background preloader

Intuitive Data Analysis

Intuitive Data Analysis
Statwing vs. Statistical Software Statwing is built for business users, data analysts, and market researchers, so it chooses statistical tests automatically. Traditional statistical software was built for statisticians, and requires technical expertise to ask even simple questions. And unlike traditional software, Statwing accounts for data issues like outliers, so you can always be confident in your analyses. Statwing also translates results into plain English, so anyone can understand their data the way a statistician or data scientist would.

https://www.statwing.com/

Related:  DataVizBig Data

Free Online Data Training Data visualization basic training; from spreadsheet to data mapping. kdmcBerkeley is offering four free online training courses in data journalism. You'll learn basic data visualization skills, from spreadsheets to data mapping. Each of the four one-hour long courses builds upon the other; register for all four sessions or choose the session that best meets your needs. Each course is offered twice, once at 10am PST and then again at 1pm PST. Registration is limited to the first 200 participants per session and registration for all four courses is now open. The most influential data scientists on Twitter Twitter is emerging as an important medium for determining influence in many fields. Social ranking sites like Klout and Traackr include Twitter as a heavily-weighted component of their ranking algorithms, for example. Twitter isn't representative of the members of any field, but in areas where the members primarily engage online, it can be a useful proxy. SocialFlow's Gilad Lotan has used an analysis of social networks on Twitter to rank influences in such fields, including the communities of python users and data scientists. Here's the network chart for data scientists:

50 New Tools Democratizing Data Analysis & Visualization Just as we've seen the shift to "DIY" data collection platforms, we're also seeing the development of a whole new class of self-service data exploration and visualization tools. Just as we’ve seen the shift to “DIY” data collection platforms, we’re also seeing the development of a whole new class of self-service data exploration and visualization tools. These are not necessarily replacements for SPSS, SAS, R, and other traditional analytical suites: those enterprise level systems are often still needed to do more complex and advanced statistical analysis.

Where to Learn Deep Learning – Courses, Tutorials, Software Deep Learning is a very hot Machine Learning techniques which has been achieving remarkable results recently. We give a list of free resources for learning and using Deep Learning. By Gregory Piatetsky, @kdnuggets, May 26, 2014. Deep Learning is a very hot area of Machine Learning Research, with many remarkable recent successes, such as 97.5% accuracy on face recognition, nearly perfect German traffic sign recognition, or even Dogs vs Cats image recognition with 98.9% accuracy. Many winning entries in recent Kaggle Data Science competitions have used Deep Learning. Learn how to use Gephi Welcome to Gephi! Gephi is an open-source software for visualizing and analysing large networks graphs. Gephi uses a 3D render engine to display graphs in real-time and speed up the exploration. You can use it to explore, analyse, spatialise, filter, cluterize, manipulate and export all types of graphs. Getting Started

What a Big-Data Business Model Looks Like - R “Ray” Wang by R “Ray” Wang | 10:00 AM December 6, 2012 The rise of big data is an exciting — if in some cases scary — development for business. Together with the complementary technology forces of social, mobile, the cloud, and unified communications, big data brings countless new opportunities for learning about customers and their wants and needs. 10 Awesome Free Tools To Make Infographics Advertisement Who can resist a colourful, thoughtful venn diagram anyway? In terms of blogging success, infographics are far more likely to be shared than your average blog post. Why D3.js is So Great for Data Visualization When D3 came out in 2011, it became clear pretty quickly that it was going to be a powerful tool for creating data visualizations. But it’s certainly not the first — or only — tool. Why did it succeed when so many other libraries have failed? First of all, it works on the web.

Related: