R is the world's most popular language for developing statistical software: Archaeologists use it to track the spread of ancient civilizations, drug companies use it to discover which medications are safe and effective, and actuaries use it to assess financial risks and keep economies running smoothly.
The Art of R Programming takes you on a guided tour of software development with R, from basic types and data structures to advanced topics like closures, recursion, and anonymous functions. No statistical knowledge is required, and your programming skills can range from hobbyist to pro.
Norman Matloff is a professor of computer science (and was formerly a professor of statistics) at the University of California, Davis. His research interests include parallel processing and statistical regression, and he is the author of a number of widely-used Web tutorials on software development. He has written articles for the New York Times, the Washington Post, Forbes Magazine, and the Los Angeles Times, and is the co-author of The Art of Debugging (No Starch Press).
Earning a Google Data Analytics Professional Certificate or IBM Data Analyst Professional Certificate gives you a framework for learning a statistical programming language within the greater context of data analysis. The Google certificate teaches R, and the IBM certificate teaches Python. Both include other job-ready skills, like SQL, spreadsheets, and data visualization. Not only can you learn to program, you can learn how all these critical data skills work together.
In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.
Substantive questions in empirical scientific and policy research are often causal. Does voter outreach increase turnout Are job training programs effective Can a universal health insurance program improve people's health This class will introduce students to both statistical theory and practice of causal inference. As theoretical frameworks, we will discuss potential outcomes, causal graphs, randomization and model-based inference, sensitivity analysis, and partial identification. We will also cover various methodological tools including randomized experiments, regression discontinuity designs, matching, regression, instrumental variables, difference-in-differences, and dynamic causal models. The course will draw upon examples from political science, economics, education, public health, and other disciplines. Download the syllabus and the course materials.
In this course, we consider ways to illustrate compelling storieshidden in a blizzard of data. Equal parts art, programming, andstatistical reasoning, data visualization is a critical tool foranyone who seeks to analyze data. In recent years, data analysisskills have become essential for those pursuing careers in policyadvocacy and evaluation, business consulting and management, oracademic research in the fields of education, health, medicine, andsocial sciences. This course introduces students to the powerful Rprogramming language and the basics of creating data-analysis graphicsin R. From there, we use real datasets to explore topics ranging fromnetwork data (like social interactions on Facebook or trade betweencounties) to geographical data (like county-level election returns inthe US or the spatial distribution of insurgent attacks inAfghanistan). No prior background in statistics or programming isrequired or expected. Download the syllabus.
What accounts for who votes and their choice of candidate Would universal health insurance improve the health of the poor Researchers and policy makers use statistics to answer these questions. However, the validity of their conclusions depends upon underlying assumptions and correct application of statistical methods. The course will introduce basic principles of statistical inference and programming skills for data analysis. The goal is to provide students with the foundation necessary to analyze data in their independent research at Princeton and to become a critical consumer of news articles and academic studies that use statistics. Download the syllabus and the course materials.
In this course, students will learn basic research design and data analysis methodology in empirical social science research. The main goal is to learn how statistical theory can be used to make causal inferences in experimental and observational studies. The course satisfies the analytical methods requirement for politics majors. The materials of this course are particularly useful for those who plan to use quantitative analysis in their junior papers and senior thesis as well as for those who wish to apply for graduate programs in the social sciences. Familiarity with elementary probability theory is helpful, but is not required. Download the syllabus.
This course is the first course in applied statistical methodsfor social scientists. Students will learn how statistical methods canbe used to conduct causal inferences, exploratory data analysis,forecasting, and hypothesis testing. The first half of the course willbe devoted to probability theory, which serves as a foundation ofstatistical theory. The second half covers the linear model in somedepth and if time permits also introduces generalized linear models.An emphasis of the course is given to practical data analysis, andstudents will learn statistical programming as well as basicprinciples of probability theory and statistical inference. Thiscourse assumes the mathematical knowledge taught in POL 502, andprepares students for the next course in the sequence, POL 572.Download the syllabus andhandouts.
This course is the first course in applied statistical methods for social scientists. Students will learn a variety of basic cross-section regression models (as time permits!) including linear regression model, discrete choice models, duration (or hazard) models, event count models, structural equation models, and others. Unlike traditional courses on applied regression modeling, I will emphasize the connections between these methods and causal inference, which is the primary goal of social science research. Prerequisites, POL 502 and POL 571. Download the syllabus and handouts.
This course is the second course in applied statistical methods for social scientists. Building on the materials we covered in POL 572 or its equivalent (i.e., linear regression, structural equation modeling, instrumental variables, maximum likelihood estimation, discrete choice models), students will learn a variety of statistical methods including models for longitudinal data and survival data. Unlike traditional courses on applied regression modeling, I will emphasize the connections between these methods and causal inference, which is the primary goal of social science research. Download the syllabus and handouts.
The main goal of this course is to help students to write a publishable paper that uses advanced statistical methods. At the beginning of the semester, I will give brief introductory lectures on causal inference and applied Bayesian statistics to cover the fundamentals. Thereafter the materials covered will focus on the statistical methods appropriate for the projects selected by students. Download the syllabus.
This course presents basic principles of mathematical probability and statistics that are essential for advanced quantitative analysis in political science research. The first half of the course will cover basic probability theory, which serves as a foundation of statistical theory. The second half will be devoted to topics for statistical inference, which include estimation, hypothesis testing, asymptotic analysis and regression. Students are expected to complete twelve problem sets, one for each topic, each of which consists of four or five exercises. Download the syllabus
IBM SPSS Statistics is a powerful statistical software platform. It offers a user-friendly interface and a robust set of features that lets your organization quickly extract actionable insights from your data. Advanced statistical procedures help ensure high accuracy and quality decision making. All facets of the analytics lifecycle are included, from data preparation and management to analysis and reporting.
The data API provides programmatic access to BEA published economic statistics using industry-standard methods and procedures. BEA's data API includes methods for retrieving a subset of our statistical data and the meta-data that describes it.
The initial version of R was released in 1995 to allow academic statisticians and others with sophisticated programming skills to perform complex data statistical analysis and display the results in any of a multitude of visual graphics. The \"R\" name is derived from the first letter of the names of its two developers, Ross Ihaka and Robert Gentleman, who were associated with the University of Auckland at the time.
Because it's been around for many years and has been popular throughout its existence, the language is fairly mature. Users can download add-on packages that enhance the basic functionality of the language. These packages enable users to visualize data, connect to external databases, map data geographically and perform advanced statistical functions. There is also a popular user interface called RStudio, which simplifies coding in the R language. 59ce067264