcare and hospital big data: why and how can it help?

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datapine

AUGUST 29, 2022

The saying “knowledge is power” has never been more relevant, thanks to the widespread commercial use of big data and data analytics. The rate at which data is generated has increased exponentially in recent years. Companies, both big and small, are seeking the finest ways to leverage their data into a competitive advantage.

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Alation

JANUARY 17, 2023

Chances are, you’ve heard of the term “modern data stack” before. In actual fact, it isn’t all that confusing at all, and understanding what it means can have huge benefits for your organization. In this article, I will explain the modern data stack in detail, list some benefits, and discuss what the future holds.

Data Warehouse 52

Data Warehouse Cost-Benefit Data Transformation Business Intelligence 52

O'Reilly on Data

OCTOBER 13, 2020

From a technical perspective, it is entirely possible for ML systems to function on wildly different data. For example, you can ask an ML model to make an inference on data taken from a distribution very different from what it was trained on—but that, of course, results in unpredictable and often undesired performance. I/O validation.

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Domino Data Lab

AUGUST 8, 2019

Paco Nathan ‘s latest monthly article covers Sci Foo as well as why data science leaders should rethink hiring and training priorities for their data science teams. In this episode I’ll cover themes from Sci Foo and important takeaways that data science teams should be tracking. Introduction.

Data Science 71

Data Science Machine Learning Data Governance Statistics 71

IBM Big Data Hub

JULY 6, 2023

These computer science terms are often used interchangeably, but what differences make each a unique technology? How do artificial intelligence, machine learning, deep learning and neural networks relate to each other? Whether you use AI applications based on ML or foundation models, AI can give your business a competitive advantage.

Deep Learning 64

Deep Learning Machine Learning Unstructured Data Modeling 64

DataRobot

DECEMBER 16, 2021

DataRobot is home to a treasure trove of Data Scientists. We’ve started a series called “Data Scientist Spotlight” as a way to meet the people behind the technology and introduce you to our great team. Bias and Fairness: This topic has been at the top of my mind since I started my career as a data scientist.

Forecasting 52

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Domino Data Lab

JUNE 2, 2019

Co-chair Paco Nathan provides highlights of Rev 2 , a data science leaders summit. We held Rev 2 May 23-24 in NYC, as the place where “data science leaders and their teams come to learn from each other.” Nick Elprin, CEO and co-founder of Domino Data Lab. Introduction. Leadership. In many, many ways.

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datapine

SEPTEMBER 16, 2022

datapine is filling your bookshelf thick and fast. Previously, we discussed the top 19 big data books you need to read, followed by our rundown of the world’s top business intelligence books as well as our list of the best SQL books for beginners and intermediates. Data visualization: What You Need To Know.

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The Unofficial Google Data Science Blog

JULY 18, 2023

by MICHAEL QUINN, JEREMY MILES, KA WONG As data scientists, we often encounter situations in which human judgment provides the ground truth. But humans often disagree, and groups of humans may disagree with each other systematically (say, experts versus laypeople). Some of this data is human-labeled.

Measurement 52

Measurement Metrics Uncertainty Slice and Dice 52

Domino Data Lab

JULY 21, 2019

Julia Lane , NYU Professor, Economist and cofounder of the Coleridge Initiative , presented “ Where’s the Data: A New Approach to Social Science Search & Discovery ” at Rev. Lane described the approach that the Coleridge Initiative is taking to address the science reproducibility challenge. Session Summary.

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Alation

FEBRUARY 13, 2020

It requires organizations to adopt a scientific mindset of testing and iteration to find the truth, rather than using data to support a predetermined path. According to a new survey from NewVantage Partners , 72% of executives say they have yet to forge a data culture, let alone a customer data culture. The reason?

Digital Transformation 59

Digital Transformation Insurance Data-driven Machine Learning 59

Domino Data Lab

FEBRUARY 4, 2019

In Paco Nathan ‘s latest column, he explores the role of curiosity in data science work as well as Rev 2 , an upcoming summit for data science leaders. Welcome back to our monthly series about data science. and dig into details about where science meets rhetoric in data science.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

FineReport

DECEMBER 10, 2019

Why We Need Data Cleaning?. Data analysis is a time-consuming task, but are you prepared before the data analysis, and have you omitted the important step: data cleaning? In the process of data analysis, data cleaning is such a preliminary preparation after data extraction. From Google.

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