Dec
2015 Jan Opinions, Interviews, Reports
All (120) | Courses, Education (14) | Meetings (14) | News, Features (21) | Opinions, Interviews, Reports (46) | Publications (2) | Software (4) | Top Tweets (13) | Webcasts (6)
- Big Data Could Revolutionize Healthcare. Will We Let it?
- Jan 31, 2015.
The power to access and analyze enormous data sets can improve our ability to anticipate and treat illnesses. The benefits for society are just too great, and they won’t be ignored for long.
- Interview: Anthony Bak, Ayasdi on How to Get Started on Topology
- Jan 30, 2015.
We discuss the best resources to learn Topology, career motivation, important qualities sought in data scientists and more.
- Why unsupervised learning is more robust to adversarial distortions
- Jan 30, 2015.
Yoshua Bengio, a leading expert on Deep Learning, explains why good unsupervised learning should be much more robust to adversarial distortions than supervised learning.
- Interview: Anthony Bak, Ayasdi on Novel Insights using Topological Summaries
- Jan 29, 2015.
We discuss examples of Topological Data Analysis (TDA) revealing new insights, recommended approach for creating Topological Summaries, Manual vs Automation approach and trends.
- Year 2014 in Review as Seen by a Event Detection System
- Jan 29, 2015.
We examine the significant events of 2014 found by event/trend detection tool Signi-Trend, including Sochi, Ukraine and Russia, Malaysian airlines, and Islamic State (ISIS).
- Data Science 102: K-means clustering is not a free lunch
- Jan 29, 2015.
K-means is a widely used method in cluster analysis, but what are its underlying assumptions and drawbacks? We examine what happens for non-spherical data and unevenly sized clusters.
- Interview: Anthony Bak, Ayasdi on Managing Data Complexity through Topology
- Jan 28, 2015.
We discuss the definition of Topology, its relevance to Big Data and compare Topological Data Analysis (TDA) with other approaches.
- Interview: Nandu Jayakumar, Yahoo on What Does One Need for Big Data Success
- Jan 27, 2015.
We discuss Yahoo’s contributions to Big Data ecosystem, recommendation to Big Data vendors, predictions for Big Data, advice, and more.
- Interview: Nandu Jayakumar, Yahoo on How Yahoo is Harnessing Big Data
- Jan 26, 2015.
We discuss the major Big Data uses cases at Yahoo, major challenges, trends in enterprise Big Data implementations, and advantages of using Spark.
- (Deep Learning’s Deep Flaws)’s Deep Flaws
- Jan 26, 2015.
Recent press has challenged the hype surrounding deep learning, trumpeting several findings which expose shortcomings of current algorithms. However, many of deep learning's reported flaws are universal, affecting nearly all machine learning algorithms.
- Interview: John Schitka, SAP on The Type of Data Scientists We Need
- Jan 24, 2015.
We discuss the focus areas of Big Data strategy at SAP, how SAP is leading the competition, the kind of data scientists we need, advice and more.
- Text Analysis 101: Document Classification
- Jan 24, 2015.
Document classification is an example of Machine Learning (ML) in the form of Natural Language Processing (NLP). By classifying text, we are aiming to assign one or more classes or categories to a document, making it easier to manage and sort.
- Interview: John Schitka, SAP on How to Get Started with Big Data
- Jan 23, 2015.
We discuss the current perceptions of Big Data, challenges for Big Data consumerization, dealing with the talent gap, and business strategy for Big Data.
- Shining Light on Dark Data
- Jan 23, 2015.
Dark Data is the ever-present, relatively unknown, and unmanaged volumes of data that exist in every corner of one’s business. Here’s what to do with it.
- Interview: Arno Candel, H2O.ai on the Journey from Physics to Machine Learning
- Jan 22, 2015.
We discuss Arno’s career path, transition from Physics to Machine Learning, talent gap in Big Data, advice and more.
- Interview: Arno Candel, H20.ai on How to Quick Start Deep Learning with H2O
- Jan 21, 2015.
We discuss H2O use cases, resources to start using H2O for Deep Learning, evolution of High Performance Computing (HPC) and the future of HPC.
- The High Cost of Maintaining Machine Learning Systems
- Jan 21, 2015.
Google researchers warn of the massive ongoing costs for maintaining machine learning systems. We examine how to minimize the technical debt.
- Can noise help separate causation from correlation?
- Jan 21, 2015.
How to tell correlation from causation is one of the key problems in data science and Big Data. New Additive Noise Models methods can do it with over 65% accuracy, opening new breakthrough possibilities.
- 8 Trends In Big Data For 2015
- Jan 21, 2015.
2015 trends include Non-Data Scientists, Real Time Big Data, Self Service Big Data, Shared Big Data, Big Data and IoT, Richer Data, More Big Data Geeks, and Creative Recruitment - read why.
- Interview: Arno Candel, H2O.ai on the Basics of Deep Learning to Get You Started
- Jan 20, 2015.
We discuss how Deep Learning is different from the other methods of Machine Learning, unique characteristics and benefits of Deep Learning, and the key components of H2O architecture.
- Genetics as a Social Network – A Data Scientist Perspective
- Jan 19, 2015.
You can think about a cell’s genetics as a huge social network. We can then take the DNA sequences of the transcription factor footprints associated with each gene and predict the proteins bound to these regulatory regions, and in this way reconstruct the genetic regulatory networks in every cell type.
- Simple Data Science of Global Warming
- Jan 19, 2015.
You don't have to be a climatologist to empirically confirm global warming. It is enough to have a computer, a reliable data set of historical temperatures, and software like R to do simple calculations.
- IE Masters in Analytics and Big Data – first hand report
- Jan 18, 2015.
First hand report on Master in business analytics and big data program at IE (Madrid, Spain) - why, what, how, days, and challenges.
- Interview: Amit Sheth, Kno.e.sis on Designing Academic Curriculum for Data Science
- Jan 16, 2015.
We discuss curriculum development around Data Science, trends in Big Data arena, qualities sought in students and more.
- How to interview a data scientist
- Jan 16, 2015.
Having spent the last year interviewing a large number of Data Scientists, I’ve developed a simple set of questions that help me to understand the what, the why and the how of what they do.
- Interview: Amit Sheth, Kno.e.sis on Deriving Actionable Insights from Social Data
- Jan 15, 2015.
We discuss Twitris—a tool for collective social intelligence, challenges in using social data to get actionable insights during emergency situations, managing Data Variety, and entrepreneurship.
- MetaMind Competes with IBM Watson Analytics and Microsoft Azure Machine Learning
- Jan 14, 2015.
While Microsoft and IBM rush to bring data science and visualization to the masses, MetaMind follows another path, offering deep learning as a service.
- Interview: Amit Sheth, Kno.e.sis on Deriving Value from Big Data through Smart Data
- Jan 14, 2015.
We discuss the definition of Smart Data, how to derive Smart Data from Big Data, maturity assessment for Smart Data pursuit, computing for human experience and Kno.e.sis.
- Deep Learning can be easily fooled
- Jan 14, 2015.
It is almost impossible for human eyes to label the images below to be anything but abstract arts. However, researchers found that Deep Neural Network will label them to be familiar objects with 99.99% confidence. The generality of DNN is questioned again.
- Exclusive: Interview with Chris Wiggins, NYTimes Chief Data Scientist
- Jan 13, 2015.
New York Times Chief Data Scientist Chris Wiggins on the transformation of digital journalism, key Data Science skills, favorite tools, why better wrong than nice, and how Thomas Jefferson is very relevant today.
- Predictions: 2015 Analytics and Data Science Hiring Market
- Jan 13, 2015.
Thanks to Big Data, analytics have become inescapable. Forget the C-Suite if you’re not a Data Geek, recruiting for startups gets harder, analytics salary bands get a lift, and more 2015 predictions.
- Interview: Miriah Meyer, Univ. of Utah on the Art and Science of Visualization
- Jan 12, 2015.
We discuss insights from the best paper at ACM AVI 2014, increasing interest in visualization, infographics, trends, challenges, advice and more.
- Deep Learning in a Nutshell – what it is, how it works, why care?
- Jan 12, 2015.
Deep learning and neural networks are increasingly important concepts in computer science with great strides being made by large companies like Google and startups like DeepMind.
- Fundamental methods of Data Science: Classification, Regression And Similarity Matching
- Jan 12, 2015.
Data classification, regression, and similarity matching underpin many of the fundamental algorithms in data science to solve business problems like consumer response prediction and product recommendation.
- Debunking Big Data Myths. Again
- Jan 11, 2015.
Myths change with understanding. Misunderstandings on some of the current myths surrounding big data as follows will fade away: big data is made for big business, big data adoption is high and machine learning overcomes human bias.
- Interview: Sharmila Mulligan, ClearStory Data on Variety & Velocity to be Big Data Priorities
- Jan 10, 2015.
We discuss the ClearStory Data’s competitive differentiation, client use case, Big Data trends, advice, desired soft skills in data scientists and more.
- AI Says Data Scientists Not So Sexy in 2015
- Jan 10, 2015.
In 2015, democratization of data will become the democratization of information, data-hoarding era will be end and artificial intelligence will step into the mainstream.
- Differential Privacy: How to make Privacy and Data Mining Compatible
- Jan 9, 2015.
Can privacy coexist with machine learning and data mining? Differential privacy allows the learning of general characteristics of populations while guaranteeing the privacy of individual records.
- Predictive Analytics Innovation Summit, Chicago – Day 2 Highlights
- Jan 9, 2015.
Highlights from the presentations by Predictive Analytics leaders from Time Warner Cable, AT&T and Verizon on day 2 of Predictive Analytics Innovation Summit 2014 in Chicago.
- Interview: Sharmila Mulligan, ClearStory Data on Collaborative StoryBoards for Big Data
- Jan 8, 2015.
We discuss the founding story of ClearStory Data, progress since its launch, Collaborative StoryBoards, common pain points in business analytics and data harmonization.
- Predictive Analytics Innovation Summit, Chicago – Day 1 Highlights
- Jan 6, 2015.
Highlights from the presentations by Predictive Analytics leaders from Netflix, LinkedIn and Mashable on day 1 of Predictive Analytics Innovation Summit 2014 in Chicago.
- Interview: Paul Robbins, STATS on the Potential and Challenges for Sports Analytics
- Jan 5, 2015.
We discuss Analytics at STATS, typical daily tasks, ICE Analytics platform, key challenges, response from coaches/players, career advice and more.
- Causation vs Correlation: Visualization, Statistics, and Intuition
- Jan 4, 2015.
Visualizations of correlation vs. causation and some common pitfalls and insights involving the statistics are explored in this case study involving stock price time series.
- Data Mining and Text Analytics of World Cup 2014
- Jan 3, 2015.
Explore how text analysis techniques to dig into some of the data in a series of blog posts, focusing on matches and their events, tweets languages, tweets volumes for different teams and sentiment analysis.
- 11 Clever Methods of Overfitting and how to avoid them
- Jan 2, 2015.
Overfitting is the bane of Data Science in the age of Big Data. John Langford reviews "clever" methods of overfitting, including traditional, parameter tweak, brittle measures, bad statistics, human-loop overfitting, and gives suggestions and directions for avoiding overfitting.
- Analytics: Five Rules to Cut Through the Hype
- Jan 1, 2015.
Cut through the analytics hype by asking the right questions, discerning between value-add analytics, considering in and out of house solutions, forming an iterative analytics process, and making sure your organization uses it.
