- Top KDnuggets tweets, Jul 20-26: Math-free simple explanation: #DeepLearning Demystified; Are #Humans Becoming More Machine-Like? - Jul 27, 2016.
Finally, a #TensorFlow book for humans; Great math-free simple intro explanation video: Deep Learning Demystified; Does #sentiment analysis work? A tidy analysis of Yelp reviews; JupyterLab: the next generation of the #Jupyter Notebook
- Would You Survive the Titanic? A Guide to Machine Learning in Python Part 3 - Jul 27, 2016.
This is the final part of a 3 part introductory series on machine learning in Python, using the Titanic dataset.
- 7 Steps to Understanding NoSQL Databases - Jul 27, 2016.
Are you a newcomer to NoSQL, interested in gaining a real understanding of the technologies and architectures it includes? This post is for you.
- Internet of Things Key Terms, Explained - Jul 27, 2016.
This post will define 12 Key Terms for the Internet of Things, in straightforward manner.
- UF Health Shands Hospital: Decision Support Analyst, Data Warehousing/Reporting Infrastructure - Jul 27, 2016.
Design, develop and maintain the data warehouse and/or semantic reporting layer for UF Health Shands, including delivery of BI information to the entire organization.
- KDnuggets™ News 16:n27, Jul 27: 5 Big Data Projects You Cant Overlook; Pokemon Go and Big Data; SAS vs R vs Python - Jul 27, 2016.
5 Big Data Projects You Can No Longer Overlook; What Has Pokemon Got To Do With Big Data? 10 Great Talks From SciPy 2016; SAS vs R vs Python: Which Tool Do Analytics Pros Prefer?
- Analytical Approaches to Solving Problems in Communications and Media - Jul 26, 2016.
Discover a set of techniques and methodologies to analyze and explore telecommunications data in order to improve business and operational performances. This new course debuts Sep 14 at Analytics Experience 2016 in Las Vegas.
- Boost your Business Analytics Skills - Jul 26, 2016.
Learn the latest business practices, concepts, methodologies and techniques in advanced analytics, data mining, survival analysis, explaining analytics to decision makers, fraud detection, and more with the SAS Business Knowledge Series.
- Predictive Analytics World for Government, Washington, DC, Oct 17-20 - Jul 26, 2016.
PAW Government provides the best information on applying predictive analytics to government with a special track that includes technical training on most relevant tools and concepts. Get extra KDnuggets discount w. code KDN150.
- The Fallacy of Seeing Patterns - Jul 26, 2016.
Analysts are often on the lookout for patterns, often relying on spurious patterns. This post looks at some spurious patterns in univariate, bivariate & multivariate analysis.
- Would You Survive the Titanic? A Guide to Machine Learning in Python Part 2 - Jul 26, 2016.
This is part 2 of a 3 part introductory series on machine learning in Python, using the Titanic dataset.
- Data Science for Beginners 1: The 5 questions data science answers - Jul 26, 2016.
A series of videos and write-ups covering the basics of data science for beginners. This first video is about the kinds of questions that data science can answer.
- Online MS in data science and analytics | Deadline Aug. 6 - Jul 26, 2016.
Sharpen your edge with an online master’s degree in data science and analytics (MS) from the University of Missouri Informatics Institute. Apply today!
- Barley, Hops, and Bayes: Predicting The World Beer Cup - Jul 26, 2016.
This post covers predicting award counts by the United States in an international beer competition. Exploratory data analysis and Bayes methods are also supported.
- Global Big Data Conference, Santa Clara, Aug 30 – Sep 1, 2016 - Jul 25, 2016.
Understand emerging big data trends, develop new technical skills through hands on workshops, analyze multiple industry case studies, learn emerging best practices in big data. Use code KDNUGGETS to save.
- Would You Survive the Titanic? A Guide to Machine Learning in Python Part 1 - Jul 25, 2016.
Check out the first of a 3 part introductory series on machine learning in Python, fueled by the Titanic dataset. This is a great place to start for a machine learning newcomer.
- Why Do Deep Learning Networks Scale? - Jul 25, 2016.
A discussion of what about deep learning architectures allows them to scale, and addresses some assumptions that often inhibit an understanding of this topic.
- DuPont Pioneer: Data Scientist – Encirca. - Jul 25, 2016.
Seeking a strong data scientist with a background in math, statistics, machine learning and scientific computing to join our team. A critical position with the potential to make immediate, significant impact on our business.
- 35 Open Source tools for Internet of Things - Jul 25, 2016.
If you have heard about the Internet of Things many times by now, its time to join the conversation. Explore the many open source tools & projects related to Internet of Things.
- Data Analytics Bootcamp to make you irreplaceable - Jul 25, 2016.
Become irreplaceable at Level Bootcamp by learning how to use data to solve real problems. Get 15% KDnuggets discount for upcoming programs in Boston, Seattle, Charlotte, Silicon Valley, and online.
- Top Stories, July 18-24: Why Big Data is in Trouble; In Deep Learning, Architecture Engineering is the New Feature Engineering - Jul 25, 2016.
Why Big Data is in Trouble: They Forgot About Applied Statistics; In Deep Learning, Architecture Engineering is the New Feature Engineering; 5 Big Data Projects You Can No Longer Overlook; What Has Pokemon Got To Do With Big Data?
- What Has Pokemon Got To Do With Big Data? - Jul 23, 2016.
For me, the millions of people around the world playing Pokémon last weekend (and crashing their servers on a regular basis) showed me a glimpse of the future. There may well be an opportunity for real-time Big Data - I will give you a glimpse.
- Monash University: Applied Data Science Research Position - Jul 23, 2016.
Play a critical role working with the staff and students of the faculty to conduct joint research with external industry and research partners applying advanced data analytics to solve scientific and industrial problems.
- Building a Data Science Portfolio: Machine Learning Project Part 3 - Jul 22, 2016.
The final installment of this comprehensive overview on building an end-to-end data science portfolio project focuses on bringing it all together, and concludes the project quite nicely.
- Machine Learning: Separating Hype From Reality - Jul 22, 2016.
When it comes to business value and ROI, does machine learning live up tot he claims? We’ll explore a pure machine learning approach through the lens of a typical enterprise use case.
- Webinar, July 28: How Open Data Science Can Help Analytics Leaders Survive & Thrive in an Era of Accelerating Technology Disruption - Jul 22, 2016.
Continuum Analytics CTO Peter Wang will show how you, an analytics leader, and your team can continuously leverage the latest innovations in data, analytics and computation by joining the big data party in the Open Data Science tent.
- SAS vs R vs Python: Which Tool Do Analytics Pros Prefer? - Jul 22, 2016.
There are lots of flame wars involving different data science and analytics tools... but this isn't one of them. Check out the quantitative results and analysis of a Burtch Works survey on the subject.
- AT&T: Inventive Scientist. - Jul 22, 2016.
Seeking a PhD level Statistician with a passion for extracting insights from data to join our Statistics Research Department, to work on problems from all aspects of AT&T’s business, including new service creation, network design and optimization, marketing and customer care.
- Improve Your Regression with Modern Regression Analysis Techniques, July 27, Aug 10 Webinars - Jul 22, 2016.
This two part webinar will help you improve your regression using modern regression analysis techniques. July 27 (part 1) and August 10 (part 2).
- Big Data Bootcamp, Boston, Aug 19-21 - Jul 21, 2016.
This is a fast paced, vendor agnostic, technical overview of the Big Data landscape. No prior knowledge of databases or programming is assumed. Use code KDNUGGETS to save - extra discount if you register by July 31.
- An education so adventurous, we wrote a field guide - Jul 21, 2016.
Through our project-based graduate program, you'll get an ethical approach to data science, helping businesses untangle the complexities of data collection and analytics to build a better business and a more equitable society. Now that's a beautiful thing.
- Building a Data Science Portfolio: Machine Learning Project Part 2 - Jul 21, 2016.
The second part of this comprehensive overview on building an end-to-end data science portfolio project concentrates on data exploration and preparation.
- AlertLogic: Marketing Data Scientist - Jul 21, 2016.
Seeking a Marketing Data Scientist to join some of the industry’s brightest minds that are dedicated to providing our customers and partners with security value and outcomes.
- Interesting Things I Learned at SciPy 2016 - Jul 21, 2016.
Learn about some interesting projects featured at SciPy 2016, brought to you by an attendee who put in the work to bring you this great list of projects.
- Introducing Cloud Hosted Deep Learning Models - Jul 21, 2016.
Algorithmia introduces a solution for hosting and distributing locally-trained deep learning models on Algorithmia using GPUs in the cloud, where they become smart API endpoints for other developers to use.
- 5 Big Data Projects You Can No Longer Overlook - Jul 21, 2016.
Check out 5 Big Data projects that you are not likely to have seen before, but which may be useful to you, and perhaps even scratch an itch you didn't know you had.
- Building a Data Science Portfolio: Machine Learning Project Part 1 - Jul 20, 2016.
Dataquest's founder has put together a fantastic resource on building a data science portfolio. This first of three parts lays the groundwork, with subsequent posts over the following 2 days. Very comprehensive!
- Precision for Medicine: Data Scientist - Jul 20, 2016.
Seeking a Data Scientist who will be part of the Corporate Analytics team and work on our Proprietary PATH platform, with R, UI design, web technologies, and AWS experience.
- Top KDnuggets tweets, Jul 13 – Jul 19: Bayesian #MachineLearning, Explained; Introducing JupyterLab - Jul 20, 2016.
Bayesian #MachineLearning, Explained; JupyterLab: the next generation of the #Jupyter Notebook; On the importance of democratizing #ArtificialIntelligence
- Multi-Task Learning in Tensorflow: Part 1 - Jul 20, 2016.
A discussion and step-by-step tutorial on how to use Tensorflow graphs for multi-task learning.
- 10 Great Talks From SciPy 2016 - Jul 20, 2016.
Here's a curated short list of interesting and insightful talks to watch from SciPy 2016 to help guide your search through the volume of great video material emerging from the conference.
- KDnuggets™ News 16:n26, Jul 20: Bayesian Machine Learning, Explained; Start Learning Deep Learning; Big Data is in Trouble - Jul 20, 2016.
Bayesian Machine Learning, Explained; How to Start Learning Deep Learning; Why Big Data is in Trouble: They Forgot About Applied Statistics; Data Mining/Data Science "Nobel Prize": 2016 SIGKDD Innovation Award to Philip S. Yu
- In Deep Learning, Architecture Engineering is the New Feature Engineering - Jul 19, 2016.
A discussion of architecture engineering in deep neural networks, and its relationship with feature engineering.
- What the Next Generation of IoT Sensors Have in Store - Jul 19, 2016.
This post is an overview of some of the next-generation IoT sensors, and what they could mean for our future.
- MNIST Generative Adversarial Model in Keras - Jul 19, 2016.
This post discusses and demonstrates the implementation of a generative adversarial network in Keras, using the MNIST dataset.
- Online Master of Science in Predictive Analytics - Jul 19, 2016.
Build in-demand skills for the growing analytics field with the Northwestern University Master of Science in Predictive Analytics degree, completely online.
- Statistical Data Analysis in Python - Jul 18, 2016.
This tutorial will introduce the use of Python for statistical data analysis, using data stored as Pandas DataFrame objects, taking the form of a set of IPython notebooks.
- Why Big Data is in Trouble: They Forgot About Applied Statistics - Jul 18, 2016.
This "classic" (but very topical and certainly relevant) post discusses issues that Big Data can face when it forgets, or ignores, applied statistics. As great of a discussion today as it was 2 years ago.
- Predictive Analytics Introductory Key Terms, Explained - Jul 18, 2016.
Here is a collection of introductory predictive analytics terms and concepts, presented for the newcomer in a straight-forward, no frills definition style.
- O’Reilly AI: Last chance to get Best Price - Jul 18, 2016.
This week is your last chance to get the Best Price for the O'Reilly Artificial Intelligence Conference happening in New York September 26-27. Register with your KDnuggets discount code now!
- Top Stories, July 11–17: Top Machine Learning MOOCs and Online Lectures; Bayesian Machine Learning, Explained - Jul 18, 2016.
Top Machine Learning MOOCs and Online Lectures; Bayesian Machine Learning, Explained; 10 Algorithm Categories for A.I., Big Data, and Data Science; 5 Deep Learning Projects You Can No Longer Overlook; The Hard Problems AI Can't (Yet) Touch
- KDnuggets Interview: Inderpal Bhandari, IBM Global Chief Data Officer on 4 key ideas of Cognitive Computing - Jul 17, 2016.
In this wide-ranging interview, we discuss the role of IBM global chief data officer, 4 key ideas of cognitive computing, risks of AI, IBM Data Science Experience, healthcare, basketball, sports analytics, and more.
- America’s Next Topic Model - Jul 15, 2016.
Topic modeling is a a great way to get a bird's eye view on a large document collection using machine learning. Here are 3 ways to use open source Python tool Gensim to choose the best topic model.
- Data Mining Most Vexing Problem Solved, or is this drug REALLY working? - Jul 15, 2016.
This is a summary of the basic principle behind a new paper on multiple test correction for streams and cascades of statistical hypothesis tests, showing how to strictly control the risk of making a mistake over a series of tests and draw appropriate conclusions.
- 4 Major Trends Disrupting the Data Science Market - Jul 15, 2016.
An interesting excerpt from Burtch Works' recently published Burtch Works Study: Salaries of Data Scientists 2016, focusing on trends disrupting the data science market.
- 2016’s Best Places for Data Scientist Jobs - Jul 15, 2016.
Get the info on the Best Places in the U.S. for Data Scientist Jobs with GoodCall's new data-driven report.
- Data Mining/Data Science “Nobel Prize”: 2016 SIGKDD Innovation Award to Philip S. Yu - Jul 15, 2016.
Dr. Philip S. Yu wins ACM KDD Innovation Award for his influential research and scientific contributions on mining, fusion and anonymization of big data.
- 10 Algorithm Categories for A.I., Big Data, and Data Science - Jul 14, 2016.
With a focus on leveraging algorithms and balancing human and AI capital, here are the top 10 algorithm categories used to implement A.I., Big Data, and Data Science.
- How to Start Learning Deep Learning - Jul 14, 2016.
Want to get started learning deep learning? Sure you do! Check out this great overview, advice, and list of resources.
- What Data Scientists Can Learn From Qualitative Research - Jul 14, 2016.
Learn what data scientists can learn from qualitative researchers when it comes to analysing text, and how this relates to writing quality code.
- Online Courses: Big Data Projects and Data Science Pipelines - Jul 14, 2016.
Check out these online courses from O'Reilly Media on managing big data projects and building distributed data pipelines.
- 2016 SIGKDD Service Award to Wei Wang - Jul 14, 2016.
Prof. Wei Wang wins ACM SIGKDD 2016 Service Award for her significant technical contributions to the principles, practice and application of data mining and for her outstanding services to society and the data mining community.
- Take a Risk Free Hadoop Ride. Save up to 80% cost and offload time. - Jul 14, 2016.
The Impetus Data Warehouse Workload Migration product is a proven, cost-effective, and low-risk solution to offload traditional data warehouse to Big Data warehouse. Contact us for a proof-of-concept.
- Top KDnuggets tweets, Jul 6 – Jul 12: Statistical Data Analysis #Python #Jupyter Notebooks; Modern Pandas Notebooks - Jul 13, 2016.
Statistical Data Analysis in #Python (#Jupyter Notebooks); Modern Pandas: idiomatic Pandas notebook collection; New (free) book by @rdpeng: #rstats Programming for #DataScience
- Metis Data Science Open Houses: San Francisco and New York City - Jul 13, 2016.
Visit Metis in San Francisco (July 14) and New York City (July 20) to learn about their 12-week data science bootcamps.
- What do Postgres, Kafka, and Bitcoin Have in Common? - Jul 13, 2016.
These three technologies on the surface couldn't look any more different, but under the hood they have one interesting thing in common.
- Bayesian Machine Learning, Explained - Jul 13, 2016.
Want to know about Bayesian machine learning? Sure you do! Get a great introductory explanation here, as well as suggestions where to go for further study.
- Explore your unstructured text data - Jul 13, 2016.
Learn examples of success with text exploration, what engineers and scientists can (and should) do with text data, and the consequences of collecting data and doing nothing with it.
- KDnuggets™ News 16:n25, Jul 13: Top Machine Learning MOOCs; 5 Deep Learning Projects; Support Vector Machines Overview - Jul 13, 2016.
Top Machine Learning MOOCs and Online Lectures: A Comprehensive Overview; Support Vector Machines: A Simple Explanation; 5 Deep Learning Projects You Can No Longer Overlook; Why You Should Attend the Data Science Summit 2016 and 9 Talks To Be Excited About
- Axa: Data Scientist - Jul 12, 2016.
Axa is seeking a Data Scientist to be involved in scoping requirements for our analysis platform, and then using it to deliver a market leading pricing initiative, working on all areas of a data science project life cycle.
- A Survey of Available Corpora for Building Data-driven Dialogue Systems - Jul 12, 2016.
This post is a summary of Serban, et al. "A Survey of Available Corpora for Building Data-Driven Dialogue Systems," which is of increasing relevance given the recent state of conversational AI.
- TMA Predictive Analytics and Data Mining Training – Live Online, August - Jul 12, 2016.
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 online in August.
- Semi-supervised Feature Transfer: The Practical Benefit of Deep Learning Today? - Jul 12, 2016.
This post evaluates four different strategies for solving a problem with machine learning, where customized models built from semi-supervised "deep" features using transfer learning outperform models built from scratch, and rival state-of-the-art methods.
- Webinar: Predictive Analytics: Failure to Launch [July 14] - Jul 12, 2016.
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 July 14.
- Using Big Data and Predictive Analytics to Reach 63% Growth – case study - Jul 12, 2016.
Join The Big Data Channel and Innovation Enterprise for three summits September 8 & 9 in Boston, where KDnuggets readers get a 10% discount. Register now!
- TalkingData Data Science Competition: understand mobile users - Jul 12, 2016.
Unique opportunity to solve complex real world big data challenges for the China mobile market - predict users demographic characteristics based on their app usage, geolocation, and mobile device properties.
- 5 Deep Learning Projects You Can No Longer Overlook - Jul 12, 2016.
There are a number of "mainstream" deep learning projects out there, but many more niche projects flying under the radar. Have a look at 5 such projects worth checking out.
- Top Stories, July 4–10: The Invention of Support Vector Machines; Storytelling: The Power to Influence in Data Science - Jul 11, 2016.
Data Mining History: The Invention of Support Vector Machines; Storytelling: The Power to Influence in Data Science; Support Vector Machines: A Simple Explanation; Big Data, Bible Codes, and Bonferroni
- The Hard Problems AI Can’t (Yet) Touch - Jul 11, 2016.
It's tempting to consider the progress of AI as though it were a single monolithic entity, advancing towards human intelligence on all fronts. But today's machine learning only addresses problems with simple, easily quantified objectives
- Top Machine Learning MOOCs and Online Lectures: A Comprehensive Survey - Jul 11, 2016.
This post reviews Machine Learning MOOCs and online lectures for both the novice and expert audience.