20 Questions to Detect Fake Data Scientists; TensorFlow Disappoints - Google Deep Learning falls shallow; 7 Common Data Science Mistakes and How to Avoid Them; Businesses Will Need One Million Data Scientists by 2018.
Academic/Research positions Analytics and Data Science at INRIA, U. Texas, U. Mannheim, Eindhoven U. of Technology, IBM Social Good Fellowship, Yale, Xavier, U. Western Switzerland, U. Paris-Est Marne-la-Vallee, and U. Tampere.
The rise of Apple/Google smartphone payments and new fintech start ups present challenges to traditional banks. Banks can fight back, but they need to understand how to better use their data to understand its customers.
Feature engineering plays major role while solving the data science problems. Here, we will learn Feature Hashing, or the hashing trick which is a method for turning arbitrary features into a sparse binary vector.
A detailed explanation of one of the most used machine learning algorithms, k-Nearest Neighbors, and its implementation from scratch in Python. Enhance your algorithmic understanding with this hands-on coding exercise.
With mathematical rigor and narrative flair, Adam Kucharski reveals the tangled history of betting and science. The house can seem unbeatable. In this book, Kucharski shows us just why it isn't. Even better, he shows us how the search for the perfect bet has been crucial for the scientific pursuit of a better world.
ESSEC Specialization on “Strategic Business Analytics” was ranked #5 most coveted Coursera certificate on LinkedIn in 2015. The course is aimed at students, business analysts and data scientists who want to apply statistical knowledge and techniques to business contexts.
How to take data science techniques even further to extract actionable insight and take advantage of advanced modeling features. You will walk away with several different methods to turn your ordinary regression into an extraordinary regression!
Modern big data discovery tools enable all employees to access the data, streamlining the data prep process, and allowing data scientists to spend more time on advanced analytics. The infographics in this post show the evolution of the data scientist from data drudgery to modern data science for all.
Data scientist in business is as similar as to that of a detective: discovering the unknown. But, while venturing onto this journey they do tend to fall into the pitfalls. Understand, how these mistakes are made and how you can avoid them.
TextAnalytics examines: Is #Quran really more violent that #Bible? Why R Users will inevitably become #Bayesians; Next #MachineLearning problem: what to do with 80% accurate algorithm? ;Learning to Code #NeuralNetworks #MachineLearning Tutorial;
Listen to some of the greatest data scientists to speak about their use of KNIME software to solve complex data problems in life sciences, manufacturing, marketing, retail sales, and many other areas. Register and save with code KDNUGGETS_KNIME_SUMMIT2016.
Two upcoming webinars show how to use the powerful GraphDB from Ontotext: Powerful Searches and Data Visualization in Graph Database (Jan 28) and Transforming your Graph Analytics with GraphDB (Feb 4). Check also GraphDB free version.
The financial market is the ultimate testbed for predictive theories. With this post we want to highlight the common mistakes, observed in the world of predictive analytics, when computer scientists venture into the field of financial trading and quantitative finance.
20 Questions to Detect Fake Data Scientists; Google Deep Learning TensorFlow Disappoints; Yahoo Releases the Largest-ever Machine Learning Dataset for Researchers; Research Leaders on Data Mining, Data Science and Big Data.
Apache Spark had robust machine learning, graph, streaming, and in-memory capability to the Hadoop-centric ecosystem. In 2016, we expect adoption in diverse big data, advanced analytics, data science, Internet of Things, and other application domains.
Consumer Analytics is still the leading application area for analytics & data mining (although losing its share), followed by Finance, and Banking. Highest growth is in Games, Entertainment/Music, and Social Good/Non-profit applications. 3-year comparison shows surprising stability.
6 questions surrounding Hadoop and Big Data are posed and answered, including those related to implementation, management, and practical uses. Find out where Hadoop currently sits in the world of Big Data.
R vs Python for Data Science: The Winner is ...; 60+ Free Books on Big Data, Data Science, Data Mining, Machine Learning; Top 20 Python Machine Learning Open Source Projects; 50+ Data Science and Machine Learning Cheat Sheets.
Currently, only global corporations like Google or Facebook can maintain a vast knowledge graph about the world. Little companies which rely on knowing world context need to unite to create a Public Knowledge Graph, or they will fall further behind the big guys.
Data Scientist is the best job in America, according to Glassdoor, with median base salary $117K, and high career prospects. Analytics Manager ranks n. 11. However, the number of available positions is much less than predicted shortage of 140-190,000.
Quora is launching a new format for interacting with domain experts and sharing knowledge, and its first topic is Machine Learning. Yoshua Bengio is the first expert, and he is accepting questions now.
Camelyon16 challenge in conjugation with IEEE International Symposium on Biomedical Imaging is here! You have to design and develop a system which can detect and localize metastatic regions in whole slide microscopic images.
20 Questions to Detect Fake Data Scientists; TensorFlow Disappoints - Google Deep Learning falls shallow; Machine Intelligence vs Machine Learning vs Deep Learning vs AI; What To Expect from Deep Learning in 2016.
A discussion of three major approaches to building smart machines - Classic AI, Simple Neural Networks, and Biological Neural Networks - and examples as to how each approach might address the same problem.
Global warming has been argued in depths and breadths and arguments for and against are championed too.Here, with a simple data science we obtained a simple (and increasingly accepted) conclusion: the global warming is real.
Instead of proceeding with a mediocre analysis, join us for this 2-part webinar series. We will show you how modern algorithms can take your regression model to the next level and expertly handle your modeling woes
Successful analytics in the big data era does not start with data and software, but with hands-on, immersive training and goal-driven strategy - get it from The Modeling Agency in Orlando, February 18-26.
#NIPS2015 #DeepLearning Tutorial by top researchers Bengio, LeCun, Hinton; Understanding Rare Events and streak patterns; Most Influential Data Scientists on Twitter and Quora; 12 Tips for Data-Driven Research.
The industry is in the midst of the biggest change in enterprise computing in decades. Schroeder sees an acceleration in big data deployments, and has crystallized his view of market trends into these five major predictions for 2016.
There are many deep learning resources freely available online, but it can be confusing knowing where to begin. Go from vague understanding of deep neural networks to knowledgeable practitioner in 7 steps!
A top statistics professor and statistical researcher reflects on a number of awesome accomplishments by individuals in, and related to, the fields of statistics and data science, with a focus on the world of academia but with resonance far beyond.
We often look back at the past year and an overall history of rare events, and try to then extrapolate future odds of the same rare event, based on that. We illustrate here, that rare past events have no usefulness in understanding the rarity of the same events in the future!
New year has just begun and for the year ahead is a good opportunity to consider the passage of time, how much is left to each of us. We’re presenting some of the best visualizations of lifespans and life expectancy.
Through real-world case studies, this technical primer will help you: find best practices to interactively explore the patterns in your data, build useful statistical models, and visually interact with these models.
New, fresh Predictive Analytics World events feature prominent predictive analytics experts to learn from. Join us this spring in San Francisco as part of the leading, world-renowned events in predictive analytics. Use code KDN150 to save on top of early bird rates.
Through lectures and hands-on sessions, the summer school will cover the state of the art in using deep learning methods within our two disciplines, Computer Vision and Natural Language Processing. The school is open to anyone interested, free of charge. Researchers resident in COST countries are eligible to apply for financial support.
Jake Porway is a machine learning and technology enthusiast, and founder of DataKind nonprofit which helps organizations use the power of data science in the service of humanity. He will do Reddit AMA on Jan 13, 2016.
AI, machine learning, speech recognition and NLP technologies are converging to allow creation of Intelligent Virtual Assistants, explored in the first ever Virtual Assistant Summit, Jan 28-29, in SF. Use code KDNUGGETS to save 20% off.
We witness an explosion of Big Data in finance, biology, medicine, marketing, and other fields. This book describes the important statistical ideas for learning from large and sparse data in a common conceptual framework.
During his recent AMA, machine learning star Nando de Freitas answers a host of questions on a number of topics, including Bayesian methods in deep learning, harnessing AI for the good of humanity, and what the future holds for machine learning.
NASA Juno mission will arrive at Jupiter. The Summer Olympics will take place in Rio de Janeiro. The US will have a presidential election. And prescriptive analytics will take center stage as the ultimate destination on the analytics journey.
TensorFlow is Terrific - A Sober Take on Google Deep Learning; Data Science in Python 100 Interview Questions and Answers; 20 Questions to Detect Fake Data Scientists; There are only 5 questions #MachineLearning can answer.
The terrorism threat advisory system was designed to give the public prior warning to when terrorist plots are about to unfold. However, the analysis shows that this system is not more helpful than monkey throwing a dart.
The Art of Data Science: The Skills You Need and How to Get Them; 20 Questions to Detect Fake Data Scientists; TensorFlow is Terrific - A Sober Take on Deep Learning Acceleration; What questions can data science answer?
There are only five questions machine learning can answer: Is this A or B? Is this weird? How much/how many? How is it organized? What should I do next? We examine these questions in detail and what it implies for data science.