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.
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?
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.
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.
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.
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.
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.
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;
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
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.