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  • Unleash Big Data by SaaS-based End-to-End AutoML

    This SaaS-based end-to-end AutoML tool R2 Learn enables data scientists, developers and data analysts to increase productivity, reduce errors and build quality models. Try for Free today!

  • MLaaS: Best Practices for Machine Learning as a Service platform, Apr 5 Webinar

    Learn how Machine Learning as a Service initiatives bring Data Scientists, IT and Analytic Operations together to deploy and scale more models.

  • AI is not at all like Mobile/Cloud/SaaS

    AI is a hard problem and will take much longer to solve in any scope. The sudden uptick in interest may revert back to normal, but the cycle of work will be longer, much more diverse, and interesting than Mobile/Cloud/SaaS.

  • Pivotal HD ODBMS Interview with Scott Yara and Florian Waas

    ODBMS Editor Roberto Zicari talks to leaders of the new Pivotal about their new platform and Pivotal HD - their own Hadoop version.

  • Maastricht Summer School On Data Mining, Aug 27-30, The Netherlands

    This intensive 4-day introduction to data mining methods and applications balance theory and practice, with each lecture accompanied by a lab. This school is intended for students, scientists, engineers, and experts in specific fields who need to apply data-mining techniques to their fields.

  • SaaS Analytics Solutions

    Analytics 1305, provides scalable machine learning software for large data; specializing in non-parametric methods, such as nearest neighbors, kernel density estimation, local regression, support vector Read more »

  • Cloud Analytics and SaaS Providers, offering API to embed popular machine learning algorithms into applications; R as a service. Alpine Data Labs, helps you uncover the predictive analytic power Read more »

  • A List of 7 Best Data Modeling Tools for 2023

    Learn about data modeling tools to create, design and manage data models, allowing data scientists to access and use them more quickly.

  • 5 Ways to Deal with the Lack of Data in Machine Learning

    Effective solutions exist when you don't have enough data for your models. While there is no perfect approach, five proven ways will get your model to production.

  • From Data Collection to Model Deployment: 6 Stages of a Data Science Project

    Here are 6 stages of a novel Data Science Project; From Data Collection to Model in Production, backed by research and examples.

  • Beginner’s Guide to Cloud Computing

    Learn how cloud computing works, different types of models, top cloud platforms, and applications.

  • Where Collaboration Fails Around Data (And 4 Tips for Fixing It)

    Data-driven organizations require complex collaboration between data teams and business stakeholders. Here are 4 proactive tips for reducing information asymmetries and achieving better collaboration.

  • Key Data Science, Machine Learning, AI and Analytics Developments of 2022

    It's the end of the year, and so it's time for KDnuggets to assemble a team of experts and get to the bottom of what the most important data science, machine learning, AI and analytics developments of 2022 were.

  • The Complete Machine Learning Study Roadmap

    KDnuggets Top Blog Find out where you need to be to start your Machine Learning journey and what you need to do to succeed in the field.

  • What is a Function?

    This guide will help you understand the concepts of Javascript functions and their structure.

  • The Complete Data Engineering Study Roadmap

    KDnuggets Top Blog Everything you need to know to start your career in Data Engineering.

  • Getting Deep Learning working in the wild: A Data-Centric Course

    Data-centric learning resources are somewhat scattered today, and that’s why we developed a new Data Centric Deep Learning course on the co:rise education platform. It is an introduction to a set of approaches and best practices, for people who are trying to do deep learning in the wild.

  • 9 Skills You Need to Become a Data Engineer

    A data engineer is a fast-growing profession with amazing challenges and rewards. Which skills do you need to become a data engineer? In this post, we’ll take a look at both hard and soft skills.

  • Is OLAP Dead?

    OLAP enables citizen analysts to quickly, efficiently, and cost-effectively uncover new business insights at a reduced time-to-value.

  • Everything You Need to Know About Data Lakehouses

    Learn everything you need to know about data lakehouses.

  • How CoRise Helped Ben Wilson Land a New Job as a Analytics Engineer (and a Side Gig in Doodling)

    In this practical modern data stack course, you will implement a dbt project on a data warehouse from scratch and with a lot of support along the way!

  • Why Emily Ekdahl chose co:rise to level up her job performance as a machine learning engineer

    Find out what one of the first learners to complete the co:rise Machine Learning Foundations track said about her experience in the track and what she’s tackling next when she recently talked to Julia Stiglitz, co:rise co-founder and CEO.

  • 90% of Today’s Code is Written to Prevent Failure, and That’s a Problem

    Trying to anticipate and defend against these failures is the constant uphill battle that today’s engineers are up against. But it doesn’t have to be.

  • Introducing Objectiv: Open-source product analytics infrastructure

    Collect validated user behavior data that’s ready to model on without prepwork. Take models built on one dataset and deploy & run them on another.

  • Top 15 Books to Master Data Strategy

    In this article, we outline 15 books on topics ranging from the technical to the non-technical, to help you improve your understanding of end-to-end best practices related to data.

  • Every Engineer Should and Can Learn Machine Learning

    Read this interview with Sourabh Bajaj of co:rise, discussing the evolution of the ML role, how he designed the course to connect with today’s business needs, and how he thinks students can apply the covered topics at the end of each course!

  • 6 Things You Need To Know About Data Management And Why It Matters For Computer Vision

    This article will explore a few areas that we feel are essential when assessing data management solutions for computer vision.

  • The 6 Python Machine Learning Tools Every Data Scientist Should Know About

    KDnuggets Top Blog Let's look at six must-have tools every data scientist should use.

  • Software Developer vs Software Engineer

    KDnuggets Top Blog The terms developer and engineer are used synonymously, making it difficult to understand the difference between the two in the midst of a conversation.

  • MLOps: The Best Practices and How To Apply Them

    Here are some of the best practices for implementing MLOps successfully.

  • Getting Deep Learning working in the wild: A Data-Centric Course

    Data-centric learning resources are somewhat scattered today, and that’s why we developed a new Data Centric Deep Learning course on the co:rise education platform. It is an introduction to a set of approaches and best practices, for people who are trying to do deep learning in the wild.

  • 4 Factors to Identify Machine Learning Solvable Problems

    The near future holds incredible possibility for machine learning to solve real world problems. But we need to be be able to determine which problems are solvable by ML and which are not.

  • SQL Window Functions

    In this article, we’ll go over SQL window functions and how to use them when writing SQL queries.

  • People Management for AI: Building High-Velocity AI Teams

    Practical advice for managers and directors who are looking to build AI/ML teams.

  • Data Science Programming Languages and When To Use Them

    KDnuggets Top Blog Read this guide through the most common data science programming languages and when to use them in data science.

  • Data Warehousing with Snowflake for Beginners

    This tutorial provides only a brief synopsis of the data warehouse in Snowflake, which we will go through in more detail.

  • Transfer Learning for Image Recognition and Natural Language Processing

    Read the second article in this series on Transfer Learning, and learn how to apply it to Image Recognition and Natural Language Processing.

  • What Makes Python An Ideal Programming Language For Startups">Silver BlogWhat Makes Python An Ideal Programming Language For Startups

    In this blog, we will discuss what makes Python so popular, its features, and why you should consider Python as a programming language for your startup.

  • The Seven Best ELT Tools for Data Warehouses

    ELT helps to streamline the process of modern data warehousing and managing a business’ data. In this post, we’ll discuss some of the best ELT tools to help you clean and transfer important data to your data warehouse.

  • Sentiment Analysis with KNIME

    Check out this tutorial on how to approach sentiment classification with supervised machine learning algorithms.

  • Stop Blaming Humans for Bias in AI

    Can artificial intelligence be rid of bias? This is an important question, and it’s equally important that we look in the right place for the answer.

  • What Are NVIDIA NGC Containers & How to Get Started Using Them

    NVIDIA, the pioneer in the GPU technologies and deep learning revolution, has come up with an excellent catalog of specialized containers that they call NGC Collections. In this article, we explore their basic usage and some variations.

  • What’s missing from self-serve BI and what we can do about it

    The notion of self-service BI tools caught an expectation that they could provide a magic formula for easily helping everyone understand all the data. But, such an end-result isn't occurring in practice. To identify a better approach, we need to take a step back and determine what problem is actually trying to be solved.

  • How to Transform Your Data in Snowflake

    Data transformation is the biggest bottleneck in the analytics workflow. The modern approach to data pipelines is ELT, or extract, transform, and load, with data transformation performed in your Snowflake data warehouse. A new breed of “no-/low-code” data transformation tools, such as Datameer, are emerging to allow the wider analytics community to transform data on their own, eliminating analytics bottlenecks.

  • What Is The Real Difference Between Data Engineers and Data Scientists?

    To launch your data career, you’ll need both theoretical knowledge and applied skills. Bootcamp programs like Springboard’s Data Science Career Track and Data Engineering Career Track can help make you job-ready through hands-on, project-based learning and one-on-one mentorship. Wondering which data career path is right for you? Read on to find out.

  • Data Science Project Infrastructure: How To Create It

    The intension for most data science projects is to build something that people use. Creating something purposeful requires a solid infrastructure and processes that keeps problem-solving front-and-center for your audience.

  • Coding Ethics for AI & AIOps: Designing Responsible AI Systems

    AI ops has taken Human machine collaboration to the next level where humans and machines are not just coexisting but are collaborating and working together like team members.

  • Django’s 9 Most Common Applications">Gold BlogDjango’s 9 Most Common Applications

    Django is a Python web application framework enjoying widespread adoption in the data science community. But what else can you use Django for? Read this article for 9 use cases where you can put Django to work.

  • MLOps And Machine Learning Roadmap

    A 16–20 week roadmap to review machine learning and learn MLOps.

  • How To Transition From Data Freelancer to Data Entrepreneur (Almost Overnight)

    Data freelancers trade hours for dollars while data entrepreneurs have found a way to make money while they sleep. Ready to make the transition? Keep reading to learn how to do it as SEAMLESSLY and PROFITABLY as possible.

  • Predict Customer Churn (the right way) using PyCaret

    A step-by-step guide on how to predict customer churn the right way using PyCaret that actually optimizes the business objective and improves ROI.

  • BigQuery vs Snowflake: A Comparison of Data Warehouse Giants

    In this article we are going to compare the two topmost data warehouses: BigQuery and Snowflake.

  • Choosing the Right BI Tool for Your Business

    Here are six questions to ask as you search for the best BI tool for your specific needs.

  • How to pitch to VCs, explained: The Deck We Used to Raise Capital For Our Open-Source ELT Platform

    Winning seed funding from venture capitalists is a daunting task, and the pitch is key. Learn how one effective slide deck resulted in a successful early funding round for an open-source start-up, Airbyte.

  • A checklist to track your Data Science progress">Silver BlogA checklist to track your Data Science progress

    Whether you are just starting out in data science or already a gainfully-employed professional, always learning more to advance through state-of-the-art techniques is part of the adventure. But, it can be challenging to track of your progress and keep an eye on what's next. Follow this checklist to help you scale your expertise from entry-level to advanced.

  • Disentangling AI, Machine Learning, and Deep Learning

    The field of Artificial Intelligence is extremely broad and captures a winding history through the evolution of various sub-fields that experienced many ups and downs over the years. Appreciating AI within its historical contexts will enhance your communication with the public, colleagues, and potential hiring managers, as well as guide your thinking as you progress in the application and study of state-of-the-art techniques.

  • Models of Data Science teams: Chess vs Checkers

    Should we still consider data scientists and data engineers as separate roles? When should a team grow with full-stack data developers? Introducing the Checkers-like data team.

  • Overview of MLOps

    Building a machine learning model is great, but to provide real business value, it must be made useful and maintained to remain useful over time. Machine Learning Operations (MLOps), overviewed here, is a rapidly growing space that encompasses everything required to deploy a machine learning model into production, and is a crucial aspect to delivering this sought after value.

  • Top YouTube Machine Learning Channels

    These are the top 15 YouTube channels for machine learning as determined by our stated criteria, along with some additional data on the channels to help you decide if they may have some content useful for you.

  • A Critical Comparison of Machine Learning Platforms in an Evolving Market

    There’s a clear inclination towards the MLaaS model across industries, given the fact that companies today have an option to select from a wide range of solutions that can cater to diverse business needs. Here is a look at 3 of the top ML platforms for data excellence.

  • Data Science and Analytics Career Trends for 2021

    Let's check out what are the new data science and analytics career trends for 2021 that may also shape the career options in the future.

  • AI and Automation meets BI">Silver BlogAI and Automation meets BI

    Organizations use a variety of BI tools to analyze structured data. These tools are used for ad-hoc analysis, and for dashboards and reports that are essential for decision making. In this post, we describe a new set of BI tools that continue this trend.

  • 5 Most Useful Machine Learning Tools every lazy full-stack data scientist should use

    If you consider yourself a Data Scientist who can take any project from data curation to solution deployment, then you know there are many tools available today to help you get the job done. The trouble is that there are too many choices. Here is a review of five sets of tools that should turn you into the most efficient full-stack data scientist possible.

  • The Power of Spreadsheets for Achieving a Data Driven Culture [Nov 19 Webinar]

    Join Metis Senior Data Scientist Kevin Birnbaum this Thurs, Nov 19 @ 12 PM ET, as he explains how spreadsheets can help all employees get comfortable with data and empower them to perform their own analysis without hand holding by your advanced analytics team.

  • Top KDnuggets tweets, Oct 21-27: #MachineLearning can recover lost languages

    Also: Free Introductory Machine Learning Course From Amazon; Dataset Splitting Best Practices in #Python; 10 Underrated Python Skills; Computer Vision tells us how the presidential candidates really feel

  • 5 Best Practices for Putting Machine Learning Models Into Production

    Our focus for this piece is to establish the best practices that make an ML project successful.

  • Comparing the Top Business Intelligence Tools: Power BI vs Tableau vs Qlik vs Domo

    How smart are your organizations’ decisions? Do you have the right information to make those decisions in the first place?

  • Artificial Intelligence for Precision Medicine and Better Healthcare

    In this article, we will focus on various machine learning, deep learning models, and applications of AI which can pave the way for a new data-centric era of discovery in healthcare.

  • MathWorks Deep learning workflow: tips, tricks, and often forgotten steps

    Getting started in deep learning – and adopting an organized, sustainable, and reproducible workflow – can be challenging. This blog post will share some tips and tricks to help you develop a systematic, effective, attainable, and scalable deep learning workflow as you experiment with different deep learning models, datasets, and applications.

  • Content-Based Recommendation System using Word Embeddings

    This article explores how average Word2Vec and TF-IDF Word2Vec can be used to build a recommendation engine.

  • Setting Up Your Data Science & Machine Learning Capability in Python">Silver BlogSetting Up Your Data Science & Machine Learning Capability in Python

    With the rich and dynamic ecosystem of Python continuing to be a leading programming language for data science and machine learning, establishing and maintaining a cost-effective development environment is crucial to your business impact. So, do you rent or buy? This overview considers the hidden and obvious factors involved in selecting and implementing your Python platform.

  • A Tour of End-to-End Machine Learning Platforms

    An end-to-end machine learning platform needs a holistic approach. If you’re interested in learning more about a few well-known ML platforms, you’ve come to the right place!

  • Building a Content-Based Book Recommendation Engine

    In this blog, we will see how we can build a simple content-based recommender system using Goodreads data.

  • Deploy Machine Learning Pipeline on AWS Fargate">Gold BlogDeploy Machine Learning Pipeline on AWS Fargate

    A step-by-step beginner’s guide to containerize and deploy ML pipeline serverless on AWS Fargate.

  • Build and deploy your first machine learning web app">Gold BlogBuild and deploy your first machine learning web app

    A beginner’s guide to train and deploy machine learning pipelines in Python using PyCaret.

  • State of the Machine Learning and AI Industry

    Enterprises are struggling to launch machine learning models that encapsulate the optimization of business processes. These are now the essential components of data-driven applications and AI services that can improve legacy rule-based business processes, increase productivity, and deliver results. In the current state of the industry, many companies are turning to off-the-shelf platforms to increase expectations for success in applying machine learning.

  • ModelDB 2.0 is here!

    We are excited to announce that ModelDB 2.0 is now available! We have learned a lot since building ModelDB 1.0, so we decided to rebuild from the ground up.

  • The Most Useful Machine Learning Tools of 2020

    This articles outlines 5 sets of tools every lazy full-stack data scientist should use.

  • How To Build Your Own Feedback Analysis Solution

    Automating the analysis of customer feedback will sound like a great idea after reading a couple hundred reviews. Building an NLP solution to provide in-depth analysis of what your customers are thinking is a serious undertaking, and this guide helps you scope out the entire project.

  • Sharing your machine learning models through a common API

    DEEPaaS API is a software component developed to expose machine learning models through a REST API. In this article we describe how to do it.

  • Managing Machine Learning Cycles: Five Learnings from comparing Data Science Experimentation/ Collaboration Tools

    Machine learning projects require handling different versions of data, source code, hyperparameters, and environment configuration. Numerous tools are on the market for managing this variety, and this review features important lessons learned from an ongoing evaluation of the current landscape.

  • Google’s New Explainable AI Service">Gold BlogGoogle’s New Explainable AI Service

    Google has started offering a new service for “explainable AI” or XAI, as it is fashionably called. Presently offered tools are modest, but the intent is in the right direction.

  • Industry AI, Analytics, Machine Learning, Data Science Predictions for 2020

    Predictions for 2020 from a dozen innovative companies in AI, Analytics, Machine Learning, Data Science, and Data industry.

  • Would you buy insights from this guy? (How to assess and manage a Data Science vendor)

    With all the hype from data science vendors selling "actionable insights" to boost your company's bottom line, selecting your analytics partner should proceed through the same, careful process as any traditional business endeavor. Follow these questions and best practices to ensure you manage accordingly.

  • Advice for New and Junior Data Scientists

    If you are a new Data Scientist early in your professional journey, and you’re a bit confused and lost, then follow this advice to figure out how to best contribute to your company.

  • Three Methods of Data Pre-Processing for Text Classification

    This blog shows how text data representations can be used to build a classifier to predict a developer’s deep learning framework of choice based on the code that they wrote, via examples of TensorFlow and PyTorch projects.

  • Meet Neebo: The Virtual Analytics Hub

    Neebo is a SaaS solution that enables analytics teams to connect to, find, combine and collaborate on trusted data assets in hybrid cloud landscapes, and provides a unified access point where they can more effectively leverage all their analytics assets and knowledge. In this blog, we will highlight some of the features of Neebo and how they can completely transform the way analytics teams operate.

  • How to Make an Agile Team Work for Big Data Analytics

    Learn how to approach the challenges when merging an agile methodology into a data science team to bring out the best value for your Big Data products.

  • AutoML for Temporal Relational Data: A New Frontier

    While AutoML started out as an automation approach to develop optimal machine learning pipelines, extensions of AutoML to Data Science embedded products can now enable the processing of much more, including temporal relational data.

  • Top KDnuggets tweets, Oct 16-22: How YouTube is Recommending Your Next Video

    Also: The 5 Classification Evaluation Metrics Every Data Scientist Must Know; How to Recognize a Good Data Scientist Job From a Bad One; How to Easily Deploy Machine Learning Models Using Flask.

  • Platinum BlogEverything a Data Scientist Should Know About Data Management">Silver BlogPlatinum BlogEverything a Data Scientist Should Know About Data Management

    For full-stack data science mastery, you must understand data management along with all the bells and whistles of machine learning. This high-level overview is a road map for the history and current state of the expansive options for data storage and infrastructure solutions.

  • Using DC/OS to Accelerate Data Science in the Enterprise

    Follow this step-by-step tutorial using Tensorflow to setup a DC/OS Data Science Engine as a PaaS for enabling distributed multi-node, multi-GPU model training.

  • Four questions to help accurately scope analytics engineering project

    Being really good at scoping analytics projects is crucial for team productivity and profitability. You can consistently deliver on time if you work out the issue first, and these four questions can help you prepare.

  • What is Machine Behavior?

    The new emerging field that wants to study AI agents the way social scientists study humans.

  • A 2019 Guide to Speech Synthesis with Deep Learning

    In this article, we’ll look at research and model architectures that have been written and developed to do just that using deep learning.

  • What’s the Best Data Strategy for Enterprises: Build, buy, partner or acquire?

    Every large organization is investing heavily in building data solutions and tools. They are building data solutions from scratch when they could be taking advantage of readily available tools and solutions. Many organizations are re-inventing the wheel and wasting resources.

  • Big Data for Insurance

    The insurance industry has always been quite conservative; however, the adoption of new technologies is not just a modern trend but a necessity to maintain the competitive pace. In the modern digital era, Big Data technologies help to process vast amounts of information, increase workflow efficiency, and reduce operational costs. Learn more about the benefits of Big Data for insurance from our material.

  • Platinum BlogThe Death of Big Data and the Emergence of the Multi-Cloud Era">Gold BlogPlatinum BlogThe Death of Big Data and the Emergence of the Multi-Cloud Era

    The Era of Big Data is coming to an end as the focus shifts from how we collect data to processing that data in real-time. Big Data is now a business asset supporting the next eras of multi-cloud support, machine learning, and real-time analytics.

  • A Gentle Guide to Starting Your NLP Project with AllenNLP

    For those who aren’t familiar with AllenNLP, I will give a brief overview of the library and let you know the advantages of integrating it to your project.

  • How To Get Funding For AI Startups

    What are the biggest challenges AI startups have when pitching to investors? Learn how to grab their attention with these recommendations on how to start building your AI company.

  • How to Make a Success Story of your Data Science Team

    Today, data science is a crucial component for an organization's growth. Given how important data science has grown, it’s important to think about what data scientists add to an organization, how they fit in, and how to hire and build effective data science teams.

  • Understanding Cloud Data Services">Gold BlogUnderstanding Cloud Data Services

    Ready to move your systems to a cloud vendor or just learning more about big data services? This overview will help you understand big data system architectures, components, and offerings with an end-to-end taxonomy of what is available from the big three cloud providers.

  • Digible: Data Engineer [Denver, CO]

    Seeking an experienced Data Engineer to wrangle data, build data pipelines, and architect, build, and maintain our data warehouse & science infrastructure.  In partnership with our Data Scientist, the Engineer will also have the opportunity to scale and productionize machine learning algorithms.

  • Customer Churn Prediction Using Machine Learning: Main Approaches and Models

    We reach out to experts from HubSpot and ScienceSoft to discuss how SaaS companies handle the problem of customer churn prediction using Machine Learning.

  • Intuit: Sr Manager Data and Analytics – Speech and Text Insights [Mountain View, CA]

    Seeking a Sr Manager Data and Analytics for Speech and Text Insights, to oversee the design, development, deployment and management of batch and real-time fraud and credit risk models for both onboarding and monitoring purposes.

  • Intuit: Business Operations Analyst [Mountain View, CA]

    Seeking a Business Operations Analyst, a creative problem solver with a passion for innovation, technology and teams to help us revolutionize the way small businesses run their business and deliver on our mission of “powering prosperity around the world.”

  • Intuit: Group Manager, Accountant Segment Data Science and Analytics [Mountain View, CA]

    Seeking creative problem solvers with a passion to deliver data-driven insights. This position leads a team of talented analysts and scientists to work alongside with business stakeholders to deliver customer insights and recommendations.

  • Intuit: Sr Data Scientist – Business Analytics [Mountain View, CA]

    Seeking a great data scientist for our Quickbooks Online (QBO) business. You need to be a top-notch problem solver and problem identifier as there are no points for an elegant solution to the wrong problem. It’s also a good idea to know the standard suite of analytical tools but more importantly you must know how to learn tools quickly.

  • Intuit: Sr Data Scientist, Business Analytics [Mountain View, CA]

    Seeking a hands-on Senior Data Scientist in the Data Analytics & Science team, someone who is a creative problem solver with a passion to deliver advanced data-driven solutions.

  • Intuit: Sr Data Scientist, Technical Analytics [Mountain View, CA]

    Come join Intuit as a Senior Technical Data Analyst, and partner closely with business and technical teams to understand their project objectives and provide data-driven solutions and recommendations.

  • Intuit: Sr Data Scientist, Business Analytics [Mountain View, CA]

    Seeking creative problem solvers with a passion to deliver data-driven insights. This position works alongside product manager, product developer, and analytics partners to deliver business results using data for insights and optimization.

  • Intuit: Group Manager Data Science and Analytics [Mountain View, CA]

    Seeking an analytics leader to build out our team and provide analytical leadership for the business, and to lead and develop a team while also rolling up their sleeves and get work done when that is what’s needed.

  • Intuit: Staff Data Scientist, Experimentation Analytics [Mountain View, CA]

    Intuit is seeking a Staff Data Scientist to join our team, to help build a new business within the company in one of the hottest fintech spaces of the moment.

  • Intuit: Experimentation Leader, Data Science and Analytics [Mountain View, CA]

    Seeking a Principal Experimentation Data Scientist, a creative problem solver with a passion for delivering data-driven insights, a deep knowledge of Testing and Experimentation to optimize digital experiences.

  • KDnuggets™ News 19:n18, May 8: What Data Science/Machine Learning software you used – KDnuggets Poll; The Third Wave Data Scientist

    Vote in KDnuggets 20th annual poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects? Also, what skills are needed for the 3rd wave Data Scientist?; The 3 biggest mistakes in learning Data Science; What makes XGBoost so successful; The ranking of best Masters in Analytics/Data Science in US/Canada; and more.

  • 2019 Best Masters in Data Science and Analytics – Europe Edition">Gold Blog2019 Best Masters in Data Science and Analytics – Europe Edition

    We provide an updated list of our comprehensive, unbiased survey of graduate programs in Data Science and Analytics from across Europe.

  • How To Work In Data Science, AI, Big Data">Silver BlogHow To Work In Data Science, AI, Big Data

    There are many facets to working in Data Science. Your role will depend greatly on the industry you pick and the area of Data Science you want to pursue. A Data Science career is very dynamic and requires a team effort to succeed.

  • Intuit: Staff Data Scientist [Mountain View, CA]

    Intuit is seeking a Staff Data Scientist in Mountain View, CA or Woodland Hills, CA. They are looking for team members that love new challenges, cracking tough problems and working cross-functionally.

  • Intuit: Principal Data Scientist [Mountain View, CA]

    Intuit is seeking a Principal Data Scientist in Mountain View, CA, to be responsible for developing analytical solutions for the detection of fraud and credit risk across the Intuit payments and payroll ecosystem.

  • Intuit: Senior Data Scientist [Mountain View, CA]

    hiring a Staff Data Scientist (tech lead) to focus on our Customer Success technology, reporting directly to the Director of Data Science in Customer Success. We are looking for some exceptional talent to help improve the bottom line of our Customer-facing technology.

  • Intuit: Sr Data Scientist, Business Analytics [Mountain View, CA]

    Intuit is seeking a Senior Analyst who will partner with our product management team to measure product feature usage, understand customer behavior, therefore improve customer user experience. The successful candidate will have a strong combination of both technical data skills and excellent communication skills.

  • Intuit: Sr Data Scientist, Business Analytics [Mountain View, CA]

    Seeking a Senior Data Scientist who will partner with our payroll product and marketing teams and drive profitable growth. As a senior member of the analytics team, you will have a strong track record of combining advanced analytics skills, knowledge of product analytics and exceptional analytical acumen.

  • Intuit: Sr Data Scientist – Business Analytics [Mountain View, CA]

    Seeking a Senior Data Scientist in the Data Analytics & Science team. Candidates should be creative problem solvers with a passion to deliver data-driven insights.

  • Intuit: Staff Data Scientist, Experimentation Analytics [Mountain View, CA].

    The ideal candidate thrives on ambiguity and enjoys the frequent pivoting that’s part of the exploration. This role ranges from a consultative business facilitator, leading discussions, to research experimentalist to technology innovator.

  • Intuit: Staff Business Data Analyst [Mountain View, CA]

    Intuit is seeking a Staff Business Data Analyst in Mountain View, CA. Come join Intuit as a Staff Business Data Analyst in our blazingly fast paced and high performing team.

  • Group Manager Data Science and Analytics [Mountain View, CA]

    Intuit is seeking a Senior Business Data Analyst in Mountain View, CA. Your area of focus will encompass Intuit’s entire product line including QuickBooks, Payroll, and Payments, and you will interact with the larger analytics community at Intuit using industry-leading analytics tools, techniques and best practices.

  • Intuit: Staff Data Scientist, Experimentation Analytics [Mountain View, CA]

    You will be joining a team that is building a new business within the company in one of the hottest fintech spaces of the moment. Your job will be to lead our experimentation efforts as we define product market fit.

  • Artificial Intelligence and Data Science Advances in 2018 and Trends for 2019

    We recap some of the major highlights in data science and AI throughout 2018, before looking at the some of the potential newest trends and technological advances for the year ahead.

  • Unlock and Extract Data from Your PDF Documents

    Automate and accurately extract data and information locked within PDF documents using PDF Alchemist, increasing productivity and data throughput while reducing costs.

  • Building an image search service from scratch

    By the end of this post, you should be able to build a quick semantic search model from scratch, no matter the size of your dataset.

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