Feb
2015 Mar Opinions, Interviews, Reports
All (116) | Courses, Education (9) | Meetings (14) | News, Features (22) | Opinions, Interviews, Reports (47) | Publications (7) | Software (2) | Top Tweets (8) | Webcasts (7)
- Interview: Satyam Priyadarshy, Halliburton on Unlocking Success for Big Data Projects
- Mar 31, 2015.
We discuss Predictive Analytics in Oil & Gas industry, Big Data analytics, key drivers of success,common reasons of failure, trends, advice, and more.
- How Big Data Can Improve the Lives of the Poor
- Mar 31, 2015.
The role of Big Data in allowing greater financial inclusion for the poor also is a trending Internet topic. But it’s mostly creating optimism and interest, rather than controversy and dissent.
- Interview: Satyam Priyadarshy, Halliburton on Big Data Challenges in Oil & Gas Industry
- Mar 30, 2015.
We discuss Analytics at Halliburton, Big Data challenges unique to Oil & Gas industry, and the 7 V’s of Big Data.
- Data Science as a profession – time is now
- Mar 30, 2015.
Now is the time to begin thinking of Data Science as a profession not a job, as a corporate culture not a corporate agenda, as a strategy not a stratagem, as a core competency not a course, and as a way of doing things not a thing to do.
- Text Analytics 2015 – Technology and Market Overview
- Mar 30, 2015.
A leading analyst and expert on text analytics gives an overview of the past year and looks ahead on text analytics technology and market developments.
- Interview: Bill Moreau, USOC on the Pursuit of a Career in Sports Analytics
- Mar 28, 2015.
We discuss challenges in applying Data Analytics to sports, advice to beginners in the field of Sports Analytics, and more.
- The Grammar of Data Science: Python vs R
- Mar 28, 2015.
In this post, I will elaborate on my experience switching teams by comparing and contrasting R and Python solutions to some simple data exploration exercises.
- Interview: Bill Moreau, USOC on Evidence-based Medicine to Reduce Sports Injuries
- Mar 27, 2015.
We discuss the success of Analytics in predicting sports injuries, recent progress in concussion management and the trends in data-driven evidence-based sports medicine.
- PredictionIO (Open Source Version) vs Microsoft Azure Machine Learning
- Mar 26, 2015.
Azure Machine Learning and PredictionIO are tools that both have similar visions and similar features, but when digging deeper you’ll notice key differences and key advantages to each.
- Interview: Bill Moreau, USOC on Empowering World’s Best Athletes through Analytics
- Mar 26, 2015.
We discuss how United States Olympic Committee uses Big Data, how athletes respond to Analytical insights, integration of sports medicine into sports performance and sports injury.
- Talking Machine – 3 Deep Learning Gurus Talk about History and Future, part 2
- Mar 26, 2015.
Key ideas from a podcast with Deep Learning gurus Geoff Hinton, Yoshua Bengio, and Yann LeCun, where they explain the power of distributed representation and also propose a new open paper review process.
- Talking Machine – 3 Deep Learning Gurus Talk about History and Future of Machine Learning, part 1
- Mar 25, 2015.
An recent interview from the talking machine podcast with three deep learning experts. They talked about the neural network winter and its renewal.
- Interview: Beena Ammanath, GE on Data Science – It’s Not Just Science!
- Mar 24, 2015.
We discuss benefits and challenges of Data Lake, trends, life lessons, motivation, desired skills, and more.
- Data science done well looks easy, which is a big problem
- Mar 24, 2015.
Data Science done well looks too easy and that poses a major public relations problem for serious data scientists. The really tricky twist is that bad data science looks easy too.
- Interview: Beena Ammanath, GE on the Industrial Internet for Data-driven Innovation
- Mar 23, 2015.
We discuss the role of Analytics at GE, Industrial Internet and how it is different from consumer internet, and the key capabilities of Predix.
- Do We Need More Training Data or More Complex Models?
- Mar 23, 2015.
Do we need more training data? Which models will suffer from performance saturation as data grows large? Do we need larger models or more complicated models, and what is the difference?
- Top 10 UK Big Data Professionals
- Mar 23, 2015.
The top 10 Big Data Professionals in the UK include CEOs, journalists, an Information Commissioner, and Analytics leaders from leading companies and organizations.
- Interview: Brad Klingenberg, StitchFix on Decoding Fashion through Analytics and ML
- Mar 21, 2015.
We discuss the challenges in making personal styling recommendations, unexpected insights, interesting trends, motivation, advice, desired qualities in data scientists and more.
- Interview: Brad Klingenberg, StitchFix on Building Analytics-powered Personal Stylist
- Mar 20, 2015.
We discuss StitchFix, how it leverages Analytics, understanding customer preferences, and pros-and-cons of involving human judgement in the recommendation process.
- Small Data requires Specialized Deep Learning and Yann LeCun response
- Mar 19, 2015.
For industries that have relatively small data sets (less than a petabyte), a Specialized Deep Learning approach based on unsupervised learning and domain knowledge is needed.
- Interview: Vince Darley, King.com on What do you need to become Top Grossing Game
- Mar 19, 2015.
We discuss common characteristics of games that achieved top ranking, career advice, trends, desired qualities in data scientists and more.
- Interview: Vince Darley, King.com on the Serious Analytics behind Casual Gaming
- Mar 18, 2015.
We discuss key characteristics of social gaming data, ML use cases at King, infrastructure challenges, major problems with A-B testing and recommendations to resolve them.
- Interview: Dave McCrory, Basho on Why Data Gravity Cannot be Ignored in Architecture Design
- Mar 17, 2015.
We discuss data gravity and its implications, Riak Enterprise 2.0, Riak CS 1.5, competitive landscape, challenges and more.
- Interview: Dave McCrory, Basho on Distributed Database Needs of a Future Enterprise
- Mar 16, 2015.
We discuss the future of distributed storage for enterprise, Scale-up vs. Scale-out, software design patterns in Cloud era, microservices model and the place for legacy database in modern enterprise IT.
- Interview: Kenneth Viciana, Equifax on Data Governance – Red Tape or Catalyst?
- Mar 14, 2015.
We discuss recommendations for Data Governance policies, advice, Big Data trends, qualities sought in Data Scientists, and more.
- Report – MLconf: what industry leaders say about machine learning
- Mar 14, 2015.
MLconf hosted in 4 different cities, NYC, Seattle, Atlanta and San Francisco with speakers from big, established companies and from emerging startups, bringing more ideas and experience into the game.
- Interview: Kenneth Viciana, Equifax on Data Lake & Other Strategies for Insights Culture
- Mar 13, 2015.
We discuss the responsibilities of Enterprise Data Strategy team at Equifax, why Data Lake, Equifax Decision360, how to set up Insights Culture and bottlenecks for value delivery from Big Data.
- Deep Learning for Text Understanding from Scratch
- Mar 13, 2015.
Forget about the meaning of words, forget about grammar, forget about syntax, forget even the very concept of a word. Now let the machine learn everything by itself.
- Interview: Josh Hemann, Activision on Why the Tolerance for Ambiguity is Vital
- Mar 12, 2015.
We discuss handling bias in data, other data quality concerns, advice, desired qualities, and more.
- Deep Learning, The Curse of Dimensionality, and Autoencoders
- Mar 12, 2015.
Autoencoders are an extremely exciting new approach to unsupervised learning and for many machine learning tasks they have already surpassed the decades of progress made by researchers handpicking features.
- SQL-like Query Language for Real-time Streaming Analytics
- Mar 12, 2015.
We need SQL like query language for Realtime Streaming Analytics to be expressive, short, fast, define core operations that cover 90% of problems, and to be easy to follow and learn.
- Interview: Josh Hemann, Activision on Taming the Beast of Gaming Big Data
- Mar 11, 2015.
We discuss Analytics challenges at Activision, event data from games such as Call of Duty, balancing aesthetics and inference in visualization, problem with stacked charts and more.
- 10 Steps to Success in Kaggle Data Science Competitions
- Mar 11, 2015.
The author, ranked in top 10 in five Kaggle competitions, shares his 10 steps for success. These also apply to any well-defined predictive analytics or modeling problem with a closed dataset.
- Interview: Slava Akmaev, Berg on Challenges in Transitioning Analytics to Clinical Utility
- Mar 10, 2015.
We discuss Analytics use cases, challenges in relating molecular/clinical data to real-life outcomes, Healthcare Analytics trends and more.
- Strata + Hadoop World 2015 San Jose – Day 2 Highlights
- Mar 10, 2015.
Strata + Hadoop World 2015 was a great conference, and here are key insights from some of the best sessions on day 2.
- Interview: Slava Akmaev, Berg on Healthcare Transparency & Effectiveness using Big Data
- Mar 9, 2015.
We discuss Big Data Analytics at Berg, making Healthcare effective through Big Data, impact of falling cost of DNA sequencing, Berg AI-Analytics Suite and more.
- Juergen Schmidhuber AMA: The Principles of Intelligence and Machine Learning
- Mar 9, 2015.
Jürgen Schmidhuber, pioneer in innovating Deep Neural Networks, answers questions on open code, general problem solvers, quantum computing, PhD students, online courses, and the neural network research community in this Reddit AMA.
- 7 common mistakes when doing Machine Learning
- Mar 7, 2015.
In statistical modeling, there are various algorithms to build a classifier, and each algorithm makes a different set of assumptions about the data. For Big Data, it pays off to analyze the data upfront and then design the modeling pipeline accordingly.
- Interview: Lei Shi, ChinaHR.com on Unraveling Insights from Unstructured Data
- Mar 7, 2015.
We discuss challenges in leveraging Big Data, important attributes while profiling employers and job seekers, competitive landscape, desired skills in data scientists and more.
- Interview: Lei Shi, ChinaHR.com on Analytics behind the Perfect Match
- Mar 6, 2015.
We discuss analytics at ChinaHR, matching job seekers and employers, traditional job fairs vs online recruitment, key metrics and analytical insights.
- Interview: Kaiser Fung, NYU on Why Statistical Reasoning is more important than Number Crunching
- Mar 5, 2015.
We discuss why every individual should care about statistics, inspiration behind the book Numbersense, teaching statistics as liberal arts, Junk Charts blog, advice and more.
- Interview: Kaiser Fung, NYU on Why Ignoring Data Integrity is a Recipe for Disaster
- Mar 4, 2015.
We discuss different levels of Data Integrity, logical fallacies in Analytics, measures to boost accountability, role for human intelligence in Analytics and relevance of OCCAM framework.
- Failing Optimally – Data Science’s Measurement Problem
- Mar 4, 2015.
Data science has a measurement problem. Simple metrics may not address complex situations. But complex metrics present myriad problems.
- Interview: Ted Dunning, MapR on Apache Mahout & Technology Landscape in ML
- Mar 3, 2015.
We discuss Apache Mahout, its comparison with Spark and H2O, trends, advice, desired qualities in data scientists and more.
- All Machine Learning Models Have Flaws
- Mar 3, 2015.
This classic post examines what is right and wrong with different models of machine learning, including Bayesian learning, Graphical Models, Convex Loss Optimization, Statistical Learning, and more.
- Interview: Ted Dunning, MapR on The Real Meaning of Real-Time in Big Data
- Mar 2, 2015.
We discuss major Big Data developments in 2014, real-time processing, interactive queries, streaming systems, batch systems, MapR partnerships and challenges in scaling recommendation engines.
- Strata + Hadoop World 2015 San Jose – Day 1 Highlights
- Mar 2, 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, San Jose.
Automating Every Aspect of Your Python Project
Autograd: The Best Machine Learning Library You’re Not Using?