Scientific Programming: Part A - Programming

General information

Degrees:	Quantitative and Computational Biology, Data Science
Period:	September - October

Objectives

Computer pro­ces­sing is a key com­po­nent of modern data ana­ly­sis pipe­li­nes, and plays an increa­sin­gly impor­tant role in many fields of scien­ce. The goal of the fir­st part is to intro­du­ce the Python pro­gram­ming lan­gua­ge, one of the most wide­ly used scien­ti­fic com­pu­ting lan­gua­ges, and to a col­lec­tion of libra­ries that can be used to ana­ly­ze data.

At the end of the modu­le, stu­den­ts are expec­ted to:

• Remember the syn­tax and seman­tics of the Python lan­gua­ge;
• Understand pro­grams writ­ten by others indi­vi­duals;
• Analyze a sim­ple data ana­ly­sis task and refor­mu­la­te it as a pro­gram­ming pro­blem;
• Evaluate which fea­tu­res of the lan­gua­ge (and rela­ted scien­ti­fic libra­ries) can be used to sol­ve the task;
• Construct a Python pro­gram that appro­pria­te­ly sol­ves the task;
• Evaluate the results of the program.

Course Information

Instructor:	Andrea Passerini Email:
Teaching assistants:	Erik Dassi (QCB) Email: erik.dassi [guess what] unitn.it Antonio Longa (Data Science) Email: antonio.longa [guess what] unitn.it
Tutors:	TBA
Office hours:	Please write an email to arrange a call.
Lecture time:	Monday 14.30-16.30 (lecture, room a107) Tuesday 16.30-18.30 (QCB lab, room a201) Tuesday 16.30-18.30 (DS lab, room a202) Thursday 11.30-13.30 (lecture, room b108) Friday 15.30-17.30 (QCB lab, room a201) Friday 15.30-17.30 (DS lab, room b106)
Communications:	Please check the moodle page of the course for news and updates.
Bibliography:	Allen Downey, Think python — How to think like a Computer Scientist, Green Tea Press [pdf] M. Lutz, Learning Python (5th edition), O'REILLY, 2013.
Slides:	Course introduction [slides] [handouts] Introduction to Python [slides] [handouts] Strings, lists, tuples and dic­tio­na­ries [slides] [handouts] Complex sta­te­men­ts [slides] [handouts] Functions [slides] [handouts] Modules and pro­grams [slides] [handouts] Pandas [slides] [handouts] Numpy+MatPlotLib [slides] [handouts] Recursion [slides] [handouts] Programming para­digms [slides] [handouts]
Lab:	TBD

Exams

Modality:

Written exam, see course introduction slides for the details.