2021

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6 Predictive Models Every Beginner Data Scientist Should Master

KDnuggets

Data Science models come with different flavors and techniques — luckily, most advanced models are based on a couple of fundamentals. Which models should you learn when you want to begin a career as Data Scientist? This post brings you 6 models that are widely used in the industry, either in standalone form or as a building block for other advanced techniques.

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What’s New in Apache Kafka 3.0.0

Confluent

I’m pleased to announce the release of Apache Kafka 3.0 on behalf of the Apache Kafka® community. Apache Kafka 3.0 is a major release in more ways than one. Apache […].

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Building Uber’s Fulfillment Platform for Planet-Scale using Google Cloud Spanner

Uber Engineering

Introduction. The Fulfillment Platform is a foundational Uber domain that enables the rapid scaling of new verticals. The platform handles billions of database transactions each day, ranging from user actions (e.g., a driver starting a trip) and system actions … The post Building Uber’s Fulfillment Platform for Planet-Scale using Google Cloud Spanner appeared first on Uber Engineering Blog.

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Turning the page

Cloudera

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

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Driving Business Impact for PMs

Speaker: Jon Harmer, Product Manager for Google Cloud

Move from feature factory to customer outcomes and drive impact in your business! This session will provide you with a comprehensive set of tools to help you develop impactful products by shifting from output-based thinking to outcome-based thinking. You will deepen your understanding of your customers and their needs as well as identifying and de-risking the different kinds of hypotheses built into your roadmap.

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Natural Language Processing: A Guide to NLP Use Cases, Approaches, and Tools

AltexSoft

Humans have been trying to make machines chat for decades. Alan Turing considered computers’ ability to generate natural speech a proof of their ability to think. Today, we converse with virtual companions all the time. But despite years of research and innovation, their unnatural responses remind us that no, we’re not yet at the HAL 9000-level of speech sophistication.

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Improving Population Health Through Citizen 360

Teradata

By leveraging data to create a 360 degree view of its citizenry, government agencies can create more optimal experiences & improve outcomes such as closing the tax gap or improving quality of care.

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

DataKitchen

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

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Building a solid data team

KDnuggets

How do you put together a solid data science team when it comes to developing data-driven products? A variety of roles are available to consider, so which ones do you need and which are most crucial?

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Why Machine Learning Engineers are Replacing Data Scientists

KDnuggets

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

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3 Differences Between Coding in Data Science and Machine Learning

KDnuggets

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

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

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

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

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Design Patterns for Machine Learning Pipelines

KDnuggets

ML pipeline design has undergone several evolutions in the past decade with advances in memory and processor performance, storage systems, and the increasing scale of data sets. We describe how these design patterns changed, what processes they went through, and their future direction.

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Top Stories, Nov 15-21: 19 Data Science Project Ideas for Beginners

KDnuggets

Also: How I Redesigned over 100 ETL into ELT Data Pipelines; Where NLP is heading; Don’t Waste Time Building Your Data Science Network; Data Scientists: How to Sell Your Project and Yourself.

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5 Practical Data Science Projects That Will Help You Solve Real Business Problems for 2022

KDnuggets

This curated list of data science projects offers real-life problems that will help you master skills to demonstration that you are technically sound and know how to conduct data science projects that add business value.

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Where NLP is heading

KDnuggets

Natural language processing research and applications are moving forward rapidly. Several trends have emerged on this progress, and point to a future of more exciting possibilities and interesting opportunities in the field.

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Reimagined: Building Products with Generative AI

“Reimagined: Building Products with Generative AI” is an extensive guide for integrating generative AI into product strategy and careers featuring over 150 real-world examples, 30 case studies, and 20+ frameworks, and endorsed by over 20 leading AI and product executives, inventors, entrepreneurs, and researchers.

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Most Common SQL Mistakes on Data Science Interviews

KDnuggets

Sure, we all make mistakes -- which can be a bit more painful when we are trying to get hired -- so check out these typical errors applicants make while answering SQL questions during data science interviews.

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Data Scientist Career Path from Novice to First Job

KDnuggets

If you are beginning your data science journey, then you must be prepared to plan it out as a step-by-step process that will guide you from being a total newbie to getting your first job as a data scientist. These tips and educational resources should be useful for you and add confidence as you take that first big step.

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ORDAINED: The Python Project Template

KDnuggets

Recently I decided to take the time to better understand the Python packaging ecosystem and create a project boilerplate template as an improvement over copying a directory tree and doing find and replace.

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10 Key AI & Data Analytics Trends for 2022 and Beyond

KDnuggets

What AI and data analytics trends are taking the industry by storm this year? This comprehensive review highlights upcoming directions in AI to carefully watch and consider implementing in your personal work or organization.

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Entity Resolution Checklist: What to Consider When Evaluating Options

Are you trying to decide which entity resolution capabilities you need? It can be confusing to determine which features are most important for your project. And sometimes key features are overlooked. Get the Entity Resolution Evaluation Checklist to make sure you’ve thought of everything to make your project a success! The list was created by Senzing’s team of leading entity resolution experts, based on their real-world experience.

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Data Science & Analytics Industry Main Developments in 2021 and Key Trends for 2022

KDnuggets

We have solicited insights from experts at industry-leading companies, asking: "What were the main AI, Data Science, Machine Learning Developments in 2021 and what key trends do you expect in 2022?" Read their opinions here.

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Top 4 Data Integration Tools for Modern Enterprises

KDnuggets

Maintaining a centralized data repository can simplify your business intelligence initiatives. Here are four data integration tools that can make data more valuable for modern enterprises.

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Alternative Feature Selection Methods in Machine Learning

KDnuggets

Feature selection methodologies go beyond filter, wrapper and embedded methods. In this article, I describe 3 alternative algorithms to select predictive features based on a feature importance score.

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How to Get Certified as a Data Scientist

KDnuggets

If you are early in your journey to becoming a Data Scientist, an interesting option is to earn certification by DataCamp, and this guide offers tips that will help beginners complete the challenges.

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The Big Payoff of Application Analytics

Outdated or absent analytics won’t cut it in today’s data-driven applications – not for your end users, your development team, or your business. That’s what drove the five companies in this e-book to change their approach to analytics. Download this e-book to learn about the unique problems each company faced and how they achieved huge returns beyond expectation by embedding analytics into applications.

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Data Labeling for Machine Learning: Market Overview, Approaches, and Tools

KDnuggets

So much of data science and machine learning is founded on having clean and well-understood data sources that it is unsurprising that the data labeling market is growing faster than ever. Here, we highlight many of the top players in this industry and the techniques they use to help you consider which might make a good partner for your needs.

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10 AI Project Ideas in Computer Vision

KDnuggets

The field of computer vision has seen the development of very powerful applications leveraging machine learning. These projects will introduce you to these techniques and guide you to more advanced practice to gain a deeper appreciation for the sophistication now available.

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AI Infinite Training & Maintaining Loop

KDnuggets

Productizing AI is an infrastructure orchestration problem. In planning your solution design, you should use continuous monitoring, retraining, and feedback to ensure stability and sustainability.

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Machine Learning Safety: Unsolved Problems

KDnuggets

There remain critical challenges in machine learning that, if left resolved, could lead to unintended consequences and unsafe use of AI in the future. As an important and active area of research, roadmaps are being developed to help guide continued ML research and use toward meaningful and robust applications.

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Monetizing Analytics Features

Think your customers will pay more for data visualizations in your application? Five years ago, they may have. But today, dashboards and visualizations have become table stakes. Turning analytics into a source of revenue means integrating advanced features in unique, hard-to-steal ways. Download this white paper to discover which features will differentiate your application and maximize the ROI of your analytics.

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How to Speed Up XGBoost Model Training

KDnuggets

XGBoost is an open-source implementation of gradient boosting designed for speed and performance. However, even XGBoost training can sometimes be slow. This article will review the advantages and disadvantages of each approach as well as go over how to get started.

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Should You Become a Freelance Artificial Intelligence Engineer?

KDnuggets

Take the first step towards your machine learning engineering career and explore the UC San Diego Extension Machine Learning Engineering Bootcamp today. Those with prior software engineering or data science experience are encouraged to apply.

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How I 14Xed my salary in 14 years as a data analytics/science professional

KDnuggets

Learn how one data scientist increased their full-time job salary 14 times in 14 years of a career, with highlights on experiencing an IPO, RSUs, start-ups and working at FAANG companies.

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A Full End-to-End Deployment of a Machine Learning Algorithm into a Live Production Environment

KDnuggets

How to use scikit-learn, pickle, Flask, Microsoft Azure and ipywidgets to fully deploy a Python machine learning algorithm into a live, production environment.

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How To Package & Price Embedded Analytics

Just by embedding analytics, app owners can charge 24% more for their product. How much value could you add? This framework from Software Pricing Partners explains how application enhancements can extend your product offerings. You’ll learn: How to take a disciplined approach to pricing The three elements of the Packaging Decision Framework Ways to structure your new embedded analytics offering Download the White Paper to learn about How To Package & Price Embedded Analytics.