Nov
2015 Dec Opinions, Interviews, Reports
All (95) | Courses, Education (3) | Meetings (6) | News, Features (17) | Opinions, Interviews, Reports (37) | Publications (2) | Software (9) | Top Tweets (3) | Tutorials, Overviews, How-Tos (12) | Webcasts (6)
- How Big Data and Predictive Analytics can help manage climate change
- Dec 31, 2015.
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.
- 2016: The Year of Hadooplooza
- Dec 31, 2015.
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.
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TensorFlow is Terrific – A Sober Take on Deep Learning Acceleration
- Dec 30, 2015.
TensorFlow does not change the world. But it appears to be the best, most convenient deep learning library out there. - The Unsung Hero – The Data Scientist
- Dec 28, 2015.
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.
- The Star Wars social networks – who is the central character?
- Dec 25, 2015.
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 Distance approach for translating Predictive Models to Actions
- Dec 24, 2015.
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 2 Million Machine Learning Models on Kaggle
- Dec 24, 2015.
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.
- More Data Science Humor and Cartoons
- Dec 23, 2015.
More humor and cartoons from Andrii aka San Sanych, #HappyDataScientist.
- Data Science and Prejudice – Blessing or Curse ?
- Dec 23, 2015.
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?
- The future of analytics – top 5 predictions for 2016
- Dec 23, 2015.
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.
- Tamr 2016 Data Management Predictions
- Dec 22, 2015.
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.
- 8 Myths about Virtualizing Hadoop on vSphere Explained
- Dec 22, 2015.
This article takes some common misperceptions about virtualizing Hadoop and explains why they are errors in people’s understanding.
- 5 Criteria To Determine If Your Data Is Ready For Serious Data Science
- Dec 21, 2015.
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.
- OpenText Data Digest, Dec 18: The Data Awakens
- Dec 21, 2015.
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!
- 10 Business Intelligence Trends for 2016
- Dec 19, 2015.
BI analysts, industry players predict the rise of self-service, Big Data analytics, real-time data in the coming year.
- International Institute of Analytics (IIA) 5 Predictions and 5 Priorities for 2016
- Dec 18, 2015.
IIA 2016 Predictions include: Cognitive technology becomes the follow-on to automated analytics; Analytical Microservices facilitate embedded analytics; and analytics talent crunch eases.
- Vincent Granville Predictions for Data Science in 2016 and Beyond
- Dec 18, 2015.
Data science and statistical modeling will be further automated; frontiers between data science, operations research, Machine Learning will disappear, and more.
- Key Benefits of Heterogeneous Analytics Compared to Traditional BI
- Dec 18, 2015.
This blog examines why traditional BI is becoming increasingly ineffective and how heterogeneous analytics can solve business problems with analytics.
- How ‘Insights-as-a-service’ is growing based on big data
- Dec 16, 2015.
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.
- OpenText Data Digest, Dec 11: Holiday Lights
- Dec 16, 2015.
Christmas and the New Year are approaching, so this week we’re sharing some data visualizations with connections to holiday celebrations.
- Strata + Hadoop World 2015 Singapore – Day 2 Highlights
- Dec 15, 2015.
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.
- Importance of Data Science for IoT business
- Dec 14, 2015.
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.
- Augmented Intelligence – An Elegant Approach to Optimize Your Decision Making Process
- Dec 14, 2015.
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.
- Big Data and Data Science for Security and Fraud Detection
- Dec 11, 2015.
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.
- Strata + Hadoop World 2015 Singapore – Day 1 Highlights
- Dec 11, 2015.
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.
- 22 Big Data & Data Science experts predictions for 2016
- Dec 11, 2015.
Will machines become smarter than man? What technology will dominate Data Science? What is smart data? Read Big Data experts predictions for 2016.
- Top 5 Big Data / Machine Learning Podcasts
- Dec 10, 2015.
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.
- Which Database is best for an Analyst?
- Dec 10, 2015.
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.
- OpenText Data Digest Dec 4: Data Is Beautiful
- Dec 9, 2015.
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.
- Learning from Hurricanes: Big Data Analytics, Risk, & Data Visualization
- Dec 8, 2015.
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.
- 20 Lessons From Building Machine Learning Systems
- Dec 8, 2015.
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.
- Deep Learning Transcends the Bag of Words
- Dec 7, 2015.
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.
- Uber-fication: Lessons from Uber in Economics, Digital, Risk, and Analytics
- Dec 5, 2015.
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.
- OpenText Data Digest Nov 27: Data Mapping Music
- Dec 4, 2015.
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.
- Big Data is Crowdledge, not 3V
- Dec 2, 2015.
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.
- Can Big Data Catch the Bad Guys?
- Dec 2, 2015.
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.
- Deep Forger: Art Forgery Meets Deep Neural Nets
- Dec 1, 2015.
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.