Introduction to Numerical Computing with SciPy ​

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<p>The <i>« NC301µ Introduction to Numerical Computing with SciPy »</i> micro-course proposes an introduction to <b>numerical computing</b>, that is, how to use computers and algorithms to perform numerical computations. The presented concepts are directly applied with the <b>Python SciPy ecosystem</b>.</p>

<p>I gave this micro-course once at the <a href="https://www.ecam.be">ECAM Brussels Engineering School</a> (ECAM), in 2020, as a part of the numerical computing course. The course is taught in French, but all the material is available in English and <a href="/fr/teaching/ucourses/numcomp/">in French</a>.</p>


<h2>Documents</h2>

<ul>
  <li><a href="/files/ecam/general/ECAM-Competency-Based-Assessment-Slides.pdf">Competency Based Assessment <img src="/images/slides.png" width="16" height="16" alt="Slides"></a></li>
  <li><a href="/files/ucourses/numcomp/NumCompScipy-Competencies-List.pdf">Grid of skills to acquire <img src="/images/pdf.png" width="16" height="16" alt="PDF"></a></li>
</ul>


<h2>Theory</h2>

<ul>
  <li><a href="/files/ucourses/numcomp/NumCompScipy-Session1-Slides.pdf">Session 1:  Multidimensional Arrays and Linear Algebra with NumPy <img src="/images/slides.png" width="16" height="16" alt="Slides"></a></li>

  <li><a href="/files/ucourses/numcomp/NumCompScipy-Session2-Slides.pdf">Session 2: Plotting with Matplotlib and Numerical Analysis with SciPy <img src="/images/slides.png" width="16" height="16" alt="Slides"></a></li>
</ul>


<h2>Practice</h2>

<ul>
  <li><a href="/files/ucourses/numcomp/NumCompScipy-Quizz1.pdf">Quizz 1: Numerical computing with SciPy <img src="/images/pdf.png" width="16" height="16" alt="PDF"></a></li>
  <li><a href="/files/ucourses/numcomp/NumCompScipy-Coding1.pdf">Coding 1: Image transformation <img src="/images/pdf.png" width="16" height="16" alt="PDF"></a></li>
  <li><a href="/files/ucourses/numcomp/NumCompScipy-Coding2.pdf">Coding 2: Presenting data <img src="/images/pdf.png" width="16" height="16" alt="PDF"></a></li>
  <li><a href="/files/ucourses/numcomp/NumCompScipy-Mission1.pdf">Mission 1: SciPy Hello World <img src="/images/pdf.png" width="16" height="16" alt="PDF"></a></li>
  <li><a href="/files/ucourses/numcomp/NumCompScipy-Mission2.pdf">Mission 2: Solving a linear algebra problem <img src="/images/pdf.png" width="16" height="16" alt="PDF"></a></li>
  <li><a href="/files/ucourses/numcomp/NumCompScipy-Mission3.pdf">Mission 3: Time complexity of ndarray operations <img src="/images/pdf.png" width="16" height="16" alt="PDF"></a></li>
  <li><a href="/files/ucourses/numcomp/NumCompScipy-Project1.pdf">Project 1: Scipy application <img src="/images/pdf.png" width="16" height="16" alt="PDF"></a></li>
</ul>


<h2>Resources</h2>

<h3>Official book</h3>

<ul>
  <li>Sébastien Combéfis. (to be published). Découvrir SciPy et s'initier au calcul numérique avec Python. UKO Publication.</li>
</ul>

<h3>Reference books</h3>

<ul>
  <li>Eli Bressert. (2012). SciPy and NumPy. O'Reilly. <small>(ISBN: 978-1-449-30546-8)</small></li>
  <li>Robert Johansson. (2018). Numerical Python: A Practical Techniques Approach for Industry. Apress. <small>(ISBN: 978-1-484-20554-9)</small></li>
  <li>Sergio J. Rojas G., Erik A. Christensen and Francisco J. Blanco-Silva. (2015). Learning Scipy for Numerical and Scientific Computing. Packt. <small>(ISBN: 978-1-783-98770-2)</small></li>
</ul>

<h3>Online resources</h3>

<ul>
  <li><a href="https://www.scipy.org">Official website</a> of the SciPy ecosystem.</li>
</ul>