Syllabus Lectures: none (directed reading course) Textbook: Class Notes (see references below) References: A Gentle Introduction to Correspondence Analysis There are some digital humanists who are competent mathematicians, but most of us experience some anxiety about the more advanced mathematics involved in the text analysis methodologies that we use. Dammit Jim, I’m a humanist, not a mathematician! The problem of course is that there are clearly some statistical and graphical techniques that can be very powerful for humanities research (if you’re unconvinced by this claim, please read on anyway). So one faces a choice: not using these techniquesusing these techniques naïvely and trusting that they’re working properly and that one is interpreting the results properlyinvesting a ton of time learning the mathematics involved, sometimes to the detriment of the original research agendacollaborating with someone who does understand the mathematics Correspondence Analysis is a good example of a technique that can appear very intimidating but that can also be a very powerful tool in the arsenal of a digital humanist.
Beginning Python Visualization We are visual animals. But before we can see the world in its true splendor, our brains, just like our computers, have to sort and organize raw data, and then transform that data to produce new images of the world. Beginning Python Visualization: Crafting Visual Transformation Scripts discusses turning many types of small data sources into useful visual data. And, you will learn Python as part of the bargain. What you’ll learn
Forecasting: principles and practice Welcome to our online textbook on forecasting. This textbook is intended to provide a comprehensive introduction to forecasting methods and to present enough information about each method for readers to be able to use them sensibly. We don’t attempt to give a thorough discussion of the theoretical details behind each method, although the references at the end of each chapter will fill in many of those details. The book is written for three audiences: (1) people finding themselves doing forecasting in business when they may not have had any formal training in the area; (2) undergraduate students studying business; (3) MBA students doing a forecasting elective. We use it ourselves for a second-year subject for students undertaking a Bachelor of Commerce degree at Monash University, Australia. For most sections, we only assume that readers are familiar with algebra, and high school mathematics should be sufficient background.
Speaker Videos The first video covers introductions from the workshop’s sponsors MITH and the NEH ODH, via Neil Fraistat, Jen Guiliano, and Jen Serventi; Matthew Jockers presenting on literary topic modeling with “Thematic Change and Authorial Innovation in the 19th Century Novel”; and Robert Nelson on historical topic modeling with”Analyzing Nationalism and Other Slippery ‘Isms’”. Topic Modeling Workshop: Jockers and Nelson from MITH in MD on Vimeo. The second video covers Jo Guldi and Christopher Johnson-Roberson’s presentation “Paper Machines: A Tool for Analyzing Large-Scale Digital Corpora” Topic Modeling Workshop: Guldi and Johnson-Roberson from MITH in MD on Vimeo. The third video covers David Mimno’s presentation “The details: how we train big topic models on lots of text”. Topic Modeling Workshop: Mimno from MITH in MD on Vimeo.
10 places where anyone can learn to code Teens, tweens and kids are often referred to as “digital natives.” Having grown up with the Internet, smartphones and tablets, they’re often extraordinarily adept at interacting with digital technology. But Mitch Resnick, who spoke at TEDxBeaconStreet, is skeptical of this descriptor. Sure, young people can text and chat and play games, he says, “but that doesn’t really make you fluent.” Mitch Resnick: Let's teach kids to code Fluency, Resnick proposes in this TED Talk, comes not through interacting with new technologies, but through creating them. The former is like reading, while the latter is like writing. Mann-Whitney-Wilcoxon Test Two data samples are independent if they come from distinct populations and the samples do not affect each other. Using the Mann-Whitney-Wilcoxon Test, we can decide whether the population distributions are identical without assuming them to follow the normal distribution. Example In the data frame column mpg of the data set mtcars, there are gas mileage data of various 1974 U.S. automobiles. > mtcars$mpg  21.0 21.0 22.8 21.4 18.7 ... Meanwhile, another data column in mtcars, named am, indicates the transmission type of the automobile model (0 = automatic, 1 = manual).
TxDHC Webinar Series The TxDHC hosts a webinar 2-3 times per semester. The webinars are open to all and are announced via the TxDHC website and listserv. We simply ask that you register beforehand. 27 Ways to Learn to Program Online Whether you are looking to switch careers and become a full-time programmer, want to try to build a website or app on the side, or are just looking to round out your skill set, learning to code has certainly been something a lot of people have started to do lately. And while being a programmer might not be for everyone, there is a lot to be said about gaining a better, more educated view of how all those pixels get moved around all those screens. Before we delve into our list of learning resources sites, we wanted to share some advice from Marissa Louie, a self-taught product designer for Ness Computing. A former startup founder, Louie told TNW that the hardest part of being self-taught – whether it’s design, programming, or any other discipline is, “gathering the courage. The most important barrier is just to overcome your fears” (she also said having the ability to follow instructions helps as well). F**k it, we'll do it live!
Data Mining With R: TIME SERIES using R FITTING ARIMA MODEL in RAuto Regressive Integrated Moving Average ( ARIMA) model is generalisation of Auto Regressive Moving Average Model (ARMA) and used to predict future points in Time series.But what is time series , Acc to google "A time series is a sequence of data points, typically consisting of successive measurements made over a time interval. Examples of time series are ocean tides, counts of sunspots, and the daily closing value of the Dow Jones Industrial Average." and more in a general way it is a series of values of a quantity obtained at successive times, often with equal intervals between them.ARIMA models are defined for stationary time series , stationary time series is one whose mean, variance, autocorrelation are all constant over time. But for checking that the time series is stationary or not , we have several statistical tests for them namely.