Topic: Programming

Description: This course is an introduction to scientific computing in Julia for those who have some experience programming in other languages. Basic concepts of Julia and its scientific stack will be introduced. We will compare syntax and data structures in Julia with those of other scientific computing languages, and demo how to install Julia packages and how to run simple Julia scripts on Biowulf.

Expected knowledge: Some programming experience in a scientific computing language (Matlab, Python, R). Familiarity with the Linux/Unix command line.

Instructor: Antonio Ulloa (NIH HPC Staff)

The class is free but registration is required.
You can register at https://hpc.nih.gov/nih/classes/

Please contact staff@hpc.nih.gov with any questions about the NIH HPC Systems

Register 

This is the first class in the NIH Library Introduction to R Series. This class provides a basic overview of the functionality of RStudio, including how to import data, create scripts, and run code. R is a programming language and open source environment for statistical computing and graphics. The R class series is a comprehensive collection of training sessions offered by the NIH Library Data Services and Bioinformatics programs that is designed to teach non-programmers how to write modular code and to introduce best practices for using R for data analysis and data visualization. Each class uses both evidence-based best practices for programming and practical hands-on lessons.

By the end of this class, students should be able to: list reasons for using R; describe the purpose of the RStudio Script, Console, Environment, and Plots panes; describe the various methods for finding help on R and RStudio; create a script in R and run code from a script and from the console; define the following terms as they relate to R: object, assign, call, function, and arguments; assign values to objects in R; learn about R objects; describe the major R data types; understand how to create and work with R vectors; describe the steps for importing data into R.

Students are encouraged to install R and RStudio before the class so that they can follow along with the instructor. Attendees will need to download the data before the class. Please bring your laptop with R and RStudio installed.

Registration is required. Register at this link.

Before the workshop: We will use Google Colab for this workshop. Colab allows us to write and execute Python code through the browser. A Google Account is required for using Colab. If you do not have one, please create a Google Account before the workshop. For technical assistance, contact NCI technical support at https://service.cancer.gov/ncisp.

NOTE: A one-hour help session will be offered on April 23, 11 AM – 12 PM: Getting Started with Google Colab

Python is a good programming language for scientists to solve a wide variety of biological problems. We find that many scientists who come to Software Carpentry workshops use Python and want to learn more.

If you are a novice and want to learn how to program in Python to help you in your work, please join our NEW six-week series of two-hour workshops! Instructors will use live code and you will follow along on your own computer. You will gain a strong foundation in the fundamentals of Python along with best practices for scientific computing: breaking down analyses into modular units, task automation, and encapsulation.

NOTE: This is a six-part course, held weekly for 6 weeks from 11 am – 1 pm, with a 20-minute lunch break. When you register, you are registering for all 6 weeks. Please make sure you have time in your schedule to commit to all six sessions!

Presenters: Pinyi Lu, PhD, Bioinformatics Analyst; Robin Kramer, MS, Bioinformatics Analyst; and George Zaki, PhD, Bioinformatics Manager, Frederick National Laboratory for Cancer Research (FNLCR)

Questions? Contact the NCI Data Science Learning Exchange

Register

Session Description
This is the second class in the NIH Library Introduction to R Series. A basic understanding of R and RStudio is expected. This class provides a basic overview of R data types, data frames, and factors. Additionally, this class will cover indexing and subsetting data frames, and dealing with missing data. R is a programming language and open source environment for statistical computing and graphics. The R class series is a comprehensive collection of training sessions offered by the NIH Library Data Services program that is designed to teach non-programmers how to write modular code and to introduce best practices for using R for data analysis and data visualization. Each class uses both evidence-based best practices for programming and practical hands-on lessons.

By the end of this class, participants should be able to: define R data frames; characterize how to inspect data frames; list the major methods for describing the content and structure of data frames; illustrate how to index and subset a data frame; describe how to use comparison operators on a data frame; discuss R factors; describe how to convert R factors; describe how to rename factors; discuss options for dealing with missing data in R; and describe how to save data in R.

Participants are encouraged to install R and RStudio before the class so that they can follow along with the instructor. Attendees will need to download the class data before the class.

Registration is required. Register at this link.

Python is a good programming language for scientists to solve a wide variety of biological problems. We find that many scientists who come to Software Carpentry workshops use Python and want to learn more.

If you are a novice and want to learn how to program in Python to help you in your work, please join our NEW six-week series of two-hour workshops! Instructors will use live code and you will follow along on your own computer. You will gain a strong foundation in the fundamentals of Python along with best practices for scientific computing: breaking down analyses into modular units, task automation, and encapsulation.

NOTE: This is a six-part course, held weekly for 6 weeks from 11 am – 1 pm, with a 20-minute lunch break. When you register, you are registering for all 6 weeks. Please make sure you have time in your schedule to commit to all six sessions!

Presenters: Pinyi Lu, PhD, Bioinformatics Analyst; Robin Kramer, MS, Bioinformatics Analyst; and George Zaki, PhD, Bioinformatics Manager, Frederick National Laboratory for Cancer Research (FNLCR)

Questions? Contact the NCI Data Science Learning Exchange

Registration is required. Register at this link.

Python is a good programming language for scientists to solve a wide variety of biological problems. We find that many scientists who come to Software Carpentry workshops use Python and want to learn more.

If you are a novice and want to learn how to program in Python to help you in your work, please join our NEW six-week series of two-hour workshops! Instructors will use live code and you will follow along on your own computer. You will gain a strong foundation in the fundamentals of Python along with best practices for scientific computing: breaking down analyses into modular units, task automation, and encapsulation.

NOTE: This is a six-part course, held weekly for 6 weeks from 11 am – 1 pm, with a 20-minute lunch break. When you register, you are registering for all 6 weeks. Please make sure you have time in your schedule to commit to all six sessions!

Presenters: Pinyi Lu, PhD, Bioinformatics Analyst; Robin Kramer, MS, Bioinformatics Analyst; and George Zaki, PhD, Bioinformatics Manager, Frederick National Laboratory for Cancer Research (FNLCR)

Questions? Contact the NCI Data Science Learning Exchange

Register

Session Description
Data Wrangling in R is the third class in the NIH Library Introduction to R Series. A basic understanding of R and R Data Types is expected. This class provides a basic overview of manipulating, analyzing and exporting data with the R tidyverse. R is a programming language and open source environment for statistical computing and graphics. The R class series is a comprehensive collection of training sessions offered by the NIH Library Data Services Program that is designed to teach non-programmers how to write modular code and to introduce best practices for using R for data analysis and data visualization. Each class uses both evidence-based best practices for programming and practical hands-on lessons.

By the end of this class, students should be able to: describe the purpose of Tidyverse packages; select certain columns or rows in a data frame; describe the function of the pipe operator; add new columns to a dataframe that are functions of existing columns; use the split-apply-combine concept for data analysis; use summarize, group by, and count to split a data frame into groups of observations, apply summary statistics for each group, and then combine the results; describe the concept of a wide and a long table format and for which purpose those formats are useful; describe the function of key-value pairs; reshape a data frame using the gather commands from the tidyr package; export a data frame to a .csv file.

Students are encouraged to install R and RStudio and download the class data before the class so that they can follow along with the instructor. Attendees will need to download the class data before the class.

Registration is required. Register at this link.

Python is a good programming language for scientists to solve a wide variety of biological problems. We find that many scientists who come to Software Carpentry workshops use Python and want to learn more.

If you are a novice and want to learn how to program in Python to help you in your work, please join our NEW six-week series of two-hour workshops! Instructors will use live code and you will follow along on your own computer. You will gain a strong foundation in the fundamentals of Python along with best practices for scientific computing: breaking down analyses into modular units, task automation, and encapsulation.

NOTE: This is a six-part course, held weekly for 6 weeks from 11 am – 1 pm, with a 20-minute lunch break. When you register, you are registering for all 6 weeks. Please make sure you have time in your schedule to commit to all six sessions!

Presenters: Pinyi Lu, PhD, Bioinformatics Analyst; Robin Kramer, MS, Bioinformatics Analyst; and George Zaki, PhD, Bioinformatics Manager, Frederick National Laboratory for Cancer Research (FNLCR)

Questions? Contact the NCI Data Science Learning Exchange

Registration is required. Register at this link.

Python is a good programming language for scientists to solve a wide variety of biological problems. We find that many scientists who come to Software Carpentry workshops use Python and want to learn more.

If you are a novice and want to learn how to program in Python to help you in your work, please join our NEW six-week series of two-hour workshops! Instructors will use live code and you will follow along on your own computer. You will gain a strong foundation in the fundamentals of Python along with best practices for scientific computing: breaking down analyses into modular units, task automation, and encapsulation.

NOTE: This is a six-part course, held weekly for 6 weeks from 11 am – 1 pm, with a 20-minute lunch break. When you register, you are registering for all 6 weeks. Please make sure you have time in your schedule to commit to all six sessions!

Presenters: Pinyi Lu, PhD, Bioinformatics Analyst; Robin Kramer, MS, Bioinformatics Analyst; and George Zaki, PhD, Bioinformatics Manager, Frederick National Laboratory for Cancer Research (FNLCR)

Questions? Contact the NCI Data Science Learning Exchange

Registration is required. Register at this link.

Python is a good programming language for scientists to solve a wide variety of biological problems. We find that many scientists who come to Software Carpentry workshops use Python and want to learn more.

If you are a novice and want to learn how to program in Python to help you in your work, please join our NEW six-week series of two-hour workshops! Instructors will use live code and you will follow along on your own computer. You will gain a strong foundation in the fundamentals of Python along with best practices for scientific computing: breaking down analyses into modular units, task automation, and encapsulation.

NOTE: This is a six-part course, held weekly for 6 weeks from 11 am – 1 pm, with a 20-minute lunch break. When you register, you are registering for all 6 weeks. Please make sure you have time in your schedule to commit to all six sessions!

Presenters: Pinyi Lu, PhD, Bioinformatics Analyst; Robin Kramer, MS, Bioinformatics Analyst; and George Zaki, PhD, Bioinformatics Manager, Frederick National Laboratory for Cancer Research (FNLCR)

Questions? Contact the NCI Data Science Learning Exchange