Data Engineering Podcast

NOVEMBER 11, 2019

Summary The practice of data management is one that requires technical acumen, but there are also many policy and regulatory issues that inform and influence the design of our systems. With the introduction of legal frameworks such as the EU GDPR and California’s CCPA it is necessary to consider how to implement data protectino and data privacy principles in the technical and policy controls that govern our data platforms.

Designing 100

Designing Data Pipeline Programming Language Data 100

KDnuggets

NOVEMBER 12, 2019

While Pandas is the library for data processing in Python, it isn't really built for speed. Learn more about the new library, Modin, developed to distribute Pandas' computation to speedup your data prep.

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Coding Python Data Process Process 122

Teradata

NOVEMBER 11, 2019

An integrated data foundation allows data science models to be more accurate, actionable and engage more customers. Find out how your model can positively impact your bottom line.

Data Science 75

Data Science Data 75

U-Next

NOVEMBER 9, 2019

Introduction to Workforce Analytics Today, the need to understand what attracts skillful individuals to join an organization, stay motivated, and deliver outstanding results has become more important than ever. However, this is not a task which can be shouldered by the HR team alone; they need the right tools to deliver optimal results. Over the years, organizations around the globe have spent billions of dollars on employee performance analysis, talent recruitment, leadership training, and deve

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Recruitment Data Analytics Media Education 55

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.

Data Science

Netflix Tech

NOVEMBER 12, 2019

Page Simulation for Better Offline Metrics at Netflix by David Gevorkyan , Mehmet Yilmaz , Ajinkya More , Gaurav Agrawal , Richard Wellington , Vivek Kaushal , Prasanna Padmanabhan , Justin Basilico At Netflix, we spend a lot of effort to make it easy for our members to find content they will love. To make this happen, we personalize many aspects of our service, including which movies and TV shows we present on each member’s homepage.

Algorithm 49

Algorithm Machine Learning Entertainment Systems 49

KDnuggets

NOVEMBER 11, 2019

A primary advantage of data analytics tools is that they can handle massive quantities of information at once. These solutions typically learn what's normal within a collection of information and how to spot anomalies.

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Data Analytics Data 107

KDnuggets

NOVEMBER 11, 2019

With so many Data Scientists showing up on LinkedIn, it's time to make sure your profile is top-notch because your talent is still highly sought after. Recruitment specialists want to find you fast, and this guide will help you create the best profile to feature your expertise.

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Data Science Recruitment Data IT 95

KDnuggets

NOVEMBER 13, 2019

While the revolution of deep learning now impacts our daily lives, these networks are expensive. Approaches in transfer learning promise to ease this burden by enabling the re-use of trained models -- and this hands-on tutorial will walk you through a transfer learning technique you can run on your laptop.

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Coding Deep Learning Machine Learning 92

KDnuggets

NOVEMBER 13, 2019

The following article is an introduction to classification and regression — which are known as supervised learning — and unsupervised learning — which in the context of machine learning applications often refers to clustering — and will include a walkthrough in the popular python library scikit-learn.

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Machine Learning Python 89

KDnuggets

NOVEMBER 14, 2019

This article provides covers how to automatically identify the topics within a corpus of textual data by using unsupervised topic modelling, and then apply a supervised classification algorithm to assign topic labels to each textual document by using the result of the previous step as target labels.

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Algorithm Data 89

Speaker: Anne Steiner and David Laribee

As a concept, Developer Experience (DX) has gained significant attention in the tech industry. It emphasizes engineers’ efficiency and satisfaction during the product development process. As product managers, we need to understand how a good DX can contribute not only to the well-being of our development teams but also to the broader objectives of product success and customer satisfaction.

Engineering

KDnuggets

NOVEMBER 14, 2019

Producing accessible data visualizations is a key data science skill. The following guidelines will help you create the best representations of your data using R and Python's Pandas library.

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Python Data Science Data Accessible 87

KDnuggets

NOVEMBER 15, 2019

With artificial intelligence and machine learning now a mainstay of our daily awareness, news organizations have been seen to overstate the reality behind progress in the field. Learn more about recent examples of media hyperbole and explore why this may be happening.

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Media Machine Learning 84

KDnuggets

NOVEMBER 15, 2019

In Python, lists store an ordered collection of items which can be of different types. This post is an overview of lists and their manipulation.

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Python Programming 88

KDnuggets

NOVEMBER 12, 2019

In this guide, we’ll look at papers aimed at solving the problems of depth estimation using deep learning.

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Deep Learning 97

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.

Building

KDnuggets

NOVEMBER 14, 2019

Let’s take a look at traditional testing methodologies and how we can apply these to our data/ML pipelines.

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Machine Learning Data Python 88

KDnuggets

NOVEMBER 13, 2019

Check out this author's collection of tips and tricks that I learned over the years to get better at Machine Learning.

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Machine Learning 84

KDnuggets

NOVEMBER 13, 2019

Whether you’re a beginner or an expert, there’s always new ways you can improve your Python coding. Save 40% off this trio of Manning Python books today! Just enter the code nlpropython40 at checkout when you buy from manning.com.

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Python Computer Science Coding 59

KDnuggets

NOVEMBER 15, 2019

Spoiler: you don’t need a VM running 24/7 to handle 16 requests a day.

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Machine Learning Project 91

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Project

KDnuggets

NOVEMBER 14, 2019

During this free Metis Corporate Training webinar, Dec 5 @ 12pm ET, Kerstin Frailey, Senior Data Scientist and Head of Executive Corporate Training at Metis, will walk through what you need to ask before, during, and after the lifetime of a data science project to accurately assess its impact on the business.

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Data Science Project IT Data 56

KDnuggets

NOVEMBER 13, 2019

On KDnuggets this week: Orchestrating Dynamic Reports in Python and R with Rmd Files; How to Create a Vocabulary for NLP Tasks in Python; What is Data Science?; The Complete Data Science LinkedIn Profile Guide; Set Operations Applied to Pandas DataFrames; and much, much more.

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Data Science Python Data Deep Learning 48

KDnuggets

NOVEMBER 11, 2019

Also: Understanding Boxplots; Probability Learning: Maximum Likelihood; Designing Your Neural Networks; Facebook Has Been Quietly Open Sourcing Some Amazing Deep Learning Capabilities for PyTorch; 5 Statistical Traps Data Scientists Should Avoid.

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Deep Learning Designing Data 47

KDnuggets

NOVEMBER 11, 2019

In this article, I will show you how to quickly extract Google Maps coordinates with a simple and easy method.

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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

Building

KDnuggets

NOVEMBER 13, 2019

Also: It's time to make your Data Science LinkedIn profile ready for recruiters.; Python Libraries for Interpretable Machine Learning - KDnuggets; Process your data with Pandas up to 4x faster with this new Python library.; How to Extract Google Maps Coordinates.

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Recruitment Python Machine Learning Data Science 46

KDnuggets

NOVEMBER 12, 2019

This live webinar, Nov 14 @ 12pm EST, on MLOps for production-level machine learning, will detail MLOps, a compound of “machine learning” and “operations”, a practice for collaboration and communication between data scientists and operations professionals to help manage the production machine learning lifecycle. Register now.

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Machine Learning Management Data 45

KDnuggets

NOVEMBER 12, 2019

In this post, we seek to understand why topic modeling is important and how it helps us as data scientists.

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IT Data 61

KDnuggets

NOVEMBER 11, 2019

ReAgent is a new framework that streamlines the implementation of reasoning systems.

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Systems 51

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.

Certification

Netflix Tech

NOVEMBER 12, 2019

Page Simulation for Better Offline Metrics at Netflix by David Gevorkyan , Mehmet Yilmaz , Ajinkya More , Gaurav Agrawal , Richard Wellington , Vivek Kaushal , Prasanna Padmanabhan , Justin Basilico At Netflix, we spend a lot of effort to make it easy for our members to find content they will love. To make this happen, we personalize many aspects of our service, including which movies and TV shows we present on each member’s homepage.

Algorithm 40

Algorithm Machine Learning Entertainment Systems 40

Netflix Tech

NOVEMBER 12, 2019

Page Simulation for Better Offline Metrics at Netflix by David Gevorkyan , Mehmet Yilmaz , Ajinkya More , Gaurav Agrawal , Richard Wellington , Vivek Kaushal , Prasanna Padmanabhan , Justin Basilico At Netflix, we spend a lot of effort to make it easy for our members to find content they will love. To make this happen, we personalize many aspects of our service, including which movies and TV shows we present on each member’s homepage.

Algorithm 40

Algorithm Machine Learning Entertainment Systems 40