Topics: 
Apr
2015 May Opinions, Interviews, Reports
All (110) | Courses, Education (5) | Meetings (8) | News, Features (22) | Opinions, Interviews, Reports (37) | Publications (8) | Software (6) | Top Tweets (4) | Tutorials, Overviews, How-Tos (9) | Webcasts (11)
- Applied Statistics Is A Way Of Thinking, Not Just A Toolbox
- May 29, 2015.
The choice of tools in applied statistics is driven by the objective, the structure of the data, and the nature of the uncertainty in the numbers, whereas in academic statistics its driven by publishing or teaching. Here we provide some of common statistical tools and the overlapping genealogy.
- R vs Python for Data Science: The Winner is …
- May 26, 2015.
In the battle of "best" data science tools, python and R both have their pros and cons. Selecting one over the other will depend on the use-cases, the cost of learning, and other common tools required.
- White House sees Data as the 21st Century Catalyst for Effective Policing
- May 25, 2015.
Review of the steps taken by White House over last six months to modernize police data systems to better fight crime as well as build trust between community and police.
- Deep Learning and Big Data Products: Synthos Technologies
- May 23, 2015.
Using Deep Learning to build products on massive scale, virtues of transaction analytics, and which industries will likely to be disrupted in the near future, a perspective from Synthos Technologies.
- Exclusive Interview: Matei Zaharia, creator of Apache Spark, on Spark, Hadoop, Flink, and Big Data in 2020
- May 22, 2015.
Apache Spark is one the hottest Big Data technologies in 2015. KDnuggets talks to Matei Zaharia, creator of Apache Spark, about key things to know about it, why it is not a replacement for Hadoop, how it is better than Flink, and vision for Big Data in 2020.
- Interview: Linda Powell, Consumer Financial Protection Bureau (CFPB) on Data Governance for Finance Industry
- May 22, 2015.
We discuss the chief data officer role at CFPB, big data opportunities and challenges, ontology, vintage data, data governance trends, advice, and more.
- Trifacta – Wrangling US Flight Data, part 2
- May 22, 2015.
This post shows how to use Trifacta to clean the data and enrich it with airport geo-locations and airline names, including filling missing values, and doing a lookup from another dataset. We also learn which is the best airline at O’Hare airport.
- 5 Not-to-be-Missed Ideas about Big Data
- May 21, 2015.
The things we can measure are never exactly what we care about; When everything hinges on metrics, people will game the metrics to the point of losing any meaning; and more key ideas summarized by Kaiser Fung.
- Essays On Statistics Denial
- May 20, 2015.
Statistics denial comes in waves as areas of application discover and rediscover the potential of data insights. We examine the statistics denial myths and where they come from.
- I’ve Been Replaced by an Analytics Robot
- May 20, 2015.
A veteran statistician reflects on the journey from a statistician of the past to data scientist of today, how the work he used to do became automated, and what future can data scientists can expect.
- Big Data Lessons from Microsoft “how-old” Experiment
- May 19, 2015.
Salil Mehta examines Microsoft’s viral “How old do I look?” site, the limits of its age recognition, possible algorithms, and implications for Big Data analysis.
- Interview: Antonio Magnaghi, TicketMaster on Why Honesty is Key for Analytics Success
- May 19, 2015.
We discuss lessons from implementing lambda architecture, impact of Big Data on recommender systems, trends, advice, and more.
- Strategies for Monetizing Big Data
- May 19, 2015.
In the current tsunami of “Big Data” every business wants to get value out of the data. We examine four overarching data strategies and their specific monetization strategies.
- Interview: Antonio Magnaghi, TicketMaster on Unifying Heterogeneous Analytics through Lambda Architecture
- May 18, 2015.
We discuss the role of Data Science team at Ticketmaster, ecommerce data characteristics, analytics based on highly variant data flow, infrastructure challenges, and merits of lambda architecture.
- Will the Real Data Scientists Please Stand Up?
- May 18, 2015.
Job postings for data scientists are everywhere. But what is a data scientist? I present a few archetypes.
- Interview: Sheridan Hitchens, Auction.com on Customer Lifetime Value as the Cornerstone for Marketing Analytics
- May 15, 2015.
We discuss Customer Lifetime Value (CLV) metric, maturity level for the CLV metric, different models for calculating it, challenges in designing strategy based on CLV and tackling attribution.
- Data Scientists Thoughts that Make Me Happy: Chris Wiggins
- May 15, 2015.
A Happy Data Scientist makes brilliant images for inspiring quotes by Chris Wiggins, NYTimes Chief Data Scientist - Enjoy.
- Surprising Random Correlations
- May 14, 2015.
An interesting demo showing how easy it is to find surprising correlations in real data. Is German unemployment rate related to Apple Stock? Is 10-year Treasury rate related to price of Red Winter Wheat? You will be surprised.
- Interview: Hobson Lane, SHARP Labs on How Analytics can Show You “All the Light You Cannot See”
- May 14, 2015.
We discuss the impact of rapid growth in magnitude of data, programming skills for data science, major trends, advice, data science skills, and more.
- Cloud Machine Learning’s Ostrich Mania & Uncanny Valley
- May 14, 2015.
Cloud machine learning services are popping up by the tens, providing automated data science solutions. What will the anticipated customers want? They may follow a peculiar distribution reminiscent of the uncanny valley.
- Interview: Hobson Lane, SHARP Labs on the Beauty of Simplicity in Analytics
- May 13, 2015.
We discuss Predictive Analytics projects at Sharp Labs of America, common myths, value of simplicity, tools and technologies, and notorious data quality issues.
- Should Data Science Really Do That?
- May 13, 2015.
Data Science amazing progress in its ability to do predictions and analysis is raising important ethical questions, such as should that data be collected? Should the collected data be used for that application? Should you be involved?
- Predictive Analytics World Workforce 2015: Highlights
- May 13, 2015.
PAW Workforce 2015 highlights include: analytics is now redefining Human Resources, Analytics lessons are quite applicable to workforce questions, and unique challenges of Workforce analytics.
- Interview: Mark Weiner, Temple University Health System on Addressing Healthcare Data Gaps through Advanced Simulation
- May 12, 2015.
We discuss dealing with current gaps in healthcare data, challenges in using real world healthcare data, desired skills for data scientists in healthcare industry, advice, and more.
- Trifacta – Wrangling US Flight Data
- May 12, 2015.
A useful case study shows how Trifacta can clean and analyze US Flight data, including cleaning up markup, removing unrelated and redundant columns, cleaning geographic names and more.
- Interview: Mark Weiner, Temple University Health System on Maturity Assessment of Healthcare Analytics
- May 11, 2015.
We discuss the challenges and opportunities created by increased collection of healthcare data, state of data accessibility, and the value of Analytics to the drug development process.
- Gaming Analytics Summit 2015, San Francisco – Day 2 Highlights
- May 11, 2015.
Highlights from the presentations by Gaming Analytics leaders from Activision, Riot Games and Daybreak Game Company (formerly Sony Online Entertainment) on day 2 of Gaming Analytics Innovation Summit 2015 in San Francisco.
- Interview: Alison Burnham, Scorebig on Optimal, Real-time Pricing through Analytics
- May 8, 2015.
We discuss Analytics at ScoreBig, company’s business model, unexpected insights, challenges in customer value management, advice, and more.
- Gaming Analytics Summit 2015, San Francisco – Day 1 Highlights
- May 8, 2015.
Highlights from the presentations by Gaming Analytics leaders from Facebook, Turbine/Warner Bros Games, and Sega on day 1 of Gaming Analytics Innovation Summit 2015 in San Francisco.
- Interview: Michael Stonebraker, greatest living contributor to database technology
- May 7, 2015.
Michael Stonebraker, described as the greatest living contributor to database technology, on how he adjusts to the award and what trends he foresees in database management systems and big data.
- 10 reasons why how-old.net went viral and how does it work?
- May 7, 2015.
10 reasons why how-old.net went viral and how does it actually works - classic linear regression on top of amazing face recognition.
- Deep Learning with Structure – a preview
- May 6, 2015.
A big problem with Deep Learning networks is that their internal representation lacks interpretability. At the upcoming #DeepLearning Summit, Charlie Tang, a student of Geoff Hinton, will present an approach to address this concern - here is a preview.
- The Inconvenient Truth About Data Science
- May 5, 2015.
Data is never clean, you will spend most of your time cleaning and preparing data, 95% of tasks do not require deep learning, and more inconvenient wisdom.
- Basketball Predictive Analytics: Will he take the shot?
- May 5, 2015.
Sports analytics has reached a new level - now researchers can predict whether and from where a basketball player will take shot, Check a fun online app that lets you play with predictions.
- Talking Machine – Deep Learning in Speech Recognition
- May 2, 2015.
A summary about an episode on the talking machine about deep neural networks in speech recognition given by George Dahl, who is one of Geoffrey Hinton’s students and just defended his Ph.D last month.
- Interview: Haile Owusu, Mashable on Surviving Imprecision in Digital Media Analytics
- May 1, 2015.
We discuss the challenges in tracking social media sharing, advice, important trends, and more.
- How Data Science makes Better Products
- May 1, 2015.
This video defines adaptive software, shows how data science realizes these applications, and discusses how these new tools are addressing real world challenges across all industries.
Nine lessons learned during my first year as a Data Scientist