Jupyter Course
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README.md

Binder

Reproducible and Interactive Data Science

Syllabus

The aim of this course is to introduce students to the Jupyter Notebook which is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, machine learning and much more. Through the notebooks, research results and the underlying analysis can be transparently reproduced as well as shared. As an example of a Notebook on gravitational waves published in Phys. Rev. Lett., see here.

During three days with alternating lectures and hands-on exercises, the participants will learn to construct well-documented, electronic notebooks that perform advanced data analysis and produce publication ready plots. While the course is based on Python, this is not a prerequisite, and many other programming languages can be used.

Credits

4 ECTS.

Program

Lectures on December 11, 14, 15 (2017) and project presentations: January 5th, 2018. All dates from 10:15 to 15:00.

Location: Sölvegatan 27 (The observatory), Department of Astronomy and Theoretical Physics, Rooms Cassiopeia (Monday morning; Thursday afternoon; and all Friday) or Andromeda.

The course consists of four full days: three with alternating lectures and hands-on exercises, and one day with project presentations. Lectures are available as Notebooks on this site in the lectures folder.

  • Day 1. Introduction

    • Introduction and overview of Jupyter notebooks
    • Installation and package management (Anaconda, managing environments)
    • Navigating cells and iPython "Magic" commands
    • Online resources and getting help
    • Documenting using Markdown: rich text, equations, images, tables, video.
    • Other languages (bash, cython, R, etc.)
    • Online viewing, conversion, sharing, version control (Github, Zenodo, Binder, NBviewer)
  • Day 2. Numerical Methods, Plotting, and visualization

    • Storage and manipulation of numerical arrays (numpy)
    • Plotting in Notebooks (matplotlib, seaborn)
    • Arranging plots, customizing
    • Making plots publication ready
    • Exporting to vectorized file formats
    • Interactive visialization (ipywidgets)
  • Day 3. Numerical methods and data science

    • Scientific python (scipy)
    • Symbolic math (sympy)
    • Data parsing and import (csv, excel, json, pickle, pdf, custom files, etc.)
    • Working with large datasets (pandas)
  • Day 4. Project presentations

Prerequisites

  • No prior knowledge in Python is required, but familiarity with programming concepts is helpful.
  • A laptop connected to the internet (eduroam, for example) and running Unix, MacOS, or Windows and with Anaconda installed, see below.

If you have little experience with Python or shell programming, the following two tutorials may be helpful:

Preparations before the first lecture

  1. Install miniconda3 alternatively the full anaconda3 enviroment on your laptop (the latter is much larger).

  2. Download the course material (this github repository) and unzip.

  3. Install and activate the LUcompute environment described by the file environment.yml by running the following in a terminal:

    conda env create -f environment.yml
    source activate LUcompute
    jupyter nbextension enable rubberband/main
    jupyter nbextension enable exercise2/main
    jupyter nbextension enable --py widgetsnbextension

Instructions for Windows:

  1. Install miniconda3.

  2. Download the course material (this github repository) and unzip.

  3. Open the anaconda prompt from the start menu.

  4. Navigate to the folder where the course material has been unzipped (e.g. using cd to change directory and dir to list files in a folder).

  5. Install and activate the LUcompute environment described by the file environment.yml by running the following in the anaconda prompt:

    conda env create -f environment.yml
    activate LUcompute
    jupyter nbextension enable rubberband/main
    jupyter nbextension enable exercise2/main
    jupyter nbextension enable --py widgetsnbextension

Further Information

Project Work

The project work consists of three steps:

  1. Each student will make a Notebook project covering topics from day 1-3 with either
  • research, presenting data analysis and theory behind a manuscript or published paper. The Notebook should ideally be written such that it can act as supporting information (SI) for a journal. Here's some inspiration.
  • or a Notebook presenting a text-book topic of choice and aimed at students. Here's some inspiration.
  • Deadline for project: 27/12
  1. A peer-review process where each student reviews and, in writing, comments on two other notebooks. The review should be based on the criteria listed below and for each point, include specific suggestions for improvements. Deadline for review: 3/1

  2. Notebook presentation to the class (day 4). Maximum 10 minutes per participant and do include your answer to the referee reports.

Notebook Requirements

The notebook must

  • include rich documentation using Markdown, equations, tables, links, etc.
  • import or generate data. If generating, data should be exported to disk.
  • perform data operations using numpy, scipy, pandas or equivalent.
  • create plots of publication ready quality. For an editorial guide on Graphical Excellence, see here.
  • include instructions on how to run the notebook, include the required packages. This could be an environment.yml file.
  • be reproducible, i.e. someone elso should be able to redo the steps

Further, the notebook could

  • act as supporting information for an article
  • have an digital object identifier (DOI)