Dec
2016 Jan Opinions, Interviews, Reports
All (120) | Courses, Education (11) | Meetings (12) | News, Features (27) | Opinions, Interviews, Reports (35) | Publications (10) | Software (5) | Top Tweets (3) | Tutorials, Overviews (11) | Webcasts (6)
- How banks can beat new finance boys with data - Jan 29, 2016.
The rise of Apple/Google smartphone payments and new fintech start ups present challenges to traditional banks. Banks can fight back, but they need to understand how to better use their data to understand its customers.
- Details on First Data Science Job Salary - Jan 29, 2016.
A person new to the Data Science field details their salary and the negotiation process.
- Is Deep Learning Overhyped? - Jan 29, 2016.
With all of the success that deep learning is experiencing, the detractors and cheerleaders can be seen coming out of the woodwork. What is the real validity of deep learning, and is it simply hype?
- Useful Data Science: Feature Hashing - Jan 28, 2016.
Feature engineering plays major role while solving the data science problems. Here, we will learn Feature Hashing, or the hashing trick which is a method for turning arbitrary features into a sparse binary vector.
- Modern Data Science and Evolution of BI - Jan 26, 2016.
Modern big data discovery tools enable all employees to access the data, streamlining the data prep process, and allowing data scientists to spend more time on advanced analytics. The infographics in this post show the evolution of the data scientist from data drudgery to modern data science for all.
- Deep Feelings On Deep Learning - Jan 25, 2016.
A thoughtful opinion piece on deep learning and its role in Strong AI. A pragmatic view of deep learning and its comparison to competing learning strategies is presented.
- Sentiment Analysis & Predictive Analytics for trading. Avoid this systematic mistake - Jan 25, 2016.
The financial market is the ultimate testbed for predictive theories. With this post we want to highlight the common mistakes, observed in the world of predictive analytics, when computer scientists venture into the field of financial trading and quantitative finance.
- Data Analytics Boosting Digital Engagement at Australian Open 2016 - Jan 25, 2016.
Advanced analytics and visualization is enhancing fan experience and operational excellence at Australian Open 2016
- Beyond the Fence, and the Advent of the Creative Machines - Jan 25, 2016.
Creative machines have been making their influence felt for some time, but an upcoming stage production challenges preconceived notions of what art is.
- Spark and the Remorseless Recrystallization of the Open Source Analytics Ecosystem - Jan 23, 2016.
Apache Spark had robust machine learning, graph, streaming, and in-memory capability to the Hadoop-centric ecosystem. In 2016, we expect adoption in diverse big data, advanced analytics, data science, Internet of Things, and other application domains.
- Hadoop and Big Data: The Top 6 Questions Answered - Jan 22, 2016.
6 questions surrounding Hadoop and Big Data are posed and answered, including those related to implementation, management, and practical uses. Find out where Hadoop currently sits in the world of Big Data.
- Airbnb: Lessons on Digital, Startups, Big Data and Disrupting Markets - Jan 21, 2016.
AirBnB has brought together unmatched supply and demand and allowed for market-driven evaluation of assets. We are sharing lessons learnt from them for digital startups and big data organisations.
- Public Knowledge Graph – small guys unite - Jan 21, 2016.
Currently, only global corporations like Google or Facebook can maintain a vast knowledge graph about the world. Little companies which rely on knowing world context need to unite to create a Public Knowledge Graph, or they will fall further behind the big guys.
- CEOs Pursue Data and Analytics for Stakeholder Engagement - Jan 21, 2016.
PWC’s Global CEO Survey highlights the strategic importance of Data and Analytics for achieving wider stakeholder engagement.
- Three Simple Resolutions to Design Better DataViz - Jan 20, 2016.
Start your New Year off with resolutions to produce better data visualizations: visualize your data, remove chart legends, and try new things.
- The Unreasonable Reputation of Neural Networks - Jan 20, 2016.
A discussion of why deep neural networks are captivating imaginations everywhere, specifically their abilities to model many natural functions well and to learn surprisingly useful representations.
- Anthony Goldbloom gives you the Secret to winning Kaggle competitions - Jan 20, 2016.
Kaggle CEO shares insights on best approaches to win Kaggle competitions, along with a brief explanation of how Kaggle competitions work.
- Data Science Humor: Google Analytics, if Applied in Real Life - Jan 16, 2016.
From the lighter side: how Google Analytics would look if applied in real life situations.
- What Is Machine Intelligence Vs. Machine Learning Vs. Deep Learning Vs. Artificial Intelligence (AI)? - Jan 14, 2016.
A discussion of three major approaches to building smart machines - Classic AI, Simple Neural Networks, and Biological Neural Networks - and examples as to how each approach might address the same problem.
- Plausibility vs. probability, prior distributions, and the garden of forking paths - Jan 14, 2016.
A discussion on plausibility vs. probability: while many given events may be plausible, but they can’t all be probable.
- Creating a methodology for Data Science for IoT (IoT Analytics) - Jan 13, 2016.
While there is no specific methodology to solve Data Science for IoT (IoT Analytics) problems, perhaps it is time to draft one.
- Climate Change, Clearly Visualized - Jan 13, 2016.
Global warming has been argued in depths and breadths and arguments for and against are championed too.Here, with a simple data science we obtained a simple (and increasingly accepted) conclusion: the global warming is real.
- The Data Awakens: Star Wars Sentiment Analysis - Jan 13, 2016.
We have tracked the activity on Twitter around the release date to gain insight into the reactions of people and their feelings about the latest episode of the most famous movie franchise in history.
- A Look Back on the 1st Three Months of Becoming a Data Scientist - Jan 13, 2016.
A person new to the Data Science field summarizes his surprising findings after a few months on the job.
- MapR CEO 5 Big Data Predictions for 2016 - Jan 11, 2016.
The industry is in the midst of the biggest change in enterprise computing in decades. Schroeder sees an acceleration in big data deployments, and has crystallized his view of market trends into these five major predictions for 2016.
- What To Expect from Deep Learning in 2016 and Beyond - Jan 11, 2016.
Predictions from some of the top names in deep learning, including Ilya Sutskever and Andrej Karpathy, about what to expect in the field over the next 5 years.
- A Non-comprehensive List of Awesome Things other People Did in 2015 - Jan 9, 2016.
A top statistics professor and statistical researcher reflects on a number of awesome accomplishments by individuals in, and related to, the fields of statistics and data science, with a focus on the world of academia but with resonance far beyond.
- OpenText Data Digest, Jan 5: Life and Expectations - Jan 8, 2016.
New year has just begun and for the year ahead is a good opportunity to consider the passage of time, how much is left to each of us. We’re presenting some of the best visualizations of lifespans and life expectancy.
- Data Science Resume Tips and Guidelines - Jan 8, 2016.
A well-built resume is key to get through the first door – in the process of getting hired as a Data Scientist. Learn more, about how to present yourself as a true DS and which pitfalls to avoid.
- The Case Against Quick Wins in Predictive Analytics Projects - Jan 6, 2016.
While “quick wins” are desirable, getting them in a predictive project can be difficult. We review 2 major obstacles to quick wins in predictive analytics projects.
- Nando de Freitas AMA: Bayesian Deep Learning, Justice, and the Future of AI - Jan 6, 2016.
During his recent AMA, machine learning star Nando de Freitas answers a host of questions on a number of topics, including Bayesian methods in deep learning, harnessing AI for the good of humanity, and what the future holds for machine learning.
- FICO Chief Analytics Officer 2016 Predictions - Jan 5, 2016.
NASA Juno mission will arrive at Jupiter. The Summer Olympics will take place in Rio de Janeiro. The US will have a presidential election. And prescriptive analytics will take center stage as the ultimate destination on the analytics journey.
- Predictive Power of Terror Alerts and Monkeys - Jan 4, 2016.
The terrorism threat advisory system was designed to give the public prior warning to when terrorist plots are about to unfold. However, the analysis shows that this system is not more helpful than monkey throwing a dart.
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20 Questions to Detect Fake Data Scientists - Jan 1, 2016.
Hiring Data Scientists is no easy job, particularly when there are plenty of fake posers. Here is a handy list of questions to help separate the wheat from the chaff. - What questions can data science answer? - Jan 1, 2016.
There are only five questions machine learning can answer: Is this A or B? Is this weird? How much/how many? How is it organized? What should I do next? We examine these questions in detail and what it implies for data science.
20 Questions to Detect Fake Data Scientists