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gloriousfestgentlemen02 · 4 months ago

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Cryptocurrency SEO Automation with R

In the fast-paced world of cryptocurrency, staying ahead in search engine rankings is crucial for attracting potential investors and maintaining a strong online presence. One effective way to achieve this is through the automation of SEO (Search Engine Optimization) processes using R, a powerful programming language known for its capabilities in data analysis and automation. In this article, we will explore how R can be leveraged to automate various SEO tasks specifically tailored for cryptocurrency websites.

Why Automate SEO with R?

1. Efficiency: Automating repetitive tasks such as keyword research, backlink analysis, and content optimization allows you to save time and focus on more strategic aspects of your SEO strategy.

2. Accuracy: R’s robust data handling capabilities ensure that your SEO efforts are based on accurate and up-to-date information.

3. Scalability: As your cryptocurrency project grows, so do your SEO needs. R’s scalability makes it easy to handle larger datasets and more complex analyses.

Keyword Research Automation

Keyword research is fundamental to any SEO strategy. By automating this process with R, you can quickly identify high-traffic keywords relevant to your cryptocurrency niche. Libraries like `googleVis` and `rvest` can be used to scrape search engine results and analyze keyword performance.

Backlink Analysis

Backlinks are a critical factor in search engine rankings. Using R, you can automate the process of analyzing your backlink profile and identifying opportunities for improvement. The `httr` package is particularly useful for fetching URLs and extracting link data.

Content Optimization

Content is king in SEO. With R, you can automate the process of optimizing your content for better search engine visibility. This includes analyzing word frequency, ensuring proper use of headers, and identifying areas where additional content could improve engagement.

Conclusion

Automating SEO with R offers numerous benefits for cryptocurrency projects looking to enhance their online visibility. By leveraging R’s powerful data analysis capabilities, you can streamline your SEO efforts, making them more efficient and effective. What are some specific challenges you face in your cryptocurrency SEO strategy? How do you think automation could help address these challenges? Share your thoughts in the comments below!

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maximaformacionsblog · 1 year ago

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Curso Moodle y Exelearning

A menudo me consultan qué software estadístico o de programación aprender para iniciar una carrera en Ciencia de Datos. Hoy te traigo algunas reflexiones para ayudarte a elegir la herramienta perfecta para ti.

Piensa en soluciones, no en herramientas

Lo primero que quiero dejar en claro es que la habilidad que adquieras debe ir más allá de utilizar un software estadístico. Recuerda que el rol del Científico de datos es resolver problemas y aportar información valiosa para la investigación o negocio.

Preocúpate entonces por lo que vas a hacer con la herramienta que elijas.

La mayoría de las funciones que utilizarás en tu día a día como Científico de datos se repiten de una herramienta a otra. En algunos casos incluso tendrás que resolver problemas con más de una herramienta.

En cualquier caso, hay que ponerse y empezar de alguna forma en el mundo de la Ciencia de Datos, por ejemplo, aprendiendo Python básico.

¿Por qué necesito programar?

Las habilidades son las capacidades para ejecutar la función y eso incluye la programación.

¡Programar te abrirá muchas posibilidades! Te permitirá:

• Automatizar tareas • Manejar bases de datos grandes • Explorar múltiples funcionalidades de manera sencilla • Múltiples formas de comunicar resultados • Registrar tu trabajo para hacerlo reproducible

No necesitas ser un ingeniero de software, pero tener claro los conceptos básicos te ayudará.

Lenguajes de la Ciencia de Datos, ¿cuál elegir?

Cada lenguaje tiene sus propias características y capacidades únicas que lo hacen funcionar para ciertos profesionales de la Ciencia de Datos. En la siguiente imagen te dejo un resumen de los 3 softwares principales hoy en día en Ciencia de Datos. Lo importante es la habilidad. No necesitas utilizar todos los idiomas, pero elige uno y domínalo con el tiempo.

Ninguna herramienta es la mejor para todos ni para todo. Sería ridículo decir que un lenguaje es el mejor. Cada uno tiene sus ventajas, y qué lenguaje necesitas aprender dependerá de tu contexto y habilidades. Piensa cuáles de estas ventajas se adaptan mejor a tus necesidades, aunque no necesariamente serán las mejores para alguien que trabaje en un área diferente, con diferentes exigencias.

Ten en cuenta el campo en el que deseas trabajar, la empresa para la que trabajas, el equipo en el que trabajas y la herramientas que utilizan. Pero cuidado, no tienes por qué seguir a la mayoría. Conocer un nuevo lenguaje te permite tener más habilidades, abrirte a más oportunidades y potenciar tu efectividad.

¿Por qué solemos elegir R para la Ciencia de Datos?

?En general, si trabajas en investigación, estadística o análisis de datos, es probable que prefieras R para transformar tu carrera profesional.

¡R es la lengua franca de las estadísticas y los gráficos! Es más fácil realizar análisis de datos complejos en R. La gran cantidad de paquetes estadísticos te permite realizar cualquier análisis de datos de manera sencilla y rápida, con pocas líneas de código. Ahora mismo R está por delante en este punto. Aquí Hadley Wickham nos lo resume de manera fantástica. ? R no requiere que seas programador. Ni siquiera diría que R es para programadores, es un lenguaje accesible para realizar análisis de datos desde cualquier disciplina. Yo no vengo de una formación en informática ni pensé convertirme en programadora, aprendí R de manera autodidacta desde 2005, cuando incluso la documentación disponible no era tan abundante como lo es hoy en día.

?R y la visualización de datos son una combinación perfecta En términos de visualización de datos, R está muy por delante, ofrece gráficos sorprendentes y sofisticados. Por ejemplo, paquetes como ggplot2 hacen que graficar sea más fácil y más personalizable en R. Otros paquetes de visualización fundamentales son ggplot2, ggvis, googleVis y rCharts. ?R está más actualizado en las últimas técnicas estadísticas, debido a su fuerte vínculo con el entorno académico. El desarrollo de nuevas técnicas estadísticas es más rápido en R. Si existe una técnica estadística, es probable que ya exista un paquete R para implementarla. Habrá momentos en que solo encuentres el análisis estadístico que buscas en R, como ocurre con: • métodos no paramétricos • modelos avanzados para diseños factoriales y ensayos longitudinales, incluidos métodos de medición repetida (WTS y ATS, ART ANOVA), • modelos mixtos (o multinivel) frecuentistas generalizados (y no lineales) donde especificar la estructura de covarianza residual (GLMM, GNLMM), • modelos mixtos para medidas repetidas (MMRM), • modelos de ecuación de estimación generalizada avanzados (GEE), • métodos para la estimación de tamaño muestral en diseños avanzados (y de potencia estadística). ¡Usar R te ahorrará mucho tiempo al no tener que volver a inventar la rueda cada vez que quieras aplicar un análisis! Pero además, R es: ? Gratuito, puedes usarlo sin tener que persuadir a tu jefe de comprar una licencia. ? De código libre y abierto, puedes extenderlos sin tener que pedir permiso. Eso significa que cualquiera puede examinar el código fuente para ver exactamente lo que está haciendo. Y también puedes corregir errores y/o agregar funciones, sin depender del proveedor. Esta naturaleza abierta de R le permite obtener las últimas características más rápido. ? Multiplataforma. Puedes utilizarlo en cualquier sistema operativo: GNU / Linux, Macintosh y Microsoft Windows. ? R tiene una comunidad online muy activa en Ciencia de Datos, que ofrecen soporte a sus usuarios, con perfiles profesionales distintos. Esto es una gran ventaja, ya que te ahorras tener que pagar por acceder a soporte técnico como sí sucede con los softwares comerciales. ¿Quieres leer más sobre R? Aquí tienes la opinión de los profesionales sobre esta poderosa herramienta.

¿Cómo comenzar a aprender sobre IA para R?

Los manuales son buenos libros de referencia y pueden ayudarte a aprender nuevas funciones, pero no son sencillos para los principiantes. Para un principiante, aprender R mediante libros puede ser como intentar aprender lengua con un diccionario, seguramente te sentirás frustrado si son tus primeros pasos. En su lugar te recomiendo: • Encuentra un tutor experimentado que te guíe y obtén una buena capacitación práctica donde veas cómo se usan realmente los lenguajes (o paquetes) para resolver problemas reales. Así estarás bien preparado para el autoaprendizaje más adelante. • Recuerda: no necesitas dominar todas las opciones de una herramienta para poder usarla. No tienes que aprender cada análisis específico desde el principio. Es como cuando comenzamos a hablar, no necesitas conocer todas las reglas gramaticales. ¡Lánzate y juega! • Construye una base sólida, desde los conceptos básicos hasta los modelos lineales (regresión lineal, ANOVA). Una vez que tengas esas habilidades, podrás agregar nuevas habilidades cuando las necesites. No olvides seguir las tendencias de la IA. Por eso te recomiendo nuestro Máster online en Estadística Aplicada a la Ciencia de Datos. Te ofrecemos tutorías individuales ilimitadas, un programa 100% práctico y amplio, con las principales técnicas estadísticas que necesitarás para resolver tus problemas de datos. Ah, y comenzamos desde cero tanto en estadística como en programación, sin calendarios, tú decides cuándo y cómo.

#datascience #machine learning #python #artificial intelligence #programming

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enkeynetwork · 2 years ago

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#acosaservegoogleimage #comefunzionagoogleimage #comeusaretiene.com #googleimage #tiene.com

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mick1031 · 5 years ago

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[R] google Vis를 이용한 다양한 그래프(챠트) demo

[책] R라뷰 : R을 활용한 데이터 분석 입문편, p178~214

-.gogoleVis demo Usage

install.packages("googleVis")

library(googleVis)

?googleVis

demo(WorldBank)

demo(googleVis)

-.chart별 상세 내용은 아래 document 에서 확인 가능 (usage 및 example)

https://cran.r-project.org/web/packages/googleVis/googleVis.pdf

-.googleVis forum(저장소)

https://github.com/mages/googleVis

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hadoopcourse · 5 years ago

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Python vs R: Which is better for data science?

It's a key question for data science professionals, especially those just starting out: Is Python or R best suited for data science?

For those venturing into the world of data science, it is important to master a language first, rather than trying to be an expert in each language. This is because knowing the processes and a technique of data science is what really matters to gain a solid foundation in the world of data science. Learn Python course and its features from our best python training Institute in Bangalore.

So which language to choose?

For years, R was the obvious choice for those starting out in data science, R was designed with statistics in mind, has a long history in the industry, has thousands of public packages, and integrates very well with languages like C, C ++, Java. Launched in 1997, R is common in a wide range of industries and can be found from Wall Street to Silicon Valley as a good alternative to Matlab and SAS. We Prwatech, India’s best R programming training institutes in Bangalore Offering advanced R Programming language course to those technology passionate who wanted to explore & brush up the technical skills from the scratch to advanced level.

On the other hand, Python offers many benefits, which means that an increasing number of people are adopting Python. It is true that Python is challenging the already established position of R as a programming language for data science. Here are some reasons why you can choose Python for data science.

§ Python is easy to use: Python has a reputation for being easy to learn. With readable syntax, Python is ideal for beginners or for data scientists who want to gain knowledge in this language.

§ Python is versatile: As a general-purpose language, Python is a fast and powerful tool that has a lot of capacity. No matter what problem you want to solve, Python can help you carry out the task, thanks to the large number of libraries it has.

§ Python is better at building parsing tools: R and Python are pretty good if you want to parse a dataset, but when it comes to building a web service for others to use the developed methods, Python is the way to go.

§ Data visualization with Python: This is where R generally beats Python. R has a wide range of visualization tools, such as, ggplot2, rCharts and googleVis. Furthermore, although Python does not lend itself naturally to visualization, it does have a wide range of libraries available, such as Matplotlib, Plot.ly and Seaborn.

§ The Python community is growing: Python has a large community, which includes a strong and growing presence in the data science community.

§ Python is better for Deep Learning: There are a lot of packages, like Theano, Keras, and Tensorflow, that make it really easy to create deep neural networks with Python, and although some of these packages are being ported to R, the support available in Python is very higher.

So should you be using python for data science? We consider Python to be a powerful and versatile tool that enables you to do more in less time. R, meanwhile, is a specialized tool, specifically designed for data analysis. In a market where diversification is increasingly becoming a key in development, adding Python to your repertoire will allow you to obtain greater benefits.

#python rprogramming prwatech prwatech2020 softwaretraining trainingcourse

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hk7356241-blog · 6 years ago

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Course In Knowledge Science By TimesPro

Introduction to Data Mining is certainly one of 5 non-credit score courses within the Certification in Practice of Information Analytics (CPDA) program. Individuals with just some days of coaching could have a hard time getting a job. The truth is, one of the job roles of information science is a statistician. I had some foundations in statistics, the corporate I was working for relied on statistical analysis for its core enterprise and I had been given great freedom in studying and experimenting with data. IT professionals planning to change in trending skills of data science like analytics, machine studying and Artificial intelligence.

After having sufficient knowledge concerning the attributes, you'll carry out a predictive activity of classification to foretell whether a person makes over 50K a 12 months or much less through the use of different Machine Learning Algorithms. College students will get to leverage information of all of the core functions of Data Science platform proper from the scratch.

Their information will help one study and perceive the varied particulars of science and how the tasks are performed and what all issues one should be mindful to be successful at one's job. Students can go for on-line coaching, the place R-SAP programs are divided into numerous categories for learners to select from. As soon as the app skeleton is built in shiny, the duty of data evaluation and data visualization can be dispensed to base R, ggplot2, leaflet, googleVis, maps, and many others.

Often, it will get difficult for a company to build predictive fashions, and thus they rely on only visualizing the info for his or her workflow. The variety in my batch helped me a lot in peer studying and networking. As a part of the Data Science Coaching In Hyderabad curriculum students will get to study advanced ideas like Chatbots, Picture Recognition, Text Mining Business Analytics, Predictive Analytics and plenty of more.

The challenging paradigm, the algorithmic modelling tradition, just depends on black bins (the machine learning algorithms) to discover a cheap technique to map a response to an input, as empirically seen on this planet. Based mostly in Bangalore, their Data Science applications imbibes varied information methods using R and Python programming. This tidal wave of knowledge is driving unprecedented demand for these with the talents required to manage and leverage these very large information sets into a competitive advantage.

Business Intelligence analysts would find the developments for a data scientist to construct predictive models upon. You (engineer, enterprise analyst) probably do already a bit of information science work, and know already among the stuff that some information scientists do. It could be easier than you assume to turn into an information scientist. We're a group of information scientists and educators who're on a mission to coach the subsequent technology of analytics practitioners.

A career in analytics and information science are appreciated as the very best-paid job roles in the IT sector. In an ocean of opportunities in the data science area, IMS Proschool presents you the Certificates in information science program which is a distinct segment program focussed on three main profiles particularly data analyst, machine studying and data engineer. to know more about data science training in mumbai .

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mariliadados · 7 years ago

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Exercício — googleVis

Com base nos dados dos governadores eleitos em 2018 para cada Unidade Federativa do Brasil, criei um mapa pela plataforma “GoogleVis”, com cores diferentes para representar cada partido dos candidatos utilizando o R.

read.csv(PARTIDOS) getwd() est <- read.csv(“PARTIDOS.csv”) install.packages(“googleVis”) library(“googleVis”) geo <- gvisGeoChart (est, “UF”,”PARTIDO”, options=list(region=”BR”, displaymode=”regions”, resolution=”provinces”, backgroungcolor=”grey”, colors=”[‘pink’]”) ) plot(geo) View(est)

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just4programmers · 8 years ago

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Introduction to Machine Learning (ML)

Here you will get introduction to machine learning.

Hello there. Many of you must be aware of this term but some might be wondering what the heck is this? Another technical jargon only? Let’s make this simple for you, Machine Learning is made up of two different words Machine and Learning which literally means “making machines learn”. Again how is this possible? We would talk about this later in this very post. Stay tuned.

Image Source

If you eager to know some interesting points about Machine Learning (ML) we’ve got you covered. Let’s dive deeper.

ML is a vast field and very often related with AI (Artificial Intelligence), whereas some people use these two terms interchangeably. But according to data scientists these two are quite distinct from each other in many aspects. In other words ML is a subset of AI.

Real Life Machine Learning (ML) Examples

Example 1:

We all use email services of Gmail on almost regular basis, but have you ever wondered why is there a section named ‘SPAM’ and there exist some mails in it. Here is where ML come into action, with the application of ML Gmail programs it’s product to differentiate between legit and spam mails. Sounds interesting? Let’s see some more examples.

Example 2:

Have you guys ever noticed that after you surf any product selling site you start seeing very similar advertisement across the web? Suppose you surfed a clothing site, right from that moment you will start noticing ads very similar to the product you searched for. This motive of big companies is accomplished by the application of ML only.

Not only these, ML is functional almost everywhere from Facebook to astronomy to predicting your credit score. Though ML practices are not evolved that much yet, but is definitely among one of the hottest topics of the decade. Also the career options in this domain would be supposed to be a wise decision on the basis of current scenario.

Image Source

As we can infer from the image above that the machine is made to learn from ‘experience’, i.e. we feed the machines with bulk of data related to any function/work that we expect it to do. The machine primarily tries to recognize the patterns in the input data and learns the pattern. Later then when machine come across any similar pattern it delivers the intended result.

Let’s understand this with an example, suppose we want to make our machine tell us the breed of the dog when we click a picture of any dog with camera. First we need to train our machine with abundance of dog related data i.e. how a breed looks like, what they eat, height of the breed, friendliness with human etc. The machine try to form some pattern from this data and trains itself from previous experiences. Next time when your machine come across any dog it will be able to tell you the breed (though not 100 percent accurately).

Getting Started with Machine Learning (ML)

As we are already aware about the fact that ML is a subset of AI right? So talking about the Artificial Intelligence, this term is not very new to us. Researches on AI is old thing i.e. scientists were trying to develop an artificial brain since 1940s and 50s which led the foundation of AI. Coming back to ML, it is an advancement in the AI’s domain with possibility of the products like Human Robot, Driverless cars etc.

Let’s have a look on what are the prime contents in ML:

Finding the Dataset

Which language to opt for ML

Development Environment (IDE) for ML

Important Packages & Libraries

Supervised Learning

Classification

Regression

Unsupervised Learning

Clustering

ML Models

Data Mining

Natural Language processing

Note: Apart from these we do have some bonus tips and suggestions for our readers, which will be provide in between the learning process.

The topics mentioned above cover most of the machine learning and are vast enough to accommodate in one, two or three blog posts. So, we will be publishing the posts on regular intervals to let our readers get a grasp over ML. Hope you guys enjoy learning with us. So let’s dive together.

Finding Dataset

Image Source

The very first step in the process of ML is finding a relevant dataset for your machine accompanied by data cleaning and pre-processing. Datasets contain abundant of data as you can see in the example above that are used as experiences for the machine and machine tries to develop some patterns from them.

You can find a dataset according to your needs very easily and essentially for free most of the times. Here are some of the open repositories that we would like to suggest our readers to have their intended datasets.

Data World Repository

UCI Repository

Kaggle Repository

Here we’ve mentioned a few online free repositories where you can find your datasets. You just need to visit the websites and download the required dataset in .csv format.

After successful downloading of the dataset, the data cleaning and pre-processing steps come into consideration which we will be studying in later posts.

Which Language for ML?

We can use any of the language like R, Python, Java, Scala etc. But in this course we will be focusing on one of the procedural language R and one object oriented language Python. Also these two languages are the most beloved and preferred languages among data scientists.

Let’s do a comparative study of R and Python and find out what they are good for and what not:

R Language

Why Good?

In-Depth Statistical Analysis: R being a language designed for statisticians, it is no point denying that R is practically very mush suited for Statistical Analysis. It adds value to your motive whether you are working data derived from sensors from an IOT device or prediction in financial models. Another reason why R is loved by the data scientists is the fact that it contains CRAN repository, which is the house of thousands of outstanding packages to allow for more elaborate analysis and visualization tasks.

High-Quality Imaging: R is a well-known language for producing high quality graphs and charts. The important packages that adds more value to R’s this functionality are ggplot2, googleVis, rCharts There exists a Shiny framework in R which can be used to turn visuals into interactive web applications.

Why not Good?

Learning Ease: R is a language for which it is said that if the programmer have a background in mathematics or statistics it would be pretty easy for him/her to get a grasp of the language otherwise it is more likely to appear counter intuitive.

Processing Large data: The flexibility provided by R when it comes to processing and creation of large-scale data products is not appreciated. Rather the data scientists prefer to go with languages like Python or Java when actual product is to be made.

Performance: When compared with other languages, the performance delivered by R is not up to the mark because R was designed with data scientists in mind, not the computers. It is observed that R is relatively slower than Java or Python.

Python Language

Why Good?

Smooth Workflow: Python provides a workflow integration and is thus popular among the developers and data scientists when it comes for applying statistical techniques or when these tasks need to be integrated with web apps or production environments. In order to manage their entire data-related workflow data scientists choose Python as their first priority.

Beneficial in ML: Various libraries that is being provided by python like Scikit-learn, Tensorflow, Pandas, Numpy, PyBrain etc. and a flexibility of python makes this language suitable for application of ML techniques and developing sophisticated models and prediction engines.

Why not Good?

Not suitable for specialized data tasks: Though Python is well known for it’s flexibility but there are still hundreds of such R packages that do not have equivalent Python substitutes. If very specific tasks have to be done, R is preferred over Python.

So, in this blog post we’ve covered a few topics pertaining to ML and we will be learning all of the remaining topics in the later posts of this course. Hope you guys enjoying learning with us. Stay tuned for more blog posts like this.

Comment below if you have any queries related to above introduction to machine learning.

The post Introduction to Machine Learning (ML) appeared first on The Crazy Programmer.

#The Crazy Programmer

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alinnorugo-blog · 8 years ago

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Why R Has Become The Programming Choice For Data Scientists

A recent report by Institute of Electrical and Electronics Engineers(IEEE) stated that R programming language - the king of statistical computing languages for analyzing and visualizing big data takes 5th place in “The 2017 Top Ten Programming Languages”, this is an extraordinary result for a domain-specific language. The other four languages in the top 5 (C, Java, Python and C++) are all general-purpose languages, suitable for just about any programming task. R by contrast is a language specifically for data science, and its high ranking here reflects both the critical importance of data science as a discipline today, and of R as the language of choice for data scientists.

So why has R programming language soared in popularity in recent years? Well, it depends on who you ask. The answer varies, for example IEEE Spectrum ranks languages according to a large number of factors, including search rankings and trends, social media mentions, and job posting while statisticians say its because of it's rich packages.

Here are my top 5 reasons why R has grown in popularity

1) R language is Free

R is an open source programming language-free for anyone to use. R language code can be executed on all platforms Windows, Mac, or Linux. Data geeks can inspect R language code and play with it as much as they want without having to bother about user limits, subscription cost and license management. The programming libraries are free to access, however there are certain commercial libraries that are meant for organizations that often deal with data in the terabyte range.

2) Large ecosystem

R has a rich ecosystem of cutting-edge packages and a large active community. Packages are available at CRAN, BioConductor and Github. You can search through all R packages at Rdocumentation.

3) Lingua franca for data science

R is developed by statisticians for statisticians. They can communicate ideas and concepts through R code and packages, you don’t necessarily need a computer science background to get started. Furthermore, it is increasingly adopted outside of academia.

4) Visualizations

Visualized data can often be understood more efficiently and effectively than the raw numbers alone. R and visualization are a perfect match. Some must-see visualization packages are ggplot2, ggvis, googleVis and rCharts.

5) Ease Of Use Beyond popularity, another reason that R is an excellent data science programming language is that it is easy to download and install, and intuitive when in use.

#data science #R

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cristian-randieri · 8 years ago

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Tweeted

Data visualization with googleVis exercises part 9 - https://t.co/QUzHAqS453

— Cristian Randieri (@C_Randieri) July 31, 2017

#IFTTT #Twitter

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simplystatistics · 13 years ago

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Sunday Data/Statistics Link Roundup (9/9/12)

Not necessarily statistics related, but pretty appropriate now that the school year is starting. Here is a little introduction to "how to google" (via Andrew J.). Being able to "just google it" and find answers for oneself without having to resort to asking folks is maybe the #1 most useful skill as a statistician.

A really nice presentation on interactive graphics with the googleVis package. I think one of the most interesting things about the presentation is that it was built with markdown/knitr/slidy (see slide 53). I am seeing more and more of these web-based presentations. I like them for a lot of reasons (ability to incorporate interactive graphics, easy sharing, etc.), although it is still harder than building a Powerpoint. I also wonder, what happens when you are trying to present somewhere that doesn't have a good internet connection?

We talked a lot about the ENCODE project this week. We had an interview with Steven Salzberg, then Rafa followed it up with a discussion of top-down vs. bottom-up science. Tons of data from the ENCODE project is now available, there is even a virtual machine with all the software used in the main analysis of the data that was just published. But my favorite quote/tweet/comment this week came from Leonid K. about a flawed/over the top piece trying to make a little too much of the ENCODE discoveries: "that's a clown post, bro".

Another breathless post from the Chronicle about how there are "dozens of plagiarism cases being reported on Coursera". Given that tens of thousands of people are taking the course, it would be shocking if there wasn't plagiarism, but my guess is it is about the same rate you see in in-person classes. I will be using peer grading in my course, hopefully plagiarism software will be in place by then.

A New York Times article about a new book on visualizing data for scientists/engineers. I love all the attention data visualization is getting. I'll take a look at the book for sure. I bet it says a lot of the same things Tufte said and a lot of the things Nathan Yau says in his book. This one may just be targeted at scientists/engineers. (link via Dan S.)

Edo and co. are putting together a workshop on the analysis of social network data for NIPS in December. If you do this kind of stuff, it should be a pretty awesome crowd, so get your paper in by the Oct. 15th deadline!

#googleVis #visualization #markdown #knitr #encode #salzberg #coursera #education

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