Domino Data Lab hosted its first ever Data Science Leaders Summit at the lovely Yerba Buena Center for the Arts in San Francisco on May 30-31, 2018. Cathy O'Neil, Nate Silver, Cassie Kozyrkov and Eric Colson were some of the speakers at this event.
This post contains some of the key findings from the SNS Telecom & IT's latest report, which indicates that Big Data investments in the healthcare and pharmaceutical industry are expected to reach nearly $4.7 Billion by the end of 2018.
I saw an article recently that referred to the normal curve as the data scientist's best friend. We examine myths around the normal curve, including - is most data normally distributed?
In today’s post, I want to look specifically at Google’s AutoML, a product which has received a lot of media attention, and address "What is Google's AutoML?" and more.
Also: How to Build a Data Science Portfolio; DevOps for Data Scientists: Taming the Unicorn; Why Germany did not defeat Brazil in the final, or Data Science lessons from the World Cup; Cookiecutter Data Science: How to Organize Your Data Science Project
TDWI Orlando, Nov 11-16, provides you with the skills and best practices you need to advance your data management and analytics initiatives now. The agenda is now live! Save big with code KD20.
Cutting-edge science and new business fundamentals intersect and merge at Strata Data Conference. Win KDnuggets Pass - submit your entry by Aug 9, 2018.
When it comes to big data, possession is not enough. Comprehensive intelligence is the key. But traditional data analytics paradigms simply cannot deliver on the promise of data-driven insights. Here’s why.
Did you know that you can execute R and Python code remotely in SQL Server from Jupyter Notebooks or any IDE? Machine Learning Services in SQL Server eliminates the need to move data around.
This post is not really about how to lie with Data Science. Instead, it’s about how we may be fooled by not giving enough attention to details in different parts of the pipeline.
Join the leading minds in AI, explore latest developments, separate hype from what is really game-changing, and learn how to apply AI in your organization. Save with code code PCKDNG.
At Bay Path University, we'll provide you with a framework for working together regardless of your background and experience. That is why we created two tracks to complete the MS in Applied Data Science degree, which is right for you?
Predictive analytics are useful for doing all those things and more, and could increase the overall competitiveness of individual companies or entire sectors.
Learn how Mark43 researched, prototyped, and iterated to deliver analytics and business intelligence tools to police departments, emergency call centers, and other public safety agencies.
The agenda for Predictive Analytics World for Business Berlin, 13-14 Nov, has just been released! Get inspired by heavyweight speakers & meet the people who make a difference!
This post will include links to where various data science professionals (data science managers, data scientists, social media icons, or some combination thereof) and others talk about what to have in a portfolio and how to get noticed.
Attend ODSC Europe 2018, London, 19-22 Sept, and get hands-on training from leading data science experts - use code ODSC45 until Fri, July 27, 2018 to save 45%. Also, use code ODSC50 to save 50% on your pass to ODSC West 2018, Oct 31 - Nov 3, San Francisco.
With Drexel University’s online MS in Business Analytics program, you’ll be able to effectively analyze this overlooked data to give your company and yourself a competitive edge.
We review World Cup predictions (all failed), examine what makes such events difficult to predict, and suggest 3 golden rules to determine when you can trust the predictions.
This tutorial will implement the genetic algorithm optimization technique in Python based on a simple example in which we are trying to maximize the output of an equation.
Also: Efficient Graph-based Word Sense Induction; 5 Quick and Easy Data Visualizations in Python with Code; Explaining the 68-95-99.7 rule for a Normal Distribution; 5 Data Science Projects That Will Get You Hired in 2018
Twenty five years covering Data Mining, Knowledge Discovery in Data, KDD, Predictive Analytics, Big Data, Data Science, Machine Learning, and AI - my reflections on 25 years of publishing and editing KDnuggets.
Anaconda Enterprise is the only product on the market that empowers your data science team to go from laptop to cluster to production with full reproducibility and governance.
Predictive Analytics World London, Oct 17-18 - the leading vendor-neutral machine learning conference - is close to a finalized agenda, packed with cutting edge insights.
At startups, you often have the chance to create products from scratch. In this article, the author will share how to quickly build valuable data science products, using his first project at Instacart as an example.
I talk to Kirill Eremenko about my journey to data science, how KDnuggets started, why you should start honing your machine learning engineering skills at this very moment, what's the future of data science, and more.
The Machine Learning with TensorFlow on Google Cloud Platform Specialization on Coursera will help you jumpstart your career, includes hands-on labs, and takes you from a strategic overview to practical skills in building real-world, accurate ML models.
Every move we make, every breath we take, and every heartbeat is an effect that is caused. Even apparent randomness may just be something we cannot explain.
Learn product analytics best practices and the "meta" perspective from a practitioner who is building products that anybody, including product managers, can use to access, analyze, and act on data to make important decisions.
This blog is however not addressing the absolute beginner. Once you have a bit of intuition about how Deep Learning algorithms work, you might want to understand how things work below the hood.
Also: Bayesian Machine Learning, Explained; Is Google Tensorflow Object Detection API the Easiest Way to Implement Image Recognition?; Data Science of Variable Selection: A Review; 7 Steps to Understanding Deep Learning
This paper describes a set of algorithms for Natural Language Processing (NLP) that match or exceed the state of the art on several evaluation tasks, while also being much more computationally efficient.
This post provides an overview of a small number of widely used data visualizations, and includes code in the form of functions to implement each in Python using Matplotlib.
This article explains K-means algorithm in an easy way. I’d like to start with an example to understand the objective of this powerful technique in machine learning before getting into the algorithm, which is quite simple.
Gain expertise in emerging operations, marketing, network analytics, data modeling, data science, and visualization. Next application deadline is Aug 1.
We explain how to easily access and manipulate the internal components of digital images using Python and give examples from satellite image processing.
This posts is a collection of a set of fantastic notes on the fast.ai deep learning part 2 MOOC freely available online, as written and shared by a student. These notes are a valuable learning resource either as a supplement to the courseware or on their own.
Learn cutting edge techniques from world-class data scientists, including keynote speaker Kirk Borne. Use code WIS199 for 20% off early bird pricing through July 31, 2018.
By dropping 'Hadoop' from its name, the @strataconf 2018 in San Jose signaled the emphasis on machine learning, cloud, streaming and real-time applications.
This article provides a list of resources for data scientists who are transitioning from early-career/entry-level positions to more established roles. Surveys have shown a sharp decrease in satisfaction starting around 4 years into the profession, and resources are less obvious and readily available for professionals who have a good handle on the basics of data science than they are for beginners.
Also: The 4 Levels of Data Usage in Data Science; fast.ai Deep Learning Part 1 Complete Course Notes; What is Minimum Viable (Data) Product?; Cartoon: Data Scientist was the sexiest job of the 21st century until...; Text Mining on the Command Line
In this tutorial, I use raw bash commands and regex to process raw and messy JSON file and raw HTML page. The tutorial helps us understand the text processing mechanism under the hood.
In this post, I am going to verify this statement using a Principal Component Analysis ( PCA ) to try to improve the classification performance of a neural network over a dataset.
Connect and learn from these 9 data science rock stars and over 231 more presenters at ODSC West 2018, Oct 31-Nov 3 in San Francisco. Get 60% off until Friday, July 13 - reserve your spot here .
Also: Analyze a Soccer (Football) Game Using #Tensorflow Object Detection; 18 Inspiring Women In AI, Big Data, Data Science, Machine Learning; Timsort - the fastest #sorting #algorithm you've never heard of.
Download this report for a list of 10 mistakes to avoid when adopting advanced analytics, learn how you can improve your own implementation, and get a taste of premium membership.
Demystifying Data Science - a completely free, live online conference for aspiring data scientists and data-curious business professionals, July 24-25. Experience 28 interactive data science talks from industry-leading speakers. Register now!
For the data scientist within you let's use this opportunity to do some analysis on soccer clips. With the use of deep learning and opencv we can extract interesting insights from video clips
This posts is a collection of a set of fantastic notes on the fast.ai deep learning part 1 MOOC freely available online, as written and shared by a student. These notes are a valuable learning resource either as a supplement to the courseware or on their own.
This Summit will bring together Big Data thought leaders, top business executives and analytics experts for two days of insights, learning and networking. Use code KDNU18 for 25% off.
The deadline to save with Early Bird prices to the 2018 Predictive Analytics World for Government conference in Washington DC is fast approaching. This means that when you register by Friday, August 3, you could save up to $800.00.
Coming soon: ICDM/MLDM New York, Data Innovation Summits Las Vegas, ICML Stockholm, IJCAI/ECAI Stockholm, TDWI Anaheim, KDD-2018 London, JupyterCon NYC, and many more.
The application of data science to streaming data from vehicles is an emerging field. Here we review general trends and some specific examples of relevant data feeds and applications where data science can deliver value.
Download this chapter by Gordon Linoff and Michael Berry, and learn how to create derived variables, which allow the statistical modeling process to incorporate human insights.
With the arrival of the GDPR there has been increased focus on non-discrimination in machine learning. This post explores different forms of model bias and suggests some practical steps to improve fairness in machine learning.
This posts is a collection of a set of fantastic notes on the fast.ai machine learning MOOC freely available online, as written and shared by a student. These notes are a valuable learning resource either as a supplement to the courseware or on their own.
The AI Conference will premiere in London, 8–11 October. The Best Price expires on 13 July. Tutorials, training courses, and hotel rooms all book up quickly. Save and additional 20% on Gold, Silver and Bronze passes with the code KDN20.
Reproducibility, good management and tracking experiments is necessary for making easy to test other’s work and analysis. In this first part we will start learning with simple examples how to record and query experiments, packaging Machine Learning models so they can be reproducible and ran on any platform using MLflow.
In this tutorial, I classify Yelp round-10 review datasets. After processing the review comments, I trained three model in three different ways and obtained three word embeddings.
This post is a distilled collection of conversations, messages, and debates on how to optimize deep models. If you have tricks you’ve found impactful, please share them in the comments below!
In this post, traditional and deep learning models in text classification will be thoroughly investigated, including a discussion into both Recurrent and Convolutional neural networks.
Most Deep Learning frameworks currently focus on giving a best estimate as defined by a loss function. Occasionally something beyond a point estimate is required to make a decision. This is where a distribution would be useful. This article will purely focus on inferring quantiles.
In this post, we walk through investigating, retrieving, and cleaning a real world data set. We will also describe the cost benefits and necessary tools involved in building your own data sets.
At the AI in Finance Summit, Sept 6-7 in NYC, RE•WORK we will be showcasing the latest breakthrough technologies & their application in the financial sector with topics including Financial Compliance, Financial Forecasting, NLP, Investment, Blockchain & more.
A good programmer or software developer should have a basic knowledge of SQL queries in order to be able retrieve data from a database. This cheat sheet can help you get started in your learning, or provide a useful resource for those working with SQL.
Just by adding the term "automated" in front of these 2 separate, distinct concepts does not somehow make them equivalent. Machine learning and data science are not the same thing.
Also: Top 20 Python Libraries for Data Science in 2018; Why Data Scientists Love Gaussian; How to Execute R and Python in SQL Server with Machine Learning Services; Explaining Reinforcement Learning: Active vs Passive; What's the Difference Between Data Integration and Data Engineering?
How to Build a Data Science Portfolio
Cookiecutter Data Science: How to Organize Your Data Science Project
Cartoon: Data Scientist was the sexiest job of the 21st century until …
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