For data scientists, journalists, and business analysts, PLOTCON is THE opportunity to meet the creators of the tools you use everyday, ask questions, hear where the future is heading, and be part of the conversation. Use code KDNUGGETS to save.
CommBank, Australia leading bank, is searching for smarter, faster and better solutions. Which is why we're investing in people like you. Talented analytics professionals ready for the next step in their career.
Our events are people-focused, bringing brands, influencers, and talent into one space with one goal: to solve all the problems worth solving. We plan conferences that are fun and relaxed on the front end and organized and optimized on the back end.
Here are 3 key traits that differentiate between a data scientist and a great data scientist, starting with – great data scientist is obsessed with solving problems, not new tools.
There is no doubt R is language of choice for the majority of data scientists who want to understand data, especially those looking to leverage its great machine learning packages.
400+ leading experts and business executives convene at the Deep Learning in Finance & Retail and Advertising Summits, London, June 1-2. KDnuggets subscribers get 20% off with code KDNUGGETS.
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 Apr 13.
The focus is increasingly shifting from storing and processing Big Data in an efficient way, to applying traditional and new machine learning techniques to drive higher value from the data at hand.
Unlike a lot of other tutorials which often pull from the real-time Twitter API, we will be using the downloadable Twitter Analytics data, and most of what we do will be done in Pandas.
While some opponents still hold the misconception that the 'science is not yet in' on the culprit, the scientific community has long reached a consensus to the drivers behind the increase in global temperatures.
Gain insight into their build, partner or buy decisions, their real-life implementation stories, using bots/VAs to generate customer insights, and the unexpected hurdles they had to overcome. Use code KNUG15 for a 15% discount.
We see beginnings of both standardization and specialization, with graduate analytics curriculum that covers math, statistics, CS, IT systems, and communications. We also see specializations in data science and BI, and verticals like marketing and healthcare analytics.
Despite the recent success of RL, there is still a lot of work to be done before it will become a mainstream technique. In this blog-post, we look at some of the remaining challenges that are currently being studied.
We discuss the advantages of tree based techniques, including automatic variable selection, variable interactions, nonlinear relationships, outliers, and missing values.
What Is Data Science, and What Does a Data Scientist Do?; The Most Underutilized Function in SQL; Getting Started with Deep Learning; Getting Up Close and Personal with Algorithms; How to think like a data scientist to become one
The rise in serverless architectures along with marketplaces from cloud providers creates a significant momentum to democratize big data analytics. Machine learning or AI services are much more valuable, tangible and easier to understand for businesses than clumsy big data platforms.
Structural Equation Modeling (SEM) is an extremely broad and flexible framework for data analysis, perhaps better thought of as a family of related methods rather than as a single technique. What is its relevance to Marketing Research?
The Big Data Innovation Summit is coming to San Francisco, April 19 & 20. We are now down to the final batch of passes - secure yours with $200 off using code KD200.
Learn Anomaly Detection, Deep Learning, or Customer Analytics in R online at Statistics.com with top instructors who are leaders of the field. Use code 3CAP17 before March 30 to save $170.
This post approaches getting started with deep learning from a framework perspective. Gain a quick overview and comparison of available tools for implementing neural networks to help choose what's right for you.
Join over 150 Chief Data Officers, Chief Analytics Officers and other senior data leaders in San Francisco. A few VIP complimentary places are still available.
The focus is increasingly shifting from storing and processing Big Data in an efficient way, to applying traditional and new machine learning techniques to drive higher value from the data at hand.
Successful analytics starts with immersive, interactive training and goal-driven strategy. TMA’s live online and classroom training spans all skill levels and analytic team roles to build analytic leaders. Washington, DC in April, Live Online in May and Seattle in July.
The author went from securities analyst to Head of Data Science at Amazon. He describes what he learned in his journey and gives 4 useful rules based on his experience.
This article is intended to help define the data scientist role, including typical skills, qualifications, education, experience, and responsibilities. This definition is somewhat loose, and given that the ideal experience and skill set is relatively rare to find in one individual.
Apache: Big Data gathers the Apache projects, people and technologies in Big Data in Miami, May 16-18, 2017. KDnuggets readers save 20% with discount code ABDKD20.
Also Hastie, Tibshirani and Friedman - The Elements of Statistical Learning Book PDF; Getting Close and Personal w. #MachineLearning #Algorithms; Open Source Toolkits for Speech Recognition.
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 Apr 13.
Explore the latest advancements in deep learning and their applications in industry and healthcare at the Deep Learning Summit and Deep Learning in Healthcare Summit in Boston, 25-26 May. Use discount code KDNUGGETS to save 20% off all tickets.
Learning about what these people do made it clear that when you are deeply involved in A/B testing at scale, there is a tremendous rush from doing so many different things that matter.
During a rally in February, President Trump had these disparaging words about Sweden’s humane immigration policy... but nothing of note actually happened the previous night in Sweden.
How did a stuffed yellow elephant permanently intertwine itself in history? What is a data scientist? Why is right now the golden age for data science? Data Crunch podcast examines these questions with the help of Gregory Piatetsky-Shapiro and Ryan Henning.
Kanri combination of patented statistical and process methods provide a powerful ability to evaluate large data, tells users the exact distance from target, and variable contributions for participant. Free trial and 88% KDnuggets discount for the first 100 buyers.
Octoparse has both a user-friendly, point and click UI for beginner and advanced mode for experts. It also provides Cloud Service with at least 6 cloud servers running your tasks simultaneously. Try it now.
It's critical to understand that statistical models are simplified representations of reality and they're all wrong but some of them are useful. So why do we use statistical models?
We've put together a brief summary of the top algorithms used in predictive analysis, which you can see just below. Read to learn more about Linear Regression, Logistic Regression, Decision Trees, Random Forests, Gradient Boosting, and more.
These are the MUST attend data events in the FS & Insurance worlds - the combination of our senior attendees, and dedicated, directly relevant industry focus. Use code KD30 for 30% off.
Different business units in the organisation have different behaviours (e.g. turnover rate) and they can’t be compared with each other. So, how can we tell whether the changes in their behaviour are reasons for concern?
Also 17 More Must-Know Data Science Interview Questions and Answers, Part 3; 7 Types of Data Scientist Job Profiles; Applying Machine Learning To March Madness
We examine the connection between Climate Change Denial and CO2 emissions and find a strong correlation - countries with higher CO2 emissions/capita also have higher percentage of climate skeptics.
This post is an overview of a spam filtering implementation using Python and Scikit-learn. The results of 2 classifiers are contrasted and compared: multinomial Naive Bayes and support vector machines.
Grab this free book on Open Data Science, a movement that makes the open source tools of data science—data, analytics and computation—work together as a connected ecosystem.
Beware of online and market research studies which can lead to false or spurious claims. We examine several notable examples including Google Street View and Argentina inflation.
March Madness is upon us. But before you get your brackets set, check out this overview of using machine learning to do the heavy lifting for you. A great discussion, and a timely topic.
We detail 50 companies leading the Artificial Intelligence revolution in AD Sales, CRM, Autotech, Business Intelligence and analytics, Commerce, Conversational AI/Bots, Core AI, Cyber-Security, Fintech, Healthcare, IoT, Vision, and other areas.
TDWI comes to Chicago May 7-12, and KDnuggets readers get special savings! Save 30% through next Friday, March 24 using priority code KDSAVE30. Did you know that teams of 3+ save an extra 10%?
Also: #ICYMI The #DataScience Project Playbook; Every Intro to #DataScience Course on the Internet, Ranked; Quick reference to #Python in a single script.
Successful analytics starts with immersive, interactive training and goal-driven strategy. TMA’s live online and classroom training spans all skill levels and analytic team roles to build analytic leaders. Washington, DC in April, Live Online in May and Seattle in July.
There is no one profile for the Data Scientist, but I tried to make a few generic job profiles that can somewhat fit job descriptions of different companies. I think there is way too much variety, but I had to narrow down on a set of profiles. Check out the list.
The third and final part of 17 new must-know Data Science interview questions and answers covers A/B testing, data visualization, Twitter influence evaluation, and Big Data quality.
Improve your skills and have fun with other talented students from all around the world. Reg by April 9, online qualification ends May 31, and final phase in Paris, Fall 2017.
Learn how to experiment with embodied robotic cognition with IBM Project Intu, a platform that extends Deep Learning and other cognitive services to new devices with minimum coding.
Grunion is a patent-pending query optimization, translation, and federation framework built to help bridge the gap between data science and data engineering teams. Read more to request access.
We ask UN Global Pulse Director about the 'Data For Climate Action' Challenge, the best sources of climate data, examples of using data for climate mitigation and climate adaptation, and resources for convincing climate change skeptics.
What makes a good data visualization - a Data Scientist perspective; How to Get a Data Science Job: A Ridiculously Specific Guide; Visualizing Time-Series Change; Working With Numpy Matrices: A Handy First Reference; Beginner’s Guide to Customer Segmentation
The challenge is to harness data science and big data from the private sector to fight climate change. Data scientists, researchers, and innovators - submit proposals at DataForClimateAction.org by 10 April 2017.
Are you a data science professional and want to advance your career as Data Science Unicorn? Here we provide important business concepts and guidelines required for a data science techie to become a Unicorn.
Predictive Analytics World comes to Chicago, June 19-22. Yes, it has a stellar agenda, but there are some things you did not know about the wonderful line-up of speakers coming. Read this for more!
What if a simple, deterministic approach which did not rely on randomization could be used for centroid initialization? Naive sharding is such a method, and its time-saving and efficient results, though preliminary, are promising.
Here a list of the best courses in data science from Udemy, covering Data Science, Machine Learning, Python, Spark, Tableau, and Hadoop - only $19 until March 31, 2017.
The HPI Future SOC (Service-Oriented Computing) Lab is a cooperation of the Hasso Plattner Institute (HPI) and industrial partners, providing free access to a powerful Big Data & Computing infrastructure. It is now accepting project proposals for 2017.
This introductory tutorial does a great job of outlining the most common Numpy array creation and manipulation functionality. A good post to keep handy while taking your first steps in Numpy, or to use as a handy reminder.
The Intelligence Analytics Summit takes place May 22-24 in Washington, D.C., where decision makers in the Intelligence community will learn to efficiently analyze and assess data and generate actionable intelligence in real time.
The Data Modeling Certificate will build your skills and get you started with emerging techniques to model the complex structures in big data and NoSQL databases. Save 30% thru Mar 17 with code KDNEWS.
It has been a challenge to keep up-to-date with new concepts from NoSQL, to machine learning, to Internet of things and blockchain, but Little Bee Books is here with free solutions to helping you do so.
Since 2009, Predictive Analytics World has been the leading commercial event for advanced analytics and machine learning, and 2017 is YOUR year to become a speaker. Apply to speak Oct 29-Nov 2 at Predictive Analytics World Events in New York.
This is the premier event for the industry high-level data practitioners to meet and discuss the biggest strategic issues of the day. Use code CDOIN500 for $500 until March 17.
When creating time-series line charts, it’s important to consider which of the following messages you would like to communicate: Actual value of units? Change in absolute units? Percent change? Change from a specific point in time?
At the core of customer segmentation is being able to identify different types of customers and then figure out ways to find more of those individuals so you can... you guessed it, get more customers!
Also Deep Forest: Towards An Alternative to Deep #NeuralNetworks; An Overview of #Python #DeepLearning Frameworks; The Gentlest Introduction to Tensorflow - Part 2.
We examine principles of good data visualization, including some great and terrible examples, guidelines for human perception, focus on key variables, changes and trends, avoiding chart junk, and more.
Unlike other data science problems, there is no one method for predicting which customers are likely to churn in the next month. Here we review the most popular approaches.
The article studies the advantage of Support Vector Regression (SVR) over Simple Linear Regression (SLR) models for predicting real values, using the same basic idea as Support Vector Machines (SVM) use for classification.
Neuroscience is very complex and advanced study of brain and people often misuse this term. Here we try to explain neuroscience terminologies and use of data science for such studies.
In this intro cluster analysis tutorial, we'll check out a few algorithms in Python so you can get a basic understanding of the fundamentals of clustering on a real dataset.
Strata + Hadoop World is the leading event on how big data and ubiquitous, real-time computing is shaping the course of business and society. Win KDnuggets free pass to Strata + Hadoop World London.
Find out how the Golden State Warriors, Country Music Association, Toronto Film Festival, San Francisco 49ers, Netflix and senior executives in sports, music and live events use analytics to engage, retain and monetize their data. Use code XLIVEKD to save.
Job hunting is challenging and sometimes frustrating task and we all experience it in our career. Here we provide a very specific and practical guide to get your dream job in Data Science world.
This is an overview of the XGBoost machine learning algorithm, which is fast and shows good results. This example uses multiclass prediction with the Iris dataset from Scikit-learn.
The courses offered in the Penn State World Campus 30-credit online Master's in Data Analytics degree could enhance your credentials in the growing field of data analytics.
Make your strategy better and more profitable with Data Strategy for Business Leaders program, and leverage consumer data for valuable insights and effective marketing decisions with Data Driven Marketing program.
If Big Data is to realize its potential, people need to understand what it is capable of, what information is out there and where every piece of data comes from. Without such transparency and understanding, it will be difficult to persuade people to rely on the findings.
7 More Steps to Mastering Machine Learning With Python; An Overview of Python Deep Learning Frameworks; Every Intro to Data Science Course on the Internet, Ranked; The Data Science Project Playbook; Hadoop Is Falling – Why?; Every Intro to Data Science Course on the Internet, Ranked
Join the growing crowd at Predictive Analytics World (May 14-18 in San Francisco) to take advantage of the potential of predictive analytics. Keynote speaker line-up has been announced.
Oxford Deep NLP Course; scikit-plot: Data Visualization for Scikit-learn Results; Machine Learning at Berkeley's ML Crash Course: Neural Networks; Predicting parking difficulty with machine learning; TensorFlow 1.0 Release
Cazena Data Science Sandbox as a Service makes it simple to load data, and run R, Python, SQL and advanced analytics on a high-performance Apache Spark platform. Get a 3-minute demo!
Coming soon: Strata + Hadoop World San Jose, Machine Intelligence Summit SF, Predictive Analytics Summit London, SAS Global Forum Orlando, TDWI Accelerate Boston, The Marketing Analytics and Data Science Conference San Francisco, and more.
Bokeh is the Python data visualization library that enables high-performance visual presentation of large datasets in modern web browsers. The package is flexible and offers lots of possibilities to visualize your data in a compelling way, but can be overwhelming.
Thomas Dinsmore critical examination of Gartner 2017 MQ of Data Science Platforms, including vendors who out, in, have big changes, Hadoop and Spark integration, open source software, and what Data Scientists actually use.
The most advanced kind of Deep Learning system will involve multiple neural networks that either cooperate or compete to solve problems. The core problem of a multi-agent approach is how to control its behavior.
Efficient experimentation can save both time and money in the long term when it helps optimize product or process performance. This webcast shows how a dynamic model can dramatically improve outcomes.
For this guide, I spent 10+ hours trying to identify every online intro to data science course offered as of January 2017, extracting key bits of information from their syllabi and reviews, and compiling their ratings.
Online MS in Predictive Analytics prepares students for rewarding careers by training in data science, modeling, business management, communications, and information technology. Summer application deadline is April 15.
Our predictions include: 2017 will be the year of Deep Learning (DL) technology, Artificial General Intelligence is still far away, Software and Hardware Progress will accelerate, and AI will have unexpected socio-political implications.
In this post, learn to build a bot to answer frequently asked questions, reducing lag time for more customers and taking the load off of engineers, ensuring they can concentrate on building products!
50 Companies Leading the #AI Revolution; #AI Nanodegree Program Syllabus: Term 1, In Depth; What is a Support Vector Machine, and Why Would I Use it?; 6 Easy Steps to Learn Naive #Bayes Algorithm (with code in #Python).
Getting new customers is much more more expensive than retaining existing ones, so reducing churn is a top priority for many firms. Understanding why customers churn and estimating the risks are powerful components of a data-driven retention strategy.
Three years ago, looking beyond Hadoop was insanity, and there was little else that could come close. Recently, adoption of Hadoop has slowed down considerably. We examine why.
This post is a follow-up to last year's introductory Python machine learning post, which includes a series of tutorials for extending your knowledge beyond the original.
The Best R Packages for Machine Learning
What is Structural Equation Modeling?
50 Companies Leading The AI Revolution, Detailed
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