The decision tree is one of the oldest and most intuitive classification algorithms in existence. This post provides a straightforward technical overview of this brand of classifiers.
What are universities and colleges doing to make Big Data skills easier to obtain, and how are they speeding up the educational process to get these people into the workforce faster?
Are you currently pursuing your masters in Data Science? Overwhelmed with Buzzwords and Information? Don’t know where and how to start your study? Then start with this article and a starter kit provided, but learn it for excellence and not just for the exams.
This edition of Deep Learning Research Review explains recent research papers in the deep learning subfield of Generative Adversarial Networks. Don't have time to read some of the top papers? Get the overview here.
5 EBooks to Read Before Getting into A Machine Learning Career; Big Data Science: Expectation vs. Reality; Mind of a Data Scientist; Machine Learning: A Complete and Detailed Overview; Using Machine Learning to Detect Malicious URLs
Join Northwestern University's Master of Science in Analytics for an upcoming Executive Education Course, March 23 - 24, 2017 in San Francisco: Big Data to Big Profits.
Read an interview with KDnuggets Top Blogger Adit Deshpande, a deep learning aficionado and masterful blogger, who also just happens to be a second year undergraduate student.
Is your code good enough to be calling yourself a Data Scientist? Figure out how to determine the answer to this question... and gain some suggestions on ensuring that the answer is "yes!"
This is an overview (with links) to a 5-part series on introductory machine learning. The set of tutorials is comprehensive, yet succinct, covering many important topics in the field (and beyond).
In most of the scientific researches, due to large amount of experiment data, statistical analysis is typically done by technical experts in computing and statistics. Unfortunately, these experts are not the experts of underlying research; which may cause gaps in analysis. If actual researchers are given easy to use tools and methods to handle and analyse data, it will enrich the research outcome for sure.
This is a write-up of an experiment employing a machine learning model to identify malicious URLs. The author provides a link to the code for you to try yourself.
The path to success and happiness of the data science team working with big data project is not always clear from the beginning. It depends on maturity of underlying platform, their cross skills and devops process around their day-to-day operations.
This post provides a technical overview of frequent pattern mining algorithms (also known by a variety of other names), along with its most famous implementation, the Apriori algorithm.
5 EBooks to Read Before Getting into A #MachineLearning Career; Top @LinkedIn Groups for #Analytics, #BigData, #DataMining, #DataScience in 2016; #ICYMI 10 Algorithms #MachineLearning Engineers Need to Know; European #MachineIntelligence Landscape
Could you have imagined a few years back how open data could be useful to get the insights about your county? Its changes, population, health, crime, education and many other aspects? How are other counties doing compared to yours? This article presents just such a benchmarking case study of US counties.
First part of this series was about formulation of the business problem and engineering the data points. This is the last part of the series and it tells us about exploratory data analysis and feature engineering.
Learn more about Academic Torrents, a platform for researchers to share data consisting of a site where users can search for datasets, and a BitTorrent backbone which makes sharing data scalable and fast.
"I, for one, welcome our new computer overlords." Far from being our overlords, machines are our societal companion, our partner that supports our success and supports the functioning of our society. But the support the machine gives us is rudimentary at best.
Insurance Nexus conducted exclusive interviews with Manulife, the largest life insurer, and a mid-tier insurer, IAT, and created an exclusive white paper on analytics, business transformation and customer-centricity.
By now, many people are aware of which technical skills are required for a Data Scientist, but do you know what mindset or thinking is required to be a good data scientist? Let’s read this two parts series by an industry expert.
Find out how SAP can bring a new level of predictive analytics capabilities to your organization, and get a fully functional version of SAP BusinessObjects Predictive Analytics software for 30 days - free download.
Top 10 Data Science Videos on YouTube; 5 EBooks to Read Before Getting into A Machine Learning Career; Jupyter Notebook Best Practices for Data Science; A Beginners Guide to Neural Networks with Python and SciKit Learn 0.18!; European Machine Intelligence Landscape
In the history of mankind and past three major industrial revolutions, horizontal innovations like wheel, steam engine, electricity and integrated chips have always been the crux of it and they changed the world dramatically. Well, fourth one is on its way! Want to know what’s driving it? Have a read at this crisp article.
CDOs are the new hot role to rock. Read about the CDO Toolkit, which integrates the disciplines of economics and analytics to help the CDO to ascertain the economic value of the organization’s data and data sources.
Read an interview with the Dean of Big Data Bill Schmarzo, one of KDnuggets' Top Bloggers for September, and gain some insight on the topics data science, IoT, Big Data... and jeans!
Collecting high quality data from various resources and turning it into data products is one of the ways to monetize data in today’s digital economy. Lets take a deeper look into it.
A carefully-curated list of 5 free ebooks to help you better understand the various aspects of what machine learning, and skills necessary for a career in the field.
Ajit Jaokar, a leading expert in the field, shares his views on evolution of IoT, Data Science, Smart Cities, the promise and dangers of AI, and encouraging young people.
Big Data and Analytics became the largest group. Overall engagement rates decline, but liking a post is 6.5 times more common than commenting. Machine Learning & Data Science, KDnuggets, and Data Scientists have the highest engagement levels.
Evolution is the truth of mankind and it’s inevitable. We all are evolutionizing everyday biologically as well as technologically and so do our roles and responsibilities. Here is the summary of evolution of Data Scientist role and it’s hiring trends in industry throughout the decade.
#DeepLearning Key Terms, Explained; Free Foundations of #DataScience text PDF; Top 12 Interesting Careers to Explore in #BigData; #ICYMI The 10 Algorithms #MachineLearning Engineers Need to Know
This post outlines setting up a neural network in Python using Scikit-learn, the latest version of which now has built in support for Neural Network models.
Respected Data Scientist Daniel Tunkelang shares some insight into problems lying at the crossroads of software engineering and data science, and prescribes one major solution: reduce scope!
In today’s Internet world, humans express their Emotions, Sentiments and Feelings via text/comments, emojis, likes and dislikes. Understanding the true meanings behind the combinations of these electronic symbols is very crucial and this is what this article explains.
This post outlines the European machine intelligence landscape, which, until recently, has been under-appreciated in its contribution to the innovation and commercialisation of machine intelligence technologies.
Getting started with Data Science or need a refresher? Clustering is among the most used tools of Data Scientists. Check out these 10 Clustering-related terms and their concise definitions.
Predictive Analytics World for Business comes to the Jacob Javits Center in New York City, October 23-27. Register by October 22 and get $350 off of onsite rates when combined with KDnuggets reader discount!
We interview LinkedIn about their recently published LinkedIn Knowledge Graph which connects their many millions of members, jobs, companies, and more.
Deep Learning Key Terms, Explained; Artificial Intelligence, Deep Learning, and Neural Networks, Explained; Top 12 Interesting Careers to Explore in Big Data; Data Preparation Tips, Tricks, and Tools: An Interview with the Insiders; Here's How IT Departments are Using Big Data
MLDB is an opensource database designed for machine learning. Send it commands over a RESTful API to store data, explore it using SQL, then train machine learning models and expose them as APIs.
Learning and the future are the key topics in the recent Youtube videos on Data Science. The main questions revolve around: “how to become a Data Scientist”, “what is a data scientist”, and “where data science is going”. But why there is so little explanation of data science to the masses?
The nation needs brilliant, creative minds to lead the next generation of crime forecasting. Enter the competition sponsored by National Institute of Justice to help improve policing and public safety with data science. $1.2 Million will be awarded.
Regression, Decision Trees, and Cluster analysis remain the most commonly used algorithms in the field, R continues to ascend, job satisfaction remains high, but customer understanding still needs improvement.
This article is meant to explain the concepts of AI, deep learning, and neural networks at a level that can be understood by most non-practitioners, and can also serve as a reference or review for technical folks as well.
The NYU Stern MS in Business Analytics is the only premier global degree program of its kind designed for senior level professionals focused on the intersection of business strategy and data science. Deadline is November 1, 2016.
This online, part-time immersive data science bootcamp is geared to help working professional become data scientists in 24 weeks, with live lectures, one-on-one supports, group study sessions, and more. Next session starts Jan 9, 2017.
Data preparation and preprocessing tasks constitute a high percentage of any data-centric operation. In order to provide some insight, we have asked a pair of experts to answer a few questions on the subject.
A tensor - a multidimensional matrix - is ideal for modeling multiaspect data, such as social interactions, which can be characterized by the means of communication, who is interacting, and the time and location of the interaction, for example.
This post is a followup to how to structure data science teams, with a focus on how we get stuff done. The same principles we follow can be applied at your data startup or data science team.
Apache: Big Data Europe (Nov 14-16, Seville, Spain) will gather together the Apache projects, people and technologies working in Big Data, ubiquitous computing and data engineering and science to educate, collaborate and connect. Register by Nov 3 to save over $250!
Welcome to the R graph gallery, a collection of R graph examples, organized by chart type, searchable by R function, with reproducible code and explanation.
Most Active #DataScientists, Free Books, Notebooks & Tutorials on #Github; Why Not So Hadoop?; Free #MachineLearning text PDF, from theory to algorithms; Top @reddit #MachineLearning Posts September.
From data driven strategies to decision making, the true worth of Big Data has been realized, and has led to opening up of amazing career choices. Check out these 12 interesting careers to explore in Big Data.
We look behind the curtain at the CAP Certification program designed to measure analytics professional’s knowledge across seven unique areas of the analytics process,
By 2020, almost every company will derive value and earn revenue from data. This webinar will explain how Big Data and its related technologies are growing today – and what they might look like tomorrow.
Have a look at our top blog posts of Q4 2015, some of which continue to be among the most popular on our site, while others are still topical and warrant a second look.
Learn how to get started with predictive modeling and overcome strategic and tactical limitations that cause data mining projects to fall short of their potential. Next webinar is Oct 13.
Humans & Machines Ethics Canvas’ main goal is to be a guide for critical thinking throughout the ethical decision-making process. It acts as a value system and an ethics framework to assess the influence of machine learning and software development while developing a system for individuals, teams, and organisations.
PAW for Healthcare - Oct 23-27 in New York - brings together top predictive analytics experts, practitioners, authors, and healthcare thought leaders to discuss concrete examples of deployed predictive analytics. Save with code KDN150.
The use cases for big data are clear when it comes to areas like marketing, healthcare, and retail, but IT’s use of big data is a little less clear. Recently, however, some IT departments are finding ways to use big data to improve their individual operations along with that of the entire organization.
Battle of the Data Science Venn Diagrams; Automated Data Science & Machine Learning: An Interview with the Auto-sklearn Team; The 10 Algorithms Machine Learning Engineers Need to Know; Top Algorithms and Methods Used by Data Scientists; Biggest Issues in Data Science
Multipliers and Big Data analytics are tightly integrated. Multipliers feed into and improve the accuracy of our analytics, while analytics feed into and improve the accuracy of our multipliers. They should be used together at all levels of the organization.
Join us as we explore key areas of technology driving innovation and the next wave of billion dollar startups. Register now with the code DN2016KDN35 and get a 35% discount on tickets.
Successful analytics in the big data era does not start with data and software, but with immersive hands-on training and goal-driven strategy. Get this training with TMA courseware, which spans all skill levels and analytic team roles - Wash-DC in October or Live Online in November.
This post proposes and outlines adversarial validation, a method for selecting training examples most similar to test examples and using them as a validation set, and provides a practical scenario for its usefulness.
Are businesses getting the ROI they desire given the hype around big data analytics? With all the promises of big data analytics, why are more than half the companies still in the red with respect to analytics investments?
Google Research announces the Open Images dataset; Canadian Government Deep Learning Research grant; DeepMind: WaveNet - A Generative Model for Raw Audio; Machine Learning in a Year - From total noob to using it at work; Phd-level machine learning courses; xkcd: Linear Regression
The highlights from The Burtch Works Study: Salaries of Predictive Analytics Professionals 2016, which examines updated compensation and demographic data on over 1,200 analytics professionals across the US.
First came Drew Conway's data science Venn diagram. Then came all the rest. Read this comparative overview of data science Venn diagrams for both the insight into the profession and the humor that comes along for free.
7 Steps to Mastering SQL for #DataScience; New Andrew Ng #MachineLearning #Book Under Construction, #Free Draft Chapters; Top #DataScientist Claudia Perlich on Biggest Issues in #DataScience; Awesome Public Datasets on GitHub
Five new courses from Statistics.com, fully online and asynchronous - interact with leading experts in private forums. Use promo code “kdn2016” for $50 off any course.
PAW for Healthcare - Oct 23-27 in New York - brings together top predictive analytics experts, practitioners, authors, and healthcare thought leaders to discuss concrete examples of deployed predictive analytics. Save with code KDN150.
This is an interview with the authors of the recent winning KDnuggets Automated Data Science and Machine Learning blog contest entry, which provided an overview of the Auto-sklearn project. Learn more about the authors, the project, and automated data science.
Postdoc on Data Mining at Umea (Sweden); Faculty Position in Data Exploration at Emory (Atlanta); Professor, Business Analytics at U. of Iowa; CS Faculty - Data Analytics (open rank) at Rowan U; and more.
Being a good data scientist takes a lot of effort. Staying relevant, making the right connections and consistently upgrading your skill set is essential. So, what steps have you taken this year to launch your data science career to the highest level? Use code ODSC-KDN to save.
This SF Bay ACM annual event combines sessions, keynote, and optional tutorial - great opportunity to learn about Data Science and connect with others, and almost free.
TDWI Austin focuses on state-of-the-art technologies and practices for storing, analyzing, and harnessing enterprise data to drive customer-centric innovation. Use code KD100 - Register by Oct 14 to save $890.
Data Science for Internet of Things (IoT) : Ten Differences From Traditional Data Science; Claudia Perlich on Biggest Issues in Data Science; Data Science Basics: Data Mining vs. Statistics; The 10 Algorithms Machine Learning Engineers Need to Know; Top Algorithms and Methods Used by Data Scientists
To alleviate the shortage of Data Scientists, many companies look to hire overseas and need to navigate the complex and expensive visa process. Here we compare two common visa categories for data scientists across 6 criteria employers care about (eligibility, legal fees, filing fees, quota, length of process, and chances of approval).
Coming soon: Data Science Conference Seattle, ODSC London, PAPIs Boston, PAW London, PAW Government DC, Deep Learning Summit Singapore, PAW Business NYC, IBM World of Watson, and many more.
Data Startup in mind? Need to structure different teams? Here are guidelines for structuring Data Team, Crawl Development Team, Data Infrastructure Team, and more.
Learn Data Science for Excellence and not just for the Exams
Machine Learning: A Complete and Detailed Overview
Big Data Science: Expectation vs. Reality
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