UT.7.01x: Foundations of Data Analysis from edX *Note - This is an Archived course* In a world that’s full of data, we have many questions: How long do animals in a shelter have to wait until they are adopted? Can we model the growth of internet usage in a country?
CS341: Project in Mining Massive Data Sets (Spring 2014) Project in Mining Massive Data Sets Spring 2014 CS341 is an advanced project based course. Learn Data Science by nborwankar Who Nitin Borwankar - primary developer (Sponsored by Pivotal Inc. and Alpine Data Labs). What Fellowship Programme What is a School of Data fellow? Fellowships are nine-month placements with School of Data for data-literacy practitioners or enthusiasts. During this time, fellows work alongside School of Data: you will learn a lot from us and we will learn a lot from you! We’ll work together to build an individual programme for your fellowship, that equips you with the skills to take your data-literacy work forward, whether that be how to train, how to network or how to organise events. In all these areas, our aim is to increase awareness of data-literacy and build communities who together, can use data literacy skills to make the change they want to see in the world. Our fellowship programme aims to to recruit and train the next generation of data leaders and trainers to magnify the reach of our data literacy programme.
Essentials of Machine Learning Algorithms (with Python and R Codes) Introduction Google’s self-driving cars and robots get a lot of press, but the company’s real future is in machine learning, the technology that enables computers to get smarter and more personal. – Eric Schmidt (Google Chairman) We are probably living in the most defining period of human history. The period when computing moved from large mainframes to PCs to cloud. But what makes it defining is not what has happened, but what is coming our way in years to come. What makes this period exciting for some one like me is the democratization of the tools and techniques, which followed the boost in computing.
Reporting, Analytics, and Big Data: A Continuous Feedback Loop to Drive Better Decision-Making March 2, 2014 at 3:07 pm Mary Ludloff By Marilyn Craig A recent conversation with a client reminded me that no matter how crazy and exciting the Big Data world gets, it is still critical to understand what your goals are and where you are in the process of reaching those goals. Having a good foundation in “what’s important” is critical before you jump into the wild world of Big Analytics. For example, in big data (well, actually all data but I digress) “Reporting” and “Analytics” are very different functions. Scraping data from Sina Weibo using Python Weibo Oauth2.0 I would like to introduce you how to use python to scrape tweets from Sina Weibo in this post. Automatically get authorization by oauth2.0 First, following this webpage to set your app, especially to get the app key, app secret, and set the callback url.
Product Overview - Big Data Analytics - Datameer Integrate, prepare, analyze and visualize any data Datameer simplifies the big data analytics environment into a single application on top of the powerful Hadoop platform. The only end-to-end big data analytics application for Hadoop designed to make big data simple for everyone, Datameer combines self-service data integration, analytics and visualization functionality that provides the fastest time to insights. Data integration Liberate your data Data is the raw materials of insight and the more data you have, the deeper and broader the possible insights. Not just traditional, transaction data but all types of data so that you can get a complete view of your customers, better understand business processes and improve business performance. Learn about data integration Self-service data analytics Insights without boundaries Datameer provides complete analytics from simple joins and transforms to complex predictive analytics. Security for your data, in Datameer and Hadoop
Weka 3 - Data Mining with Open Source Machine Learning Software in Java Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.