DataKitchen
FEBRUARY 1, 2021
Every DataOps initiative starts with a pilot project. How do you choose a project that matters to people? DataOps addresses a broad set of use cases because it applies workflow process automation to the end-to-end data-analytics lifecycle. DataOps reduces errors, shortens cycle time, eliminates unplanned work, increases innovation, improves teamwork, and more.
Analytics Vidhya
FEBRUARY 24, 2021
ArticleVideo Book Introduction to Artificial Intelligence and Machine Learning Artificial Intelligence (AI) and its sub-field Machine Learning (ML) have taken the world by storm. The post A Comprehensive Step-by-Step Guide to Become an Industry Ready Data Science Professional appeared first on Analytics Vidhya.
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
David Menninger's Analyst Perspectives
FEBRUARY 8, 2021
The industry is making huge strides with artificial intelligence (AI) and machine learning (ML). There is more data available to analyze. Analytics vendors have made it easier to build and deploy models, and AI/ML is being embedded into many types of applications. Organizations are realizing the value that AI/ML provides and there are now millions of professionals with AI or ML in their title or job description.
O'Reilly on Data
FEBRUARY 19, 2021
Here are some of the most significant themes we see as we look toward 2021. Some of these are emerging topics and others are developments on existing concepts, but all of them will inform our thinking in the coming year. MLOps FTW. MLOps attempts to bridge the gap between Machine Learning (ML) applications and the CI/CD pipelines that have become standard practice.
Speaker: Timothy Chan, PhD., Head of Data Science
Are you ready to move beyond the basics and take a deep dive into the cutting-edge techniques that are reshaping the landscape of experimentation? 🌐 From Sequential Testing to Multi-Armed Bandits, Switchback Experiments to Stratified Sampling, Timothy Chan, Data Science Lead, is here to unravel the mysteries of these powerful methodologies that are revolutionizing how we approach testing.
Rocket-Powered Data Science
FEBRUARY 12, 2021
When I was growing up, friends at school would occasionally ask me if my older brother and I were twins. We were not, though we looked twin-like! As I grew tired of answering that question, one day I decided to give a more thoughtful answer to the question (beyond a simple “No”). I replied: “Yes, we are twins. We were born 20 months apart!” My response caused the questioner to pause and think about what I said, and perhaps reframe their own thinking.
TDAN
FEBRUARY 16, 2021
With the new year events well behind us, we’re steadily focused on moving forward in 2021. While we have seen a change in the calendar year, one initiative that continues to be a top priority for businesses is storing, managing, accessing and optimizing corporate data. Given that, let’s consider what I believe will be some […].
Data Leaders Brief brings together the best content for data, strategy, and BI professionals from the widest variety of industry thought leaders.
Analytics Vidhya
FEBRUARY 18, 2021
ArticleVideos This article was published as a part of the Data Science Blogathon. Introduction Machine learning.Sounds cool right? When I see those two words, The post Introduction to Machine Learning for Absolute Beginners appeared first on Analytics Vidhya.
David Menninger's Analyst Perspectives
FEBRUARY 24, 2021
Ventana Research recently announced its 2021 market agenda for Analytics , continuing the guidance we’ve offered for nearly two decades to help organizations derive optimal value from technology investments to improve business outcomes.
O'Reilly on Data
FEBRUARY 26, 2021
A couple of years ago, Pete Skomoroch, Roger Magoulas, and I talked about the problems of being a product manager for an AI product. We decided that would be a good topic for an article, and possibly more. After Pete and I wrote the first article for O’Reilly Radar, it was clear that there was “more”–a lot more. We then added Justin Norman, VP of Data Science at Yelp, to the team.
Paul Blogs on BI
FEBRUARY 1, 2021
It’s been over 13 years since SAP acquired BusinessObjects and really stepped into the world of Business Intelligence. During the first few years after the purchase, SAP sold a ton of BusinessObjects licenses and, for at least one or two years, they sold more BusinessObjects licenses than they did SAP ERP licenses. It was a huge success both financially and directionally for SAP.
Speaker: David Bard, Principal at VP Product Coaching
In the fast-paced world of digital innovation, success is often accompanied by a multitude of challenges - like the pitfalls lurking at every turn, threatening to derail the most promising projects. But fret not, this webinar is your key to effective product development! Join us for an enlightening session to empower you to lead your team to greater heights.
Smart Data Collective
FEBRUARY 4, 2021
Companies are discovering the countless benefits of using big data as they strive to keep their operations lean. Big data technology has made it a lot easier to maintain a decent profit margin as they try to keep their heads above water during a horrific economic downturn. One of the most important benefits of using big data is with expense tracking.
Dataiku
FEBRUARY 19, 2021
In January, our Dataiku Lab team presented their annual findings for up-and-coming machine learning (ML) trends , based on the work they do in machine learning research. In this series, we're going to break up their key topics (trustworthy ML, human-in-the-loop ML, causality, and the connection between reinforcement learning and AutoML) so they're easy for you to digest as you aim to optimize your ML projects in 2021.
Analytics Vidhya
FEBRUARY 20, 2021
ArticleVideos This article was published as a part of the Data Science Blogathon. Introduction With the motivation of award-winning from Analytics Vidhya Blogathon3 continuing. The post ML Model Deployment with Webhosting frameworks appeared first on Analytics Vidhya.
David Menninger's Analyst Perspectives
FEBRUARY 26, 2021
Ventana Research recently announced its 2021 Market Agenda for data, continuing the guidance we have offered for nearly two decades to help organizations derive optimal value and improve business outcomes.
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.
Domino Data Lab
FEBRUARY 4, 2021
Container technology has changed the way data science gets done. The original container use case for data science focused on what I call, “environment management”. Configuring software environments is a constant chore, especially in the open source software space, the space in which most data scientists work. It often requires trial and error. This tinkering may break dependencies such as those between software packages or between drivers and applications.
DataCamp
FEBRUARY 19, 2021
Our infographic explains how the open-source revolution has transformed data science and maps out how to become fluent in data manipulation, data visualization, machine learning, reporting and communicating data, and natural language processing.
Smart Data Collective
FEBRUARY 16, 2021
Big data has shed some important insights on a number of facets of modern organizational functions. One of the areas that has been shaped by big data is cybersecurity. We have talked about the importance of using big data to strengthen cybersecurity by creating more robust defenses. However, there are also less direct reasons that big data can be important for stopping cyberattacks.
Dataiku
FEBRUARY 12, 2021
Following OpenAI’s update of the latest staged release of GPT-2, it’s a good time to reflect on what the project means for the future of AI’s openness, but more broadly — and probably more importantly — for the future of regulation and openness in the field.
Advertisement
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.
Analytics Vidhya
FEBRUARY 12, 2021
ArticleVideos This article was published as a part of the Data Science Blogathon. Introduction A step-by-step guide to getting started with Seaborn! If matplotlib. The post A Beginner’s Guide To Seaborn: The Simplest Way to Learn appeared first on Analytics Vidhya.
Teradata
FEBRUARY 9, 2021
Everyone knows that data is vital for success in retail. But without a clear data strategy, retailers often eat up resources fighting small-scale battles, whilst gradually losing the war.
Domino Data Lab
FEBRUARY 19, 2021
spaCy is a python library that provides capabilities to conduct advanced natural language processing analysis and build models that can underpin document analysis, chatbot capabilities, and all other forms of text analysis. The spaCy library is available under the MIT license and is developed primarily by Matthew Honnibal and Ines Montani from E xplosion.AI.
DataCamp
FEBRUARY 3, 2021
Making better, safer vehicles requires a data-driven approach. Data science unlocks better mobility solutions for all with connected and autonomous vehicles.
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.
Cloudera
FEBRUARY 23, 2021
Meet Kevin Smith, a Staff Customer Operations Engineer within the US Public Sector support team. He sums up his day-to-day by saying he works directly with clients on technical cases and provides support and guidance as they troubleshoot unexpected behavior. He also serves as a member of several project teams focusing on upgrade experiences, internal tools, product testing, training, and documentation.
Dataiku
FEBRUARY 4, 2021
In the prior posts in the How They Work (In Plain English!) series, we went through a high-level overview of machine learning and explored two key categories of supervised learning algorithms ( linear and tree-based models ), two key unsupervised learning techniques ( clustering and dimensionality reduction ), and recommendation engines which can use either supervised or unsupervised learning.
Analytics Vidhya
FEBRUARY 5, 2021
ArticleVideos This article was published as a part of the Data Science Blogathon. Introduction Hi! Young Data Science enthusiast, Let’s understand key packages for. The post Key Python Packages for Data Science appeared first on Analytics Vidhya.
Paul Turley
FEBRUARY 14, 2021
Part of the the series: Doing Power BI the Right Way Although my professional focus is building enterprise-scale BI solutions, I’ve created my share of informal Power BI reports that were put together quickly, with the goal to create something “good enough” rather then achieving perfection.
Advertisement
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.
Business Over Broadway
FEBRUARY 14, 2021
A recent survey by Kaggle revealed that data professionals used a variety of different ML algorithms in their work. On average, data professionals used two (median) algorithms. The most frequently used algorithms were 1) linear/logistic regression, 2) decision trees/random forests and 3) Convolutional Neural Networks. The total number of and use of specific algorithms varied across job titles, with ML engineers using the most (4) and DBA/Database Engineers using the least (1).
Smart Data Collective
FEBRUARY 8, 2021
It wasn’t long ago that we wrote about how big data was setting new standards in the field of web design. More web developers are going to rely heavily on data-driven technology to improve the quality of their work in the near future. The way we do things digitally is only getting more advanced. The more we learn about technology, the more we want to build it and use it across different platforms.
Cloudera
FEBRUARY 8, 2021
The digital revolution is making a deep impact on the automotive industry, offering practically unlimited possibilities for more efficient, convenient, and safe driving and travel experiences in connected vehicles. This revolution is just beginning to accelerate – in fact, according to a recent Applied Market Research study, the global connected car market was valued at $63.03 billion in 2019, and is projected to reach $225.16 billion by 2027, registering a CAGR of 17.1% from 2020 to 2027.
Dataiku
FEBRUARY 8, 2021
Reinforcement learning is a technique largely used for training gaming AI — like making a computer win at Go or finish Super Mario Bros levels super fast. That's great, but to some extent, this use case isn’t very exciting or useful. Yet reinforcement learning is also the potential first step toward bringing intuition to AI in business contexts, filling the gap between abductive reasoning and inductive reasoning.
Advertiser: Data Robot
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.
Expert insights. Personalized for you.
245 
Let's personalize your content