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Programming for Data Science A.Y. 2024/25

This is an introductory course to computer programming and related mathematical/logic background for students without a Bachelor in Computer Science or in Computer Engineering. The objective is to smoothly introduce the student to the programming concepts and tools needed for typical data processing and data analysis tasks. The course consists of lectures and practice in computer labs.

The course is in the 1st SEMESTER: so, classes will start in September and finish in January: there will be a written/lab exam first, if you succeed, then you are admitted to the second part of the exam, the oral.

Instructors

Lessons: <

Course slides & other material on Teams: “667AA 24/25 - PROGRAMMING FOR DATA SCIENCE [WDS-LM]”, https://teams.microsoft.com/l/team/19%3ArjrbC2bZuNSJ6YDv_xoiS42M171Z8jbk1PFarIMVwpk1%40thread.tacv2/conversations?groupId=63b57f30-ba5e-4503-a794-1f23f80a7f10&tenantId=c7456b31-a220-47f5-be52-473828670aa1

Text Books

  • [T] Kenneth H. Rosen. Discrete Mathematics and Its Applications. MCGraw-Hill. Supplement material (including Errata-Corrige).
  • [LA] “Linear Algebra: Theory, Intuition, Code” by Mike X Cohen, chapters: Vectors; Vector multiplication; Matrices; Matrix multiplication; Rank; Determinant; Matrix inverse; Eigendecomposition
  • [MV] Any book on multivariable calculus
  • [P] Pieter Spronck. The Coder’s Apprentice: Learning Programming with Python 3, 2017. Book and supplement material.

Software

  • Python programming: Anaconda distribution of Python 3. Computers at lab rooms include it both on Linux and Windows OS.
  • Unix Shell Either use Linux on lab machines, or install CygWin (on your PC).
  • Python online including visualization of memory state PythonTutor.
  • Jupyter Notebooks shown during theory classes GitHub

Previous years

Exams

mds/pds/start.txt · Ultima modifica: 16/09/2024 alle 17:40 (4 settimane fa) da Laura Semini

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