Daily Schedule Part 1 (Actual — Kept Retrospectively)

Regular meeting schedule is Wednesdays and Saturdays, 11:00-12:00

Part 1 Uses Pasha and lasts for the first three weeks of Term 6

May 16 — Complete Chapters 1 to 3: Unix (shell) Basics, Installing Python, and the Astronomy/Scientific Data Analysis Stack — Problem Set 0: Get Anaconda downloaded and installed and use the IPython interface — Discussed Python language features, syntax, and style (PEP 8), differences between Windows and Unix shells, globbing, and Python’s Operating System Insulation Layer (OSIL)
May 17 — Complete Chapter 4: Introduction to Python — Problem Set 1: Use for loops to compute the first 20 Fibonacci numbers (screenshot your solution in IPython) — Discussed notebook tools and IDEs

May 21 — Complete Chapter 5: Visualization with Matplotlib — Install (if not already part of your Python distribution) and start working in Jupyter Lab — Problem Set 2: Make some histogram and scatter plots using the iris dataset (save your plots as a Jupyter Lab notebook)
May 25 — Complete Chapter 6: Numerical Computing with NumPy — Create a github account, fork the repo: brianhill/scientific-data-analysis — Then figure out how to get a local copy onto your machine of your fork (hexijin/scientific-data-analysis) and this will involve installing git on your machine (which will be different for Mac or Windows) — Started learning shell access to git, and the add, commit, push cycle (which we will be adding more to once that is routine)

May 28 — Complete Chapter 7: Scientific Computing with SciPy — Problem Set 3 (in addition to working through all the code in the chapter): Do Exercise 7.1 — Introduced the linear algebra concepts and notation for column vectors, row vectors, and matrix and vector multiplication
June 1 — Complete Chapter 8: Astropy and Astronomical Packages — As Problem Set 4 (in addition to working through all the code in the chapter): Do the Chapter 8 exercises — Finally, it’s time to add to your git knowledge the ideas of origin and upstream, and a second cyle of operations: how to fetch from upstream (my GitHub repo), rebase (in your local repo), and push your rebased changes to your origin (your GitHub fork of my repo)