The Online Books Page

Job Control Language (Computer program language)

Here are entered works on the specific command language called JCL, or Job Control Language. Works on computer program languages that use commands to communicate with computer operating systems or other programs, and that are sometimes referred to as job control languages, are entered under Command languages (Computer science).

See also what's at Wikipedia, your library, or elsewhere.

• Command languages (Computer science)
• Domain-specific programming languages

• Job Control Language (Computer program language) -- Programmed instruction

• JCL (Computer program language)

• Introduction to the IBM 360 computer and OS/JCL (job control language) (Survey Research Center, University of Michigan, 1971), by Judith Rattenbury (page images at HathiTrust; US access only)

• Computer Center training program : introduction to Job Control Language. (NIH Computer Center, National Institutes of Health, 1984), by National Institutes of Health (U.S.). Division of Computer Research and Technology. Computer Center (page images at HathiTrust)

Items below (if any) are from related and broader terms.

• Data Science at the Command Line (free online edition; originally published 2014), by Jeroen Janssens (illustrated HTML at datascienceatthecommandline.com)

• GAWK: Effective Awk Programming (online edition, 2016), by Arnold Robbins and Free Software Foundation (Cambridge, Mass.) (multiple formats at gnu.org)

• AppleScript Finder Guide: English Dialect, by Apple Computer, Inc. (PDF at Apple)
• AppleScript Language Guide (revised for OS X 10.5), by Apple Computer, Inc. (HTML at Apple)
• AppleScript Scripting Additions Guide: English Dialect, by Apple Computer, Inc. (PDF at Apple)

• Learning JavaScript Design Patterns (free online version 1.7, c2017), by Addy Osmani (HTML with commentary at addyosmani.com)
• Eloquent JavaScript: A Modern Introduction to Programming (second edition, 2015), by Marijn Haverbeke (illustrated HTML with commentary at eloquentjavascript.net)
• Speaking JavaScript: An In-Depth Guide for Programmers (originally published 2014), by Axel Rauschmayer (HTML with commentary at speakingjs.com)
• Developing Backbone.js Applications (originally published 2013), by Addy Osmani (HTML with commentary at addyosmani.com)
• Customizing Vendor Systems for Better User Experiences: The Innovative Librarian's Guide (Santa Barbara and Denver: Libraries Unlimited, c2016), by Matthew Reidsma (PDF at gvsu.edu)
• D3.js Tips and Tricks, by Malcolm Maclean (JavaScript-dependent illustrated HTML at leanpub.com)
• JavaScript Enlightenment (online version for ES2015 and later), by Cody Lindley (HTML at frontendmasters.com)
• JavaScript Guide (current online edition), by MDN Web Docs (HTML at mozilla.org)
• You Don't Know JS (online prepublication editions of booklets in this series), by Kyle Simpson (illustrated HTML with commentary at GitHub)

• D3 Tutorials (original version of Interactive Data Visualization for the Web, which is no longer free online), by Scott Murray (illustrated HTML at alignedleft.com)

• MUMPS language standard (U.S. Dept. of Commerce, National Bureau of Standards : for sale by the Supt. of Docs., U.S. Govt. Print. Off., 1976), by MUMPS Development Committee, Joseph T. O'Neill, and National Center for Health Services Research (page images at HathiTrust)
• MUMPS. (U.S. Dept. of Commerce/National Bureau of Standards ;, 1986), by United States. National Bureau of Standards (page images at HathiTrust)

• Power Programming With Mathematica: The Kernel (authorized scan; New York et al.: McGraw-Hill, c1996), by David B. Wagner (PDF with commentary at stackexchange.com and dropbox.com)

• Efficient R Programming (Creative Commons online edition, 2021), by Colin Gillespie and Robin Lovelace (illustrated HTML with commentary at github.io)
• Introduction to Data Science: Data Analysis and Prediction Algorithms With R (Creative Commons online edition, 2021), by Rafael A. Irizarry (illustrated HTML with commentary at github.uio)
• Text Mining with R: A Tidy Approach (Creative Commons online edition; ca. 2019), by Julia Silge and David Robinson (illustrated HTML with commentary at tidytextmining.com)
• The R Inferno (2011), by Patrick Burns (PDF with commentary at burns-stat.com)
• An Introduction to R: Notes on R, A Programming Environment for Data Analysis and Graphics (electronic edition, 2008), by W. N. Venables, D. M. Smith, and R Development Core Team
  • HTML at r-project.org
  • PDF at r-project.org
• Statistical Inference via Data Science: A ModernDive into R and the Tidyverse (Creative Commons online edition, 2021), by Chester Ismay and Albert Young-Sun Kim (illustrated HTML with commentary at moderndive.com)
• R for Data Science (prepublication version, c2016), by Hadley Wickham and Garrett Grolemund (HTML with commentary in New Zealand)
• Time Series Analysis and its Applications, With R Examples (authors' free electronic edition, similar to published fourth edition; 2016), by Robert H. Shumway and David S. Stoffer (PDF with commentary at Pitt)
• Practical Regression and Anova using R (final web version, 2002), by Julian James Faraway (PDF with commentary at r-project.org)
• An Introduction to Statistical Learning, With Applications in R (corrected first edition and second edition; 2017 and 2021), by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani (PDF with commentary at statlearning.com)
• Learning Statistics With R, by Danielle Navarro (multiple formats with commentary at learningstatisticswithr.com)

• Data Science: A First Introduction (Boca Raton: CRC Press, 2022), by Tiffany-Anne Timbers, Trevor Campbell, and Melissa Lee (illustrated HTML with commentary at datasciencebook.ca)