Search results for saas

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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!

  • 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.

  • 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.

  • 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 Data Engineering Study Roadmap

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

  • 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.

  • 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!

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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’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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • The Role of the Data Engineer is Changing

    The role of the data engineer in a startup data team is changing rapidly. Are you thinking about it the right way?

  • KDnuggets Site Map

    About KDnuggets Awards and Honors for KDnuggets Companies, offering Bioinformatics products and solutions Data Science and Analytics products Consulting and Training Data Warehousing and OLAP Read more »

  • Cummins: Data Engineering Apps and Solutions Architect [Columbus, IN]

    Cummins is seeking a Data Engineering Apps and Solutions Architect in Columbus, IN, to focus on the specific Architecture, tools and environment optimization for the Data Engineering team within the Advanced Analytics organization and applied to Cummins standards worldwide.

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

    Intuit is seeking a Staff Data Scientist - Business Analytics in Mountain View, CA, to be responsible for identifying opportunities to use customer driven analytics to drive growth and improved decision making in Intuit’s Small Business Division.

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

    Intuit is seeking a Staff Data Scientist in Mountain View, CA, to perform hands-on data analysis and modeling with huge data sets, and apply data mining, NLP, and machine learning (both supervised and unsupervised) to improve relevance and personalization algorithms.

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

    Intuit is seeking a Staff Experimentation Data Scientist in Mountain View, CA. In this role you will drive experimentation process improvement and automation to dramatically transform the way in which we allocate customers to experiments and manage delivery of experiments that drive decision making.

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

    Intuit is seeking a Staff Data Scientist in Woodland Hills, CA and Mountain View, CA. This role will oversee the design, development, deployment and management of batch and real-time fraud and credit risk models for both onboarding and monitoring purposes.

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

    Intuit is seeking a Staff Data Scientist in Mountain View, CA. This role teams up with our credit strategy and data engineering teams closely to develop innovative credit models for our QuickBooks Financing products.

  • The Most in Demand Skills for Data Scientists">Platinum BlogThe Most in Demand Skills for Data Scientists

    Data scientists are expected to know a lot — machine learning, computer science, statistics, mathematics, data visualization, communication, and deep learning. How should data scientists who want to be in demand by employers spend their learning budget?

  • 5 “Clean Code” Tips That Will Dramatically Improve Your Productivity

    TL;DR: If it isn’t tested, it’s broken; Choose meaningful names; Classes and functions should be small and obey the Single Responsibility Principle (SRP); Catch and handle exceptions, even if you don’t think you need to; Logs, logs, logs

  • Jimdo: Team Lead Product Data Analytics [Hamburg, Germany]

    Jimdo GmbH is seeking a Team Lead Product Data Analytics in Hamburg, Germany, to be part of Jimdo’s cross-functional Business Intelligence Team at the Jimdo Headquarter in the beautiful city of Hamburg.

  • Jimdo: Sr Data Scientist [Hamburg, Germany]

    Jimdo GmbH is seeking a Sr Data Scientist in Hamburg, Germany. Your mission is to explore large data sets and combine data analytics with programming skills in order to help identify and solve business problems.

  • Better Analytics for the Product Experience – Aug 21 webinar

    Learn a process for discovering the data and analytics needs of your users using user stories, use cases and mapping to data sources; Strategies for balancing priorities and managing expectations, and more.

  • How to Make AI More Accessible

    I recently was a guest speaker at the Stanford AI Salon on the topic of accessiblity in AI, which included a free-ranging discussion among assembled members of the Stanford AI Lab. There were a number of interesting questions and topics, so I thought I would share a few of my answers here.

  • Data Exchange and Marketplace, a New Business Model in Making

    This article covers how an ever-increasing amount of data will trigger the evolution of a new ecosystem that will spur entrepreneurial activity, offering an opportunity to start a wide range of new businesses.

  • How To Choose The Right Chart Type For Your Data

    The power of charts to assist in accurate interpretation is massive and that's why it is vital to select the correct type when you are trying to visualize data.

  • A Comparative Analysis of Top 6 BI and Data Visualization Tools in 2018">Silver BlogA Comparative Analysis of Top 6 BI and Data Visualization Tools in 2018

    In this article, we will compare the most commonly used platforms and analyze their main features to help you choose one or several platforms that will provide indispensable aid for your work communication.

  • Calculating Customer Lifetime Value: SQL Example

    In order to understand how to estimate LTV, it is useful to first think about evaluating a customer’s lifetime value at the end of their relationship with us.

  • Kogentix Automated Machine Learning Platform

    Kogentix Automated Machine Learning Platform is the only solution we have seen that runs natively on Spark and includes all of the elements required to build and run a machine learning application.

  • Another Day in the Life of a Data Scientist

    Are you interested in what a data scientist does on a typical day of work? Each data science role may be different, but these five individuals provide insight to help those interested in figuring out what a day in the life of a data scientist actually looks like.

  • Jimdo: Business Intelligence Analyst

    Seeking a BI-Analyst, to bring domain knowledge and experience to a diverse team and contribute to the understanding and usage of data throughout the company and to enable data driven decision making.

  • How I started with learning AI in the last 2 months">Silver BlogHow I started with learning AI in the last 2 months

    The relevance of a full stack developer will not be enough in the changing scenario of things. In the next two years, full stack will not be full stack without AI skills.

  • Machine Learning Reveals 9 Elements of Deal-Closing Sales

    The data science team at analyzed over 67,000 sales calls/demos to understand the patterns that close deals. Here is what we found.

  • Big Data Architecture: A Complete and Detailed Overview

    Data scientists may not be as educated or experienced in computer science, programming concepts, devops, site reliability engineering, non-functional requirements, software solution infrastructure, or general software architecture as compared to well-trained or experienced software architects and engineers.

  • Digital Transformation through Data Democratization

    Digital innovators will succeed because enterprise data doesn’t belong to silos and data has immense value, but only if available as a “whole”, to allow full picture of the enterprise rather than short term trends or baseline BI reports.

  • The BI & Data Analysis Conundrum: 8 Reasons Why Many Big Data Analytics Solutions Fail to Deliver Value">Silver Blog, July 2017The BI & Data Analysis Conundrum: 8 Reasons Why Many Big Data Analytics Solutions Fail to Deliver Value

    Why many BI & Analytics projects/solutions fail to deliver the business value? Let’s find out the answers to such questions.

  • 75 Big Data Terms to Know to Make your Dad Proud

    Here is a good list of 75 Big Data terms you can use to impress your father, even if you already bought him a gift.

  • The Internet of Things in the Cloud

    Cloud computing is the next evolutionary step in Internet-based computing, which provides the means for delivering ICT resources as a service. Internet-of-Things can benefit from the scalability, performance and pay-as-you-go nature of cloud computing infrastructures.

  • Level-up your analytics with text mining, Apr 27 Webinar

    MeaningCloud, leader in SaaS semantic analytics, has new RapidMiner extension that offers very powerful and flexible text analytics and the ability to extract the meaning of any unstructured text. Learn more in April 27 webinar.

  • From Big Data Platforms to Platform-less Machine Learning

    The rise in serverless architectures along with marketplaces from cloud providers creates a significant momentum to democratize big data analytics. Machine learning or AI services are much more valuable, tangible and easier to understand for businesses than clumsy big data platforms.

  • 50 Companies Leading The AI Revolution, Detailed">Silver Blog, 201750 Companies Leading The AI Revolution, Detailed

    We detail 50 companies leading the Artificial Intelligence revolution in AD Sales, CRM, Autotech, Business Intelligence and analytics, Commerce, Conversational AI/Bots, Core AI, Cyber-Security, Fintech, Healthcare, IoT, Vision, and other areas.

  • KDnuggets™ News 17:n06, Feb 15: So What is Big Data? 52 Useful Machine Learning APIs; Data Science finds Perfect Valentines Dates

    Also Making Python Speak SQL with pandasql; 52 Useful Machine Learning & Prediction APIs, updated; New Poll: Do you support Trump Immigration Ban?

  • Domino Data Science Popup, San Francisco, Feb 22 – KDnuggets Offer

    Learn about the latest trends in data science applications in technology from the top experts in the industry. Register by Feb 8 and save with code KDNuggetsVIP.

  • Data Science of Sales Calls: 3 Actionable Findings

    How does AI help sales and marketing teams in the organisation? Let’s understand Dos and don’ts of sales calls with the help of analysis of over 70,000+ B2B SaaS sales calls.

  • What is emotion analytics and why is it important?

    In today’s Internet world, humans express their Emotions, Sentiments and Feelings via text/comments, emojis, likes and dislikes. Understanding the true meanings behind the combinations of these electronic symbols is very crucial and this is what this article explains.

  • Choosing Tools for Data ETLs

    Which tool should I use for my data pipelines? Get some advice from a data scientist recently having gone through this pipeline tool selection process.

  • Civis Analytics: Data Scientist, Statistics

    Seeking a Data Scientist to work closely and collaboratively with analysts and engineers to develop and operationalize the techniques that quantify and solve big, meaningful problems.

  • Civis Analytics: Lead Data Engineer

    Seeking a Lead Data Engineer, reporting to Director of Engineering provides technical, strategic and operational leadership to the organization, able build great products, with a particular focus on the data pipelines that support them.

  • Cloud Computing Key Terms, Explained

    A concise overview of 20 core cloud computing ecosystem concepts. The focus here is on the terminology, not The Big Picture.

  • Where are the Opportunities for Machine Learning Startups?

    Machine learning has permeated data-driven businesses, which means almost all businesses. Here are a few areas where it’s possible that big corporations haven’t already eaten everybody’s lunch.

  • CRN Top Business Analytics Vendors 2016

    The CRN editorial team has released its annual Big Data 100 report for 2016. Check out which companies made the list of Business Analytics Vendors.

  • Meet the 11 Big Data & Data Science Leaders on LinkedIn

    In this post, we present a list of popular data science leaders on LinkedIn. Follow these leaders who will keep you in touch with the latest Data Science happenings!

  • Positioning a Machine Learning Company

    The classic guide for entrepreneurs preparing a pitch is Sequoia’s Business Plan Template. This post aims to be a mere addendum to that in the age of machine learning.

  • Top Data Science Courses on Udemy

    An overview of the very best that Udemy has to offer in data science education. Includes courses covering machine learning, Python, Hadoop, visualization, and more.

  • Does Your Company Need a Data Scientist?

    Your company needs a data scientist... doesn't it? It very well may not, but you need to know either way. Read on to determine whether or not your company could benefit from the skills of an on-board data scientist.

  • Data Science Tools – Are Proprietary Vendors Still Relevant?

    We examine and quantify the dramatic impact of open source tools like R and Python on SAS, IBM, Microsoft, and other proprietary Data Science vendors. We also investigate how open source tools were faring against each other, which are growing, which are falling, and look R versus Python debate.

  • The Next Big Inflection in Big Data: Automated Insights

    To keep up with big data and improve our use of information, we need insightful applications that will quickly and inexpensively extract correlations while associating insights with actions.

  • 10 Business Intelligence Trends for 2016

    BI analysts, industry players predict the rise of self-service, Big Data analytics, real-time data in the coming year.

  • How ‘Insights-as-a-service’ is growing based on big data

    Insights-as-service should deliver not only actionable insights, but also a concrete plan to use them. We review different types of insights as a service, how they are used with big data, deployment challenges, and future trends.

  • Create or machine-learn fuzzy logic rules for use with an on-line inference engine

    New DocAndys SaaS service supports user-created embeddable Fuzzy Logic Expert Systems. Use rule language Darl to hand-create or machine-learn rule sets from data and use them via REST interfaces.

  • Northwestern MOOC Specialization: “Social Marketing – How to Profit in a Digital World;” Lexalytics CMO Seth Redmore Featured Faculty Member

    Six-part series offered through Coursera will teach entrepreneurs, executives, and marketing professionals how to manage, measure, and monetize social media marketing programs.

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