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
BABELNET 3.5, Largest Multilingual Dictionary and Semantic Network
BabelNet 3.5 covers 272 languages, and offers an improved user interface, new integrated resources of Wikiquote, VerbNet, Microsoft Terminology, GeoNames, WoNeF and ImageNet, and a very large knowledge base with over 380 million semantic relations.
on Sep 29, 2015 in BabelNet, RESTful API, Text Mining, Wikidata
Topological Analysis and Machine Learning: Friends or Enemies?
What is the interaction between Topological Data Analysis and Machine Learning ? A case study shows how TDA decomposition of the data space provides useful features for improving Machine Learning results.
on Sep 29, 2015 in Ayasdi, Machine Learning, random forests algorithm, Topological Data Analysis
The Master Algorithm – new book by top Machine Learning researcher Pedro Domingos
Wonderfully erudite, humorous, and easy to read, the Master Algorithm by top Machine Learning researcher Pedro Domingos takes you on a journey to visit the 5 tribes of Machine Learning experts and helps you understand what the Master Algorithm can be.
on Sep 25, 2015 in Algorithms, Book, Machine Learning, Pedro Domingos
15 Mathematics MOOCs for Data Science
The essential mathematics necessary for Data Science can be acquired with these 15 MOOCs, with a strong emphasis on applied algebra & statistics.
on Sep 23, 2015 in Applied Statistics, Coursera, Data Science, edX, Mathematics, MOOC, R, Udemy
SentimentBuilder: Visual Analysis of Unstructured Texts
Sankey diagrams are mainly used to visualize the flow of data on energy flows, material flow and trade-offs. SentimentBuilder found how to use them with unstructured text in their online NLP tool.
on Sep 18, 2015 in Data Visualization, Sentiment Analysis, Text Mining
Top 10 Quora Machine Learning Writers and Their Best Advice
Top Quora machine learning writers give their advice on pursuing a career in the field, academic research, and selecting and using appropriate technologies.
on Sep 18, 2015 in Machine Learning, Quora, random forests algorithm, Top 10, Xavier Amatriain, Yoshua Bengio
Top 10 Quora Data Science Writers and Their Best Advice
Top Quora data science writers give their advice on pursuing a career in the field, approaching interviews, and selecting appropriate technologies.
on Sep 17, 2015 in Data Science, Quora, scikit-learn, Top 10
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
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
A Great way to learn Data Science by simply doing it
There are tons of great online resources out there we can pick up and learn them to become a master in data science. Here is a comprehensive list of data science course providers along with links to the data science courses.
on Sep 11, 2015 in Data Science, Data Science Education
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
Salaries by Roles in Data Science and Business Intelligence
Data Scientist is the hottest role. What's next? We present national average salaries, job title progression in career, job trends and skills for popular job titles in Data Science & Business Intelligence. Check out the salaries of related roles.
on Sep 9, 2015 in Business Intelligence, Data Science, Data Science Skills, Data Scientist, Salary, Trends
Spark SQL for Real-Time Analytics
Apache Spark is the hottest topic in Big Data. This tutorial discusses why Spark SQL is becoming the preferred method for Real Time Analytics and for next frontier, IoT (Internet of Things).
on Sep 4, 2015 in Ajit Jaokar, Apache Spark, Real-time, SQL, Sumit Pal
60+ Free Books on Big Data, Data Science, Data Mining, Machine Learning, Python, R, and more
Here is a great collection of eBooks written on the topics of Data Science, Business Analytics, Data Mining, Big Data, Machine Learning, Algorithms, Data Science Tools, and Programming Languages for Data Science.
on Sep 4, 2015 in Book, Brendan Martin, Data Mining, Data Science, Free ebook, Machine Learning, Python, R, SQL
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
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
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