What is data science? We’ve all heard it: according to Hal Varian, statistics is the next sexy job. Five years ago, in What is Web 2.0, Tim O’Reilly said that “data is the next Intel Inside.” But what does that statement mean? Why do we suddenly care about statistics and about data? In this post, I examine the many sides of data science — the technologies, the companies and the unique skill sets. The web is full of “data-driven apps.” One of the earlier data products on the Web was the CDDB database. Google is a master at creating data products. Google’s breakthrough was realizing that a search engine could use input other than the text on the page. Flu trends Google was able to spot trends in the Swine Flu epidemic roughly two weeks before the Center for Disease Control by analyzing searches that people were making in different regions of the country. Google isn’t the only company that knows how to use data. In the last few years, there has been an explosion in the amount of data that’s available.
What Is Data Science? Data Scientists Data Scientists perform data science. They use technology and skills to increase awareness, clarity and direction for those working with data. The data scientist role is here to accommodate the rapid changes that occur in our modern day environment and are bestowed the task of minimising the disruption that technology and data is having on the way we work, play and learn. Data Scientists don’t just present data, data scientists present data with an intelligence awareness of the consequences of presenting that data. How To Do Data Science The three components involved in data science are organising, packaging and delivering data (the OPD of data).
Fast clustering algorithms for massive datasets Here we discuss two potential algorithms that can perform clustering extremely fast, on big data sets, as well as the graphical representation of such complex clustering structures. By extremely fast, we mean a computational complexity of order O(n) and even faster such as O(n/log n). This is much faster than good Hierarchical Agglomerative Clustering which are typically O(n^2 log n). By big data, we mean several millions, possibly a billion observations. Potential applications: Creating a keyword taxonomy to categorize the entire universe of cleaned (standardized), valuable English keywords. We also discuss whether it makes sense to perform such massive clustering, and how Map Reduce can help. A. Here's the answer, from my earlier article What MapReduce can't do. Step #1: pre-processing You gather tons of keywords over the Internet with a web crawler (crawling Wikipedia or DMOZ directories), and compute the frequencies for each keyword, and for each "keyword pair". where B. Comments
Brains of Introverts Reveal Why They Prefer Being Alone Human faces may hold more meaning for socially outgoing individuals than for their more introverted counterparts, a new study suggests. The results show the brains of extroverts pay more attention to human faces than do introverts. In fact, introverts' brains didn't seem to distinguish between inanimate objects and human faces. The findings might partly explain why extroverts are more motivated to seek the company of others than are introverts, or why a particularly shy person might rather hang out with a good book than a group of friends. The study also adds weight to idea that underlying neural differences in people's brains contribute to their personality. "This is just one more piece of evidence to support the assertion that personality is not merely a psychology concept," said study researcher Inna Fishman, of the Salk Institute for Biological Sciences in La Jolla, Calif. Personality in the brain Extroversion deals with the way people interact with others. Faces or flowers?
Tutorials # Tutorials ## General Tutorials - [Data Science Essentials]( introduction to data visualization and exploration concepts - [CS109]( Collection of data science lectures with programming assignments and projects - [Introduction to Data Science]( comprehensive overview of modern data science - [Data Analyst Nanodegree]( General Tutorials Python Tutorials R Tutorials
Institute for Advanced Analytics | Dr. Michael Rappa · Data Science Lab The Data Science Lab (or “DataLab”) is the research arm of the Institute for Advanced Analytics. It brings together investigators from various disciplines to focus on the intersection of analytical and computational challenges organizations face in extracting meaningful insights from a vast quantity and variety of data. Areas of Interest The detection of anomalous patterns or rare events within and across various realms of data, and determining whether such patterns are meaningful outliers or artifacts. This might include such varied activities as detecting fraudulent financial transactions or cheating in massive multiplayer online games.The detection of commonalities in behavior or sentiment in the analysis of text on the Web or other content systems. Collaborators Doctoral students: Mark Cusick – Machine Learning
What is a Data Scientist? – Bringing big data to the enterprise About data scientists Rising alongside the relatively new technology of big data is the new job title data scientist. While not tied exclusively to big data projects, the data scientist role does complement them because of the increased breadth and depth of data being examined, as compared to traditional roles. Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data Download the ebook So what does a data scientist do? A data scientist represents an evolution from the business or data analyst role. The data scientist role has been described as “part analyst, part artist.” Whereas a traditional data analyst may look only at data from a single source – a CRM system, for example – a data scientist will most likely explore and examine data from multiple disparate sources. Data scientists are inquisitive: exploring, asking questions, doing “what if” analysis, questioning existing assumptions and processes. Want to learn more about big data?
A Statistician's View on Big Data and Data Science (Version 1) COMER UNA VAGINA PUEDE DISMINUIR EL RIESGO DE CÁNCER. (LITERALMENTE) - Salud y Remedios Naturales Compartelo ahora Compartir Ahora Estudios universitarios realizados en la ciudad de New York dan a conocer los beneficios que trae el hacer sexo oral, hombre a mujer. (Literalmente)Comer una Vagina puede disminuir el riesgo de cáncer en el hombre. Han estado haciendo uso de una gran cantidad de su tiempo buscando la opción de nuevas soluciones a las enfermedades terminales como lo es el cáncer. ¿Sabías que el consumo de órgano del cuerpo de una mujer podría conservar de las condiciones mortales tales como las células de cáncer y las enfermedades cardiovasculares también? Basados en el estudio de investigación que se hizo en el Colegio Estatal de Nueva York, uno de los platos más vital que un chico necesita tomar es el consumo de una zona vaginal. Las hormonas corporales tales como los agentes hormonales DHEA así como la oxitocina se generan cada vez que se lleva a cabo cunnilingus. Aqui Algunos Consejos Para Mejorar tu Vida Intima!
Help on subsetting data frames using multiple logical operators in R Setting up a Data Science Laboratory There is no better way of understanding new data processing, retrieval, analysis or visualising techniques than actually trying things out. In order to do this, it is best to use a server that acts as data science lab, with all the basic tools and sample data in place. Buck Woody discusses his system, and the configuration he chose. I'm going to be describing how to set up a Data Science Laboratory system that you would be able to use in order to compare, study and experiment with various data storage, processing, retrieval, analysis and visualization methodologies. I apologize for the use of the controversial term ‘Data Science' but it captures the meaning. My plan is to set up a system that allows me to install and test various methods of storing, processing and delivering data. Text Systems Interactive Data Tools Relational Database Management Systems Key/Value Pair Document Store Databases Graph Databases Object-Oriented Databases Distributed File and Compute Data Handling Systems