Andrea Passerini

Scientific Programming: Part A - Programming

For communications (e.g. exam modality under mobility restrictions) please refer to the moodle page of the course.

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: Luca Bianco (QCB)
Email: luca.bianco [guess what] fmach.it
David Leoni (Data Science)
Email: david.leoni [guess what] unitn.it
Tutors: Samuel Valentini (QCB)
Email: samuel.valentini [guess what] unitn.it
Gabriele Masina (Data Science)
Email: gabriele.masina [guess what] studenti.unitn.it
Office hours: Wednesday 14:30-15:30 (send email before)
Lecture time and place: Monday 14.30-16.30 A107 (Lecture)
Tuesday 15.30-17.30 A107 (Lab. QCB)
Tuesday 15.30-17.30 A103 (Lab. Data Science)
Wednesday 11.30-13.30 A107 (Lecture)
Thursday 15.30-17.30 A107 (Lab. QCB)
Thursday 15.30-17.30 A208 (Lab. Data Science)
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.
Material: Slides and handouts (pdf format)
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]
Recursion [slides] [handouts]
Pandas [slides] [handouts]
Numpy+MatPlotLib [slides] [handouts]
Programming para­digms [slides] [handouts]
Exercises — 1 [slides] [handouts]
Lab: Lab page (QCB)
Lab page (Data Science)

Exams

Modality: Written exam