KDnuggets

SEPTEMBER 18, 2019

Read about how one data scientist copes with his boring days of deploying machine learning.

Data Science 88

Data Science Machine Learning 88

Analytics Vidhya

SEPTEMBER 18, 2019

Overview Writing optimized Python code is a crucial piece in your data science skillset Here are four methods to optimize your Python code (with. The post 4 Unique Methods to Optimize your Python Code for Data Science appeared first on Analytics Vidhya.

Optimization 307

Optimization Data Science Analytics 307

Corinium

SEPTEMBER 18, 2019

Instant noodles and the on-demand life. It’s no secret that, despite the huge movement toward sustainable living, we just can’t live without some things being instant. One such crutch for students worldwide and most of Asia’s young adult population (also the entire American prison system) are the ubiquitous instant noodles. 100 billion servings are eaten every year.

Business Intelligence 150

Business Intelligence IT 150

KDnuggets

SEPTEMBER 18, 2019

Algorithms are at the core of data science and sampling is a critical technical that can make or break a project. Learn more about the most common sampling techniques used, so you can select the best approach while working with your data.

Data Science 106

Data Science 106

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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.

Visualization

Smart Data Collective

SEPTEMBER 18, 2019

I recently read a great post from The Verge on the impact of AI on the video gaming industry. Author Nick Statt made a great point about the evolution of AI in the industry. Pratt pointed out that AI has been a factor in the video game industry since the very beginning. Some of the AI tools that we see today resemble those in the 1980 game Rogue. Of course, AI has improved dramatically over the last 40 years.

Machine Learning 77

Machine Learning Measurement Technology IT 77

KDnuggets

SEPTEMBER 18, 2019

Also: Cartoon: Unsupervised #MachineLearning?; Cartoon: Unsupervised Machine Learning ? How to Become More Marketable as a Data Scientist; Ensemble Methods for Machine Learning: AdaBoost.

Machine Learning 93

Machine Learning Marketing 93

KDnuggets

SEPTEMBER 18, 2019

This article covers the implementation of a data scraping and natural language processing project which had two parts: scrape as many posts from Reddit’s API as allowed &then use classification models to predict the origin of the posts.

Modeling 75

Modeling 75

Perceptual Edge

SEPTEMBER 18, 2019

In our efforts to make knowledge accessible to everyone, if we’re not careful, good intentions can cause us to blunder into useless attempts that benefit no one. I was painfully reminded of this recently when I received a request from a university for an electronic version of my book Show Me the Numbers to accommodate the needs of a student who is blind.

Visualization 72

Visualization Software Technology IT 72

KDnuggets

SEPTEMBER 18, 2019

Support for Python 2 will expire on Jan. 1, 2020, after which the Python core language and many third-party packages will no longer be supported or maintained. Take this survey to help determine and share your level of preparation.

71

71

CDW Research Hub

SEPTEMBER 18, 2019

Sirius recognized as top partner of the year. . San Antonio, TX—18 September, 2019 — Sirius Computer Solutions, Inc. (Sirius), a leading national IT solutions integrator, announced it is the recipient of the Pure Storage U.S. Partner of the Year for 2019, which was awarded during Pure’s Global Partner Forum at Pure//Accelerate 2019. The award represents the commitment of Sirius to deliver on hybrid-cloud solutions from Pure Storage and the ability to provide a modern data experience to its clie

Sales 40

Sales Optimization Technology Management 40

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.

Management

KDnuggets

SEPTEMBER 18, 2019

Check the results of KDnuggets' latest poll to find out which data science skills are core and which are hot/emerging ones; why is there no free lunch in data science?; training Scikit-learn 100x faster; poking fun at unsupervised machine learning; exploring the case for ensemble learning. All this and much more this week on KDnuggets.

Data Science 48

Data Science Machine Learning 48

DataRobot

SEPTEMBER 18, 2019

The truth is that the work of data scientists cannot generate value if the models never make it to production. For data scientists writing custom models in languages like Python and R, the number of challenges for getting models into production can be overwhelming. Issues range from how to deploy model code on production systems, how to monitor performance, and how to deploy updates to models over time.

Modeling 16

Modeling IT 16

Domino Data Lab

SEPTEMBER 18, 2019

This post covers data exploration using machine learning and interactive plotting. If interested in running the examples, there is a complementary Domino project available. Introduction. Models are at the heart of data science. Data exploration is vital to model development and is particularly important at the start of any data science project. Visualization tools help make the shape of the data more obvious, surface patterns that can easily hide in hundreds of rows of data, and can even assist

Visualization 99

Visualization Interactive Machine Learning Data Science 99

CIO Business Intelligence

SEPTEMBER 18, 2019

Self-service analytics tools have long empowered users to produce data visualizations without the need for IT intervention. Recent advances, such as data prep automation, have further lowered the barrier of entry, but this push to democratize analytics surely has its limits. After all, users still have to interpret the data visualizations they produce.

Visualization 99

Visualization Statistics Interactive Data Enablement 99

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.

Experimentation