#+TITLE: Intro to modern programming tools and techniques (1 semester version). Spring '17 #+AUTHOR: Artem Pianykh #+LATEX_HEADER: \usepackage[T2A]{fontenc} #+LATEX_HEADER: \usepackage[english, russian]{babel} #+LANGUAGE: en #+OPTIONS: ':t toc:2

The grading is available on [[https://docs.google.com/spreadsheets/d/1MakTaaqodRknx8lWdCKXM8wLHNZgIX82_tCZSBKDkoM/pubhtml][this page]].

• Goals The main objective of this course is to get familiar with modern software development tools and techniques including:

• working with a version control system,
• collaborative development through Github,
• working with dynamically types programming languages (R, Python),
• using Jupyter and RStudio for quick prototyping and visualization,
• using automated code testing,
• etc.

• Required software To start working on the course assignments you need to install:

• [[https://git-scm.com][git]] --- version control system,
• [[https://www.continuum.io/downloads][Anaconda]] --- a software package that includes Python and all necessary tools for data analysis and visualization,
• [[https://www.rstudio.com/products/rstudio/download3/][RStudio]] и [[https://cran.rstudio.com][R]] --- similar to the above, but for R programming languge.

The sotware is available for all major platforms: Windows, Linux, and Mac OS. However, Windws users may experience weird problems, so it's recommended to use a Unix-like system to work on the assignments.

Certain assignment may require additional software. Instructions will be provided as part of the assignment.

• Submission rules :PROPERTIES: :CUSTOM_ID: submission-rules :END: Each solution must contain:

• Working code and all other resources required to solve an assignment.
• File README.md or README.org with:
  1. a short description of the problem and your approach for solving it,
  2. an instruction on how to run the code,
  3. (for group assignments) a list of members and a short description of each member's contribution.

Assignments must be done in a group of 2 to 3.

All solutions must be submitted in a [[https://help.github.com/articles/creating-a-pull-request/][Pull Request]] to this repository. The root directory for N-th assignment submission is submissions/taskN/names-of-participants. Pull Request's branch should be named taskN-names-of-participants.

Example. Studends Alice and Bob decided to work together on assignment #2. First, they clone the current repository and create a new branch task2-alice-bob off of master. The they create a directory submissions/task2/alice-bob where they put all files related to their solution. When they have finished working on the solution, they commit their changes and send a Pull Request to master with a title Assignment 2. Alice, Bob.

From this moment on, their submissions is under review. The assignment is considered solved after successful review.

• Logistics and grading

There are 2 dates for each assignment:

1. The date by which the code must be read and the Pull Request submitted.
2. The data by which the review must be done (usually, +1 week after the first date). This extra time is given to students to address review comments and update their submissions.

Late submissions get a 50% penalty.

• Questions and discussions Each repository on GitHub has an Issues section. It is recommended to submit your questions as issues rather than by email because:

• issues are accessible by all,
• discussions are easier to follow,
• students can help each other.

• Assignments

• [[./tasks/task1.org][Assignment 1]]
• [[./tasks/task2.md][Assignment 2]]

• Useful resources ** Python

• [[https://www.codecademy.com/][Codecademy]] --- online platform with introductory courses on many programming languages including Python.
• [[https://www.scipy.org/][SciPy]] --- a library for scientitic computing including numerical integration and optimization.
• [[http://www.numpy.org/][NumPy]] --- a library for matric compotations.
• [[http://pandas.pydata.org/][Pandas]] --- a library for working with and manipulating structured data.
• [[http://matplotlib.org/][Matplotlib]] --- a charting library.
• [[https://jupyter.org/][Jupyter]] --- an interactive Python environment. ** R
• [[https://www.datacamp.com/][Datacamp]] --- online platform with courses on R, starting with the basics or the language, to data manipulation, to analysis and visualization.
• [[https://www.rstudio.com/][RStudio]] --- an IDE for R. ** MOOCS
• [[https://www.coursera.org/browse/computer-science?languages=en][Coursera -- Computer Science]] --- a selection of courses and specializations around Computer Science.
• [[https://www.coursera.org/browse/data-science?languages=en][Coursera -- Data Science]] --- a selection of courses and specializations around Data Science.