What Types of Questions Can Data Science Answer
Data science has enabled us to solve complex and diverse problems by using machine learning and statistic algorithms. Here we have enumerated the common applications of supervised, unsupervised and reinforcement learning techniques
on Sep 29, 2015 in Data Science, Use Cases
Data Lake vs Data Warehouse: Key Differences
We hear lot about the data lakes these days, and many are arguing that a data lake is same as a data warehouse. But in reality, they are both optimized for different purposes, and the goal is to use each one for what they were designed to do.
on Sep 29, 2015 in Data Lake, Data Warehouse, SAS, Tamara Dull
Interview: Michael Brodie – We Can’t Rely on Machines
Michael Brodie, a leading database researcher, is convinced that Big Data has more potential than the hype suggests, but also more risks.
on Sep 28, 2015 in Big Data, Machine Learning, Michael Brodie, Opportunities, Switzerland, Threat to Humanity
Workforce Data Science: Does Talent Development Increase Performance Over Time?
Large organizations spend millions on training, coaching, mentoring, re-training and competency development programs. Business ROI with these predictive projects is very significant, here we are sharing some of our findings as they may challenge some concepts we hold so closely.
on Sep 28, 2015 in Greta Roberts, Workforce Analytics
Opentext Data Driven Digest, Sep 18: Money and Finance
Pacific Stock Exchange in San Francisco was created 133 years ago this week to serve businesses that struck rich mining for gold during the California Gold Rush. Nowadays, businesses mine for data hoping to strike it rich, using visualizations like the ones below.
on Sep 23, 2015 in Data Visualization, OpenText, Stocks
Dissecting the Big Data Twitter Community through a Big data Lens
Tweeter communities have activities: tweets, retweets, replies, and followers. Retweets graph is a good representation of actual connections in the network, their strengths, as well as the propagation of information through the network.
on Sep 23, 2015 in Big Data Influencers, Srinath Perera, Twitter
Support “Talking Machines” – the best podcast on Machine Learning, Data Science and AI
Excitement, and worry, about ML, AI, and data science is at a fever pitch. It’s our responsibility to bring the conversation back to reality, and support the projects that do, or face another ‘winter’.
on Sep 20, 2015 in Data journalism, Machine Learning, Podcast, Talking Machines
Data Science Data Logic
Even though participating in MOOCs and online competitions are good exercises to learn data science, but it is more than algorithms and accuracies. Understand how to formulate hypothesis, data creation, sampling, validation, etc. to become true data scientist.
on Sep 17, 2015 in Data Preparation, Data Science, Forecasting, Olav Laudy
Deep Learning and Artistic Style – Can art be quantified?
We analyze the latest advance in Deep learning which teaches computers to paint in the style of different famous painters, from Van Gogh to Picasso. Is it really Art?
on Sep 17, 2015 in Art, Caffe, Convolutional Neural Networks, Deep Learning
Exclusive Interview: Big Data and Data Science at UN
We interview the UN Chief Information Technology Officer about how Big Data and Data Science can help solve world's problem. Check Unite Ideas crowdsourcing platform for data analytics challenges where you can help.
on Sep 16, 2015 in Challenge, Crowdsourcing, Text Analytics, United Nations
Doing Data Science at Twitter
Data scientist career exciting, fulfilling and multidimensional career path. Understand through the journey of a data scientist of twitter about data scientists roles, responsibilities and skills required to perform them.
on Sep 16, 2015 in A/B Testing, Data Science Skills, Data Science Tools, Machine Learning
Are you trying to acquire Machine Learning Skills?
Embarking on a journey through the lands of machine learning? Here are few important lessons like Feature Engineering, Model tuning, Overfitting, Ensembling etc. which you should keep in mind along the way.
on Sep 16, 2015 in Boosting, Data Science Skills, edX, Ensemble Methods, Feature Engineering, Machine Learning, MOOC, Overfitting
The 123 Most Influential People in Data Science
We used LittleBird algorithm to build a true Data Science influencer network by measuring how often influencers retweet other influencers. Top influencers include @hmason, @kdnuggets, @kaggle, @peteskomoroch, @mrogati, and @KirkDBorne.
on Sep 15, 2015 in About KDnuggets, Alex Salkever, Big Data Influencers, Data Science, Hilary Mason, Influencers, Kaggle, Kirk D. Borne, Silk.co
Opentext Data Driven Digest, Sep 11: Clouds
Big Data Analytics is now available on the OpenText Cloud, which got us thinking about actual clouds – the kind up in the sky. Not surprisingly, there are countless great data visualizations related to clouds and weather, so it was tough to choose just three to share
on Sep 15, 2015 in Data Visualization, OpenText, Weather
Big Data Monetization Lessons from Zillow
In the current tsunami of “Big Data” every business wants to get value out of the data. Here, we are sharing lessons learned by the new real estate websites who have brought together Big Data sets, home buyers, and home sellers.
on Sep 14, 2015 in Big Data, Data Monetization, Maps, Monetizing, Russell Walker, Zillow
Analytics for Personal Fitness Devices
Analytics in health care is yet an undiscovered territory, but due to IoT devices it is estimated to grow to $53 billion in the next three years. Here we explain the current status of industry, its future potential and key drivers.
on Sep 12, 2015 in Fitness, IoT, Mobility
Data Science Data Architecture
Data scientists are kind of a rare breed, who juggles between data science, business and IT. But, they do understand less IT than an IT person and understands less business than a business person. Which demands a specific workflow and data architecture.
on Sep 10, 2015 in Big Data Architecture, Data Management, Data Science, Olav Laudy
Big Data Analytics in Hotel Industry
The Hotel industry is another data rich industry that captures huge volumes of data of different types. Find out, how Customer Segmentation, Energy Consumption, Investment Management, and Resource Allocation for it can be revolutionized using big data analytics.
on Sep 7, 2015 in Big Data Analytics, Customer Analytics, Decision Management, Goran Dragosavac, Hotels
NSA Patents Analysis and Visualization
To understand details and images of nearly 300 patents filed by the National Security Agency, Alice Corona collected data made available by the USPTO, put into the Silk data publishing and data visualization platform.
on Sep 6, 2015 in Alex Salkever, Data Visualization, Geo-Localization, NSA, Patents, Silk.co, Snowden, Text Mining
How to Balance the Five Analytic Dimensions
When developing a solution one has to consider data complexity, speed, analytic complexity, accuracy & precision, and data size. It is not possible to best in all categories, but it is necessary to understand the trade-offs.
on Sep 3, 2015 in Accuracy, Complexity, Precision
OpenText Data Driven Digest, Aug 28: Treemaps
Treemaps enable us to visualize the large data set in a concise manner. There are many variations possible with them, here some of the well known tree-maps like Leaves of Green, Repping Fragmentation and All Wet.
on Sep 2, 2015 in Data Visualization, OpenText, Treemaps
Can Data Mining Extract Value from your Personal Data (and should you get a piece of the action?)
There are industries made out of the personal data, but content providers don’t get their share. What if you are presented with the opportunity to sell your personal data for financial benefits? Find out more by taking the survey from datamilk.
on Sep 2, 2015 in Big Data Privacy, Facebook, Privacy
The one language a Data Scientist must master
Getting started with the data science, and wondering which language to pick up and technology to explore. But, that is secondary, every business is structured differently and to understand it and build on top of it, is the crux of data science.
on Sep 1, 2015 in Matt Reaney, Programming Languages, Python vs R
Predictive Analytics – a Soup Story
CRISP-DM is most popular methodology for analytics, data mining, and data science projects. Learn how to cook CRISP-DM recipe in five simple steps.
on Sep 1, 2015 in CRISP-DM, Predictive Analytics