We review how Big data and data science can provide accurate analytics to help deal with climate change with tools like Global Forest Watch, Microsoft Research’s Madingley Model, and the Google Earth Engine.
Bruno Aziza examines the Hadoopalooza effect, how to avoid poor decisions to come back from the party a "Hadoop-loser", and what is needed to get value from data lakes.
Data science is not only about building the models and sharing insights, many times they have to collaborate in deploying models and sharing them with software developers, learn which things to keep in mind while doing so.
"Learn #Python" Overtakes "Learn #Java" on Google Trends ; R is the fastest-growing language on StackOverflow; More #DataScience #Humor and #Cartoons; The Star Wars Social Network - who is the central character?
Learn, how to turn the deluge of data into the gold by algorithms, feature engineering, reasoning out business value and ultimately building a data driven organization.
The data scientist is not a magician to single-handedly solve all of the challenges an organisation may face. It is through the ability to change culture, behaviour and expectation that data science truly achieves its potential.
Top 10 Machine Learning Projects on Github; 10 BI Trends for 2016; More Data Science Humor and Cartoons; Lessons from 2 MM Machine Learning Models on Kaggle.
Academic/Research position at U. Libre Brussels, Karlsruhe Inst. for Technology, U. of Iowa Tippie College of Business, Monash University, U. Strasbourg, Texas A&M, Roche (Basel) and INESC TEC (Porto).
Data Scientist looks at the 6 Star Wars movies to extract the social networks, within each film and across the whole Star Wars universe. Network structure reveals some surprising differences between the movies, and finds who is actually the central character.
Kanri proprietary combination of patented statistical and process methods provides a uniquely powerful and insightful ability to evaluate large data sets with multiple variables.
Lessons from Kaggle competitions, including why XG Boosting is the top method for structured problems, Neural Networks and deep learning dominate unstructured problems (visuals, text, sound), and 2 types of problems for which Kaggle is suitable.
We examine the deep nature of bias and prejudice and wonder if prejudiced minds and 'good' data scientists coexist in harmony and if they can coexist, does it lead to disruption or disruptive innovation?
Analytics has never been more needed or interesting and the future looks exciting. Top 2016 trends include Machine learning established in the enterprise, Internet of Things hype hits reality, and Big data moves beyond hype to enrich modeling.
2016 predictions from Tamr team, which includes Turing Award winner Mike Stonebraker and some of the most forward-thinking experts from the world of Big Data.
Top 10 #MachineLearning Algorithms, updated; Cartoon: Surprise #DataScience #Recommendations; DeepLearning in a Nutshell: History and Training; Update: Google #TensorFlow #DeepLearning Is Improving.
New KDnuggets Poll is asking - where did you apply Analytics, Data Mining, Data Science in 2015? Please vote and we will analyze the results and report on trends and interesting findings.
If your data is a large, relevant, accurate, connected, and you also have a sharp question, you ready to do some serious data science. If you’re weak on 1-2 points, don’t worry. But if most criteria are not true, you need to do more preparation.
As the world enjoys the latest instalment of the Star Wars series, we review interesting visualizations based on the movie series. Strong is the data behind the Force. Enjoy!
Natural language processing (NLP) helps computers understand human speech and language. We define the key NLP concepts and explain how it fits in the bigger picture of Artificial Intelligence.
Top 10 Machine Learning Projects on Github; Using Python and R together: main approaches; Importance of Data Science for IoT business; Top 10 Deep Learning Tips, Tricks.
Data science and statistical modeling will be further automated; frontiers between data science, operations research, Machine Learning will disappear, and more.
Taken from the answers experts gave, here is a compiled list of 5 essential actions and attitudes that keep data scientists learning after their degrees.
Join TDWI biggest event of the year featuring four in-depth learning experiences: Leadership and Innovation, Analytics, Big Data, and Data Management, Jan 31-Feb 5 in Las Vegas. Use code KD16 to save.
The recent open sourcing of Google's TensorFlow was a significant event for machine learning. While the original release was lacking in some ways, development continues and improvements are already being made.
Insights-as-service should deliver not only actionable insights, but also a concrete plan to use them. We review different types of insights as a service, how they are used with big data, deployment challenges, and future trends.
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 Jan 14.
Here are the quick takeaways and valuable insights from selected talks at one of the most reputed conferences in Big Data – Strata + Hadoop World 2015, Singapore, day 2.
We present the popular software & toolkit resources for Deep Learning, including Caffe, Cuda-convnet, Deeplearning4j, Pylearn2, Theano, and Torch. Explore the new list!
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.
R Programming: 35 Job #Interview Questions and Answers; A Look into #MachineLearning First Cheating #Scandal; The current state of #machine #intelligence 2.0 ; #Dilbert Dark #humor on combining #DNA tests and #Bevaviour #Predictions;
The top 10 machine learning projects on Github include a number of libraries, frameworks, and education resources. Have a look at the tools others are using, and the resources they are learning from.
Here, we have explored how IoT businesses can leverage data science for IT strategies, service analysis stack, capacity planning, hardware maintenance, competitive advantages and anomaly detection. Along with, the different application in multiple IoT industries.
Automation is a rising trend in the recent technology boom, but it can impose a level or risk. Harmonizing both human decision and powerful computing abilities will be key, especially for enterprises looking to unlock insights through analytics.
50 Useful Machine Learning, Prediction APIs; TensorFlow Google Deep Learning Disappoints; 7 Essential Resources, Tips To Get Started With Data Science; 20 Lessons From Building Machine Learning Systems
We review big data analytics tools and technologies that combine text mining, machine learning and network analysis for security threat prediction, detection and prevention at an early stage.
Here are the quick takeaways and valuable insights from selected talks at one of the most reputed conferences in Big Data – Strata + Hadoop World 2015, Singapore.
New KDnuggets Cartoon looks at the recent progress of machine learning algorithms reaching and exceeding human performance levels and what will they achieve next...
Give (or get) the gift of Analytics - super early bird rates expire on Dec 18 for three Predictive Analytics World conferences in San Francisco in April 2016.
Build in-demand skills for the growing analytics field, prepare for leadership career, and learn from top faculty and industry experts. Spring application deadline Jan 15.
The main technical advantage of Orange 3 is its integration with NumPy and SciPy libraries. Other improvements include reading online data, working through queries for SQL and pre-processing.
Well if Data Science and Data Scientists can not decide on what data to choose to help them decide which language to use, here is an article to use BOTH.
Podcasts are probably one of the most underrated forms of communication, especially given that, for the most part, they are free. Here we have collected best of big data and machine learning podcasts.
Database comparisons usually look at architecture, cost, scalability, and speed, but rarely address the other key factor: how hard is writing queries for these databases? We examine which of the top 8 databases are easiest to use.
This week we look at the 2015 winners of the “Information Is Beautiful” Awards, including Red vs Blue politics, a World of languages, and Working for a living.
How can we predict something we have never seen, an event that is not in the historical data? This requires a shift in the analytics perspective! Understand how to standardization the time and perform time series analysis on sensory data.
This instructional post takes you through connecting the various pieces when studying the data science pipeline. From analysis, to datasets, to MOOCs, to visualizing data, this informative post has some fresh insight.
New DocAndys SaaS service supports user-created embeddable Fuzzy Logic Expert Systems. Use rule language Darl to hand-create or machine-learn rule sets from data and use them via REST interfaces.
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.
It's that time of the year where we are happy to offer a gift to KDnuggets readers for our first analytics conferences in the new year. Join us with code KDN150 for an additional $150 off of Super Early Bird rates.
TensorFlow Disappoints - Google Deep Learning falls shallow; 5 Best Machine Learning APIs for Data Science; 7 Steps to Mastering Machine Learning With Python; A Statistical View of Deep Learning.
This year, Florida has experienced its 10th consecutive year without a hurricane, which is longest period without a hurricane strike in modern times. Exploring this is worthy of some examination, as it offers us many lessons in Big Data Analytics, Risk, and Data Visualization.
Coding categorical variables into numbers, by assign an integer to each category ordinal coding of the machine learning algorithms. Here, we explore different ways of converting a categorical variable and their effects on the dimensionality of data.
Check upcoming Rising Media conferences on predictive analytics (in business, workforce, manufacturing, financial services, and healthcare), data science, big data, digital analytics, text analytics, and more. Use KDN150 for extra savings.
Data science is not only a scientific field, but also it requires the art and innovation from time to time. Here, we have compiled wisdom learned from developing data science products for over a decade by Xavier Amatriain.
The Balkanization of #DataScience, #BigData: will it lead to empire or republics;7 Essential Resources, Tips To Get Started With Data Science ; Data Science #Cartoon Contest has 3 winners.
We present a list of 50 APIs selected from areas like machine learning, prediction, text analytics & classification, face recognition, language translation etc. Start consuming APIs!
Generative RNNs are now widely popular, many modeling text at the character level and typically using unsupervised approach. Here we show how to generate contextually relevant sentences and explain recent work that does it successfully.
Learn how to use Bokeh interactive visualization framework for open data science to create rich, interactive visualizations in the browser, without writing a line of JavaScript, HTML, or CSS.
7 Steps to Mastering Machine Learning With Python; TensorFlow Disappoints - Google Deep Learning falls shallow; Will Balkanization of Data Science lead to one Empire or many Republics?; 5 Tribes of Machine Learning - Questions and Answers.
22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining invites submissions for research papers, applied data science papers, proposals for workshops and tutorials, and nominations and ideas for the prestigious KDD CUP contest. See deadlines and details.
Uber-fication or Uberisation is the conversion of existing jobs and services into discrete tasks that can be requested on-demand; the emulation or adoption of the Uber’s business model. Here we have discussed opportunities, risk and challenges while doing uberisation.
Coming soon: Harrisburg U. Data Analytics Summit, EGC France, #BALasVegas, #DataLasVegas, BIWA Summit 16, Rework Deep Learning Summit, WSDM 2016, JMP Discovery Summit Amsterdam, and more.
Learn how to use Predictive Analytics and Hadoop to Turn the Promise of Big Data into Business Impact in this webinar with RapidMiner Founder and CTO Ingo Mierswa and leading Gartner Analyst Merv Adrian.
Training deep neural nets can take precious time and resources. By leveraging an existing distributed batch processing framework, SparkNet can train neural nets quickly and efficiently.
For this week, we present some examples of how to display music visually, which may get you thinking of other creative ways to visualize data and bring patterns to the surface.
Get technical primer with best practices to interactively explore the patterns in your data, build useful statistical models of these patterns, and visually interact with these models.
Stay ahead of the curve in business analytics with our Dec 10 Webinar Marathon, and watch industry experts deliver 7 back-to-back sessions on hot topics. Register now.
Sentiment analysis can be incredibly useful, and can help companies better answer pertinent questions and gain valuable business insights. Sentiment analysis technologies will continue to improve as they become more widely adopted. But what can sentiment analysis do for you?
November on /r/DataScience: Plot.ly is open sourced, Pokemon and Big Data games, a new social network analysis package for R, insider information on landing a Google Data Scientist job, and a free data science curriculum.
Neural networks are generating a lot of excitement, while simultaneously posing challenges to people trying to understand how they work. Visualize how neural nets work from the experience of implementing a real world project.
In November on /r/MachineLearning, we've got a good laugh, a fantastic image-generating convolutional generative adversarial network, and a whole lot of Google TensorFlow.
Crowdledge is defined as the knowledge that [weakly] emerges – and is, therefore, unexpected – from Big Data analysis of individuals’ digital footprints spontaneously left in the digital universe. It represents big data in better terms than 3Vs.
Get this white paper to explore future- proof strategies to leverage the steady flow of new, advanced real-time streaming analytics (RTSA) application development technologies.
Balancing individual liberties with Big Brother surveillance and intelligence-gathering methods means walking a fine line that will require proper balancing for the foreseeable future. Regardless of opinion, Big Data has some role to play in keeping us safe, and the sooner we recognize it the better.
Looker in-database approach to enterprise analytics allows organizations to see performance improvements by leveraging centralized data in high performance databases such as HP Vertica or Amazon Redshift.
The past year has seen deep learning make exceptional advances in imaging, perhaps most notably with Google's Deep Dream. See how a clever Twitter bot employs deep neural nets to paint images in the style of famous painters.
Learn valuable tips to help optimize Big Data for agility and speed to insight; improve data accessibility, without the limitations of data warehouses, and prevent data sources from becoming data silos.
TensorFlow is Terrific – A Sober Take on Deep Learning Acceleration
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