KDnuggets

JANUARY 8, 2020

The standard job description for a Data Scientist has long highlighted skills in R, Python, SQL, and Machine Learning. With the field evolving, these core competencies are no longer enough to stay competitive in the job market.

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Data Science Data Scientist SQL Machine Learning 397

Analytics Vidhya

JANUARY 8, 2020

Introduction Google “NLP jobs” and a remarkable number of relevant searches show up. There are businesses spinning up around the world that cater exclusively. The post A Comprehensive Learning Path to Understand and Master NLP in 2020 appeared first on Analytics Vidhya.

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Analytics Analytics Natural Language Processing 291

Dataconomy

JANUARY 6, 2020

Which are the industries that are likely to be impacted the most by alternative web data this year ? Here is a look. For the past few years, financial institutions, such as hedge fund managers, have been on the forefront of harnessing the power of alternative data solutions to help. The post Here are the Sweet Spots for Alternative Web Data in 2020 appeared first on Dataconomy.

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Big Data Big Data 198

DataRobot

JANUARY 10, 2020

Does your organization apply appropriate human resources governance when hiring staff? Large enterprises tend to follow the same basic processes for hiring human staff. First, hiring managers write a job description, including the tasks the position requires and the skills and attributes of a suitable candidate. Job vacancies are posted, sometimes recruiters are also used, and people submit their resumes for consideration.

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Data Science AI AI 132

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Generative AI is upending the way product developers & end-users alike are interacting with data. Despite the potential of AI, many are left with questions about the future of product development: How will AI impact my business and contribute to its success? What can product managers and developers expect in the future with the widespread adoption of AI?

AI

KDnuggets

JANUARY 7, 2020

Follow this overview of Natural Language Generation covering its applications in theory and practice. The evolution of NLG architecture is also described from simple gap-filling to dynamic document creation along with a summary of the most popular NLG models.

Natural Language Processing 390

Natural Language Processing 390

Machine Learning (Theory)

JANUARY 4, 2020

This blog has been quiet for the last year. I have quite a bit to write about but found myself often out of time between work at Microsoft, ICML duties, and family life. Nevertheless, I expect to get back to more substantive discussions as I adjust to the new load. In the meantime, I’ve updated the site in various ways: SSL now works, and mail for people registering new accounts should work again.

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Eugene Yan

JANUARY 5, 2020

Building a baseline recsys based on data scraped off Amazon. Warning - Lots of charts! (Part 1 of 2).

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KDnuggets

JANUARY 9, 2020

This book is thought for beginners in Machine Learning, that are looking for a practical approach to learning by building projects and studying the different Machine Learning algorithms within a specific context.

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Machine Learning Machine Learning Algorithm 389

Victor Zhou

JANUARY 10, 2020

Quick - what sport is depicted in this image? Image from the CloudCV VQA Demo You probably immediately knew the answer: baseball. Easy, right? Now imagine you’re a computer. You’re given that same image and the text ” what sport is depicted in this image? ” and asked to produce the answer. Not so easy anymore, is it? This problem is known as Visual Question Answering (VQA) : answering open-ended questions about images.

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Python Deep Learning Deep Learning Natural Language Processing 52

Data Science 101

JANUARY 7, 2020

Are you an organization new to data science? Trying to implement the latest methods in Artificial Intelligence might not be the best way to start. It is better to start with a plan. Identify where you are and where you want to be. I call this a data strategy. I just released a free Data Strategy Email Course. Hopefully, this will help get your organization off to a smoother start with data science and artificial intelligence.

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Artificial Intelligence Artificial Intelligence Data Science 83

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Many organizations today are unlocking the power of their data by using graph databases to feed downstream analytics, enahance visualizations, and more. Yet, when different graph nodes represent the same entity, graphs get messy. Watch this essential video with Senzing CEO Jeff Jonas on how adding entity resolution to a graph database condenses network graphs to improve analytics and save your analysts time.

Database

DataCentric podcast

JANUARY 6, 2020

It's been an eventful year, but largely one setting chess pieces up for what's to come. Moor Insights & Strategy technology analysts Steve McDowell and Matt Kimball each give their top trends of the past year. Not to spoil it, but you can jump directly to the topics from the timeline below: 00:50 Silicon is Sexy Again! Whether we're talking about AMD, ARM, Ampere, NVIDIA, Intel, or one of the emerging players, we're talking about chips. 03:13 The Year of AI Enablement, both a

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AI AI 52

KDnuggets

JANUARY 7, 2020

An estimated 8,650% growth of the volume of Data to 175 zetabytes from 2010 to 2025 has created an enormous need for Data Engineers to build an organization's big data platform to be fast, efficient and scalable.

Data Engineering 389

Data Engineering Data Engineer Data Engineering Data Engineering 389

Shreyansh Singh

JANUARY 4, 2020

Get the challenge from here vm1.exe implements a simple 8-bit virtual machine (VM) to try and stop reverse engineers from retrieving the flag. The VM’s RAM contains the encrypted flag and some bytecode to decrypt it. Can you figure out how the VM works and write your own to decrypt the flag? A copy of the VM’s RAM has been provided in ram.bin (this data is identical to the ram content of the malware’s VM before execution and contains both the custom assembly code and encrypted flag).

Python 52

Python 52

Analytics Vidhya

JANUARY 5, 2020

Introducing the Learning Path to become a Data Scientist in 2020! Learning paths are easily one of the most popular and in-demand resources we. The post Your Ultimate Learning Path to Become a Data Scientist and Machine Learning Expert in 2020 appeared first on Analytics Vidhya.

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Speaker: Scott Sehlhorst

We know we want to create products which our customers find to be valuable. Whether we label it as customer-centric or product-led depends on how long we've been doing product management. There are three challenges we face when doing this. The obvious challenge is figuring out what our users need; the non-obvious challenges are in creating a shared understanding of those needs and in sensing if what we're doing is meeting those needs.

DataRobot

JANUARY 9, 2020

It’s a new year and a new decade, so we’re tapping into the minds of some of our leading industry experts and general managers (GMs) to find out what big trends they see emerging in the coming months. Learn more about AI demand forecasting for retailers, spatial data for sports analytics, and much more.

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AI AI Analytics Analytics 17

KDnuggets

JANUARY 10, 2020

A resume plays a key role in bagging that dream data science job. We break down the nuances of a job-winning data science resume so that you can go ahead and transform your own resume.

Data Science 338

Data Science 338

Data Science 101

JANUARY 6, 2020

As 2020 begins, there has been limited cloud data science announcements so I put together some predictions. Here are 3 things I believe will happen in 2020. 1. Cloud Collaboration. I think we are going to see more interoperability between the major cloud providers. For example, Azure Arc now allows you to run Azure products on a kubernetes container running anywhere (even in Amazon Web Services or Google Cloud) and AWS Outposts runs AWS on-premise.

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Data Science Cloud Data Azure Data Scientist 116

Analytics Vidhya

JANUARY 6, 2020

Introduction What a time to be working in the deep learning space! 2019 was chock full of deep learning-powered developments and breakthroughs – it. The post A Comprehensive Learning Path for Deep Learning in 2020 appeared first on Analytics Vidhya.

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Deep Learning Deep Learning Analytics Analytics 268

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

DataRobot

JANUARY 6, 2020

As a football fan who is also a data scientist, the one question I get asked the most by my friends is this:

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Data Science Data Scientist AI AI 16

KDnuggets

JANUARY 9, 2020

Data Scientists work with tons of data, and many times that data includes natural language text. This guide reviews 7 common techniques with code examples to introduce you the essentials of NLP, so you can begin performing analysis and building models from textual data.

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Data Scientist Data Preparation 390

Data Science 101

JANUARY 10, 2020

2020 is now in full swing and the announcements are starting to show up. There are some good ones this week. News. Google Releases a tool for Automated Exploratory Data Analysis Exploring data is one of the first activities a data scientist performs after getting access to the data. This command-line tool helps to determine the properties and quality of the data as well the predictive power.

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Data Science Power BI Cloud Data Exploratory Data Analysis 78

Analytics Vidhya

JANUARY 8, 2020

Introduction There are an overwhelming number of resources out there these days to learn computer vision concepts. How do you pick and choose from. The post Here’s your Learning Path to Master Computer Vision in 2020 appeared first on Analytics Vidhya.

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Analytics Analytics Deep Learning Deep Learning 237

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Start-ups & SMBs launching products quickly must bundle dashboards, reports, & self-service analytics into apps. Customers expect rapid value from your product (time-to-value), data security, and access to advanced capabilities. Traditional Business Intelligence (BI) tools can provide valuable data analysis capabilities, but they have a barrier to entry that can stop small and midsize businesses from capitalizing on them.

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Dataconomy

JANUARY 9, 2020

From wild speculation that flying cars will become the norm to robots that will be able to tend to our every need, there is lots of buzz about how AI, Machine Learning, and Deep Learning will change our lives. However, at present, it seems like a far-fetched future. As we. The post 2020: The Decade of Intelligent, Democratized Data appeared first on Dataconomy.

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Deep Learning Deep Learning Machine Learning Machine Learning 243

KDnuggets

JANUARY 6, 2020

Reducing images to numbers makes them amenable to computation. Let's take a look at the why and the how using Python and Numpy.

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Python 329

KDnuggets

JANUARY 8, 2020

Check out this collection of 10 Python snippets that can be taken as a reference for your daily work.

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Python 359

KDnuggets

JANUARY 10, 2020

Deepfakes have instilled panic in experts since they first emerged in 2017. Microsoft and Facebook have recently announced a contest to identify deepfakes more efficiently.

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AI AI 262

Speaker: Kevin Kai Wong, President of Emergent Energy Solutions

In today's industrial landscape, the pursuit of sustainable energy optimization and decarbonization has become paramount. Manufacturing corporations across the U.S. are facing the urgent need to align with decarbonization goals while enhancing efficiency and productivity. Unfortunately, the lack of comprehensive energy data poses a significant challenge for manufacturing managers striving to meet their targets.

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KDnuggets

JANUARY 4, 2020

Ethics in AI has received significant attention recently, and the new KDnuggets cartoon examines the problem of teaching ethics to artificially intelligent entities.

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Artificial Intelligence Artificial Intelligence AI AI 254

KDnuggets

JANUARY 9, 2020

Time series analysis will be the best tool for forecasting the trend or even future. The trend chart will provide adequate guidance for the investor. So let us understand this concept in great detail and use a machine learning technique to forecast stocks.

Machine Learning 260

Machine Learning Machine Learning 260

KDnuggets

JANUARY 10, 2020

Finding a deep learning model to perform well is an exciting feat. But, might there be other -- less complex -- models that perform just as well for your application? A simple complexity measure based on the statistical physics concept of Cascading Periodic Spectral Ergodicity (cPSE) can help us be computationally efficient by considering the least complex during model selection.

Deep Learning 252

Deep Learning Deep Learning 252

KDnuggets

JANUARY 6, 2020

This article is an overview of H2O, a scalable and fast open-source platform for machine learning. We will apply it to perform classification tasks.

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Machine Learning Machine Learning Python 346

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.