"Statistical computing"" is sometimes defined interchangeably with "computational statistics" to describe the interface of statistics and computer science.

A proposed distinction between these terms defines:

• "Computational statistics" as the application of computer science to statistics

• "Statistical computing" as the design of computational algorithms for implementing methods and the designing methods that harness the power of computing to answer statistical questions.

This course will be organized around a broader view of statistical computing.

This course will take a broader view of statistical computing that encompasses the many ways - both routine and specialized - that computers are used by statistical analysts and data scientists.

This broad view encompasses but is not limited to:

• Managing, obtaining, and organizing data

• Data exploration and visualization

• Using statistical software for data analysis

• Reproducible reporting and presentation of analyses

• Computationally intensive methods in statistics and data science

Course Website: www.jbhender.github.io/Stats506

General computing skills: Linux shell, git, markdown

Scripting Languages: R, Stata, SAS

"Advanced" R: dplyr, package structure and development, Rcpp

Other languages: C++, C, SQL, html

High performance, parallel, distributed, and asynchronous computing

Computational algorithms: cross-validation, bootstrap re-sampling, permutation testing, Monte Carlo estimation, simulation studies, optimization.

Reading assignments and quizzes

First "quiz" is actually a survey and due this Thursday.

For future readings I will always give you at least five days and usually a week or more to complete them.

There are two optional texts for this course, both on using R.

Neither text is required to complete problem sets or quizzes, but I will occasionally include optional readings from these texts to supplement course material.

The Art of R Programming, by Norman Matloff, is recommended for those with little to no previous experience in R.

Advanced R, by Hadley Wickham, is for those who would like to develop a deep understanding of R and its inner workings.

Why will we focus on R, SAS, and Stata?

These appear to be the most in demand in the job market for statistical analysts.

For data science jobs, R and Python appear to be the most in demand.

Other courses devoted exclusively to Python.

In The Popularity of Data Science Software, Robert Muenchen presents analyses measuring the popularity of various software in job postings and academic articles.

The plots on the next five slides are screen shots taken from the article.

R, SAS, and Stata are examples of domain specific languages used primarily for statistics and data analysis.

SQL or 'structured query language' is a specialized language for querying databases.

Python is a general purpose scripting language with a number of libraries for math, statistics, and data analysis including: numpy, panda, statsmodels, and sckitlearn.

System and application languages such as C/C++, Java, and go can be used to produce very high performance code – but have a larger learning and development curve.

On the lined side of the note card, please write:

• Your full name
• The name by which you prefer to be called and a phonetic rendering if potentially difficult for me to pronounce
• Your uniqname.

On the unlined side please write just the name by which you prefer to be called.

Note cards will sometimes be used to call on students to answer questions in class.