Syllabus

Introduction to Python Programming - BIOF309 - FAES

Spring 2019

Time: Tuesday 5:30PM - 7:30PM

This document is subject to revision!

Changes are tracked using the git version control system.

To interact with the materials in the repo using JupyterLab (via Binder), please click the button below.

Additionally, the Jupyter Notebooks (ipynb files) in this repo can be opened in by clicking the icon below.

Instructors

• Martin Skarzynski - marskar at gmail dot com
• Jinping Liu - liu dot jinping at nih dot com
• Michael Chambers - michael dot chambers2 at nih dot gov

Course Description

This course is designed for non-programmers, biologists, or those without specific knowledge of Python to learn how to program. Week by week, we will slowly build up your skills and understanding of computer programming and the Python programming language. There will be in-class demonstrations, using PyCharm and to a lesser extent JupyterLab, and activities to be completed outside of class, using DataCamp, for you to practice and learn at your own pace.

Learning Objectives

By the end of this course you should be able to:

1. Look at a task and determine if you can or should automate it
2. Create working Python programs
3. Understand the difference between Python object types (e.g. lists, dicts)
4. Perform data analysis and visualization with Python
5. Use git for version control and collaboration
6. Demonstrate your Python skills with a project

Communication

Before contacting us, please check to see if your question has already been answered elsewhere, e.g. StackOverflow.

If you cannot find the answer, please make sure to ask your question thoughtfully (https://stackoverflow.com/help/how-to-ask) and provide everything needed to answer e.g. code, error message, dataset, etc.

In general, please use the course Slack workspace to communicate with classmates and instructors. If you have a course-relevant question or something to share, Slack is simply better than email. In case of personal/private question/concerns, please use Slack direct message (DM).

In case of an emergency, please send a DM on Slack and an email.

Logistics

This is a one-semester course starting on the 5th of February 2019 and finishing on ??th of May 2019.

Class Location: Room 162, Building 60, NIH Bethesda campus

Attendance in class is strongly recommended; however, we realize other commitments may occasionally prevent attendance. If you miss a class, please review the materials available at the course GitHub repository and keep up with activities and homework.

We will be offering REMOTE ATTENDANCE and CLASS RECORDINGS via GoToMeeting and WebEx. These two options are largely the same, you can pick whichever platform you prefer. Please do NOT use this as excuse to skip class and just watch the recorded lectures! This course is NOT a Massive Online Open Course (MOOC), it will feature a great deal of group work. Additionally, forming groups to complete the final project is highly encouraged! Remote attendance will work best if you can meet with classmates to work through exercises together. We will do our best to answers questions in the GoToMeeting and WebEx chat windows during class. We will also try to answer all questions on Slack, but please try to ask your questions during class, if at all possible.

Important FAES Spring 2019 semester dates:

• November 19–February 1: Online Registration
• January 17: Open House
• February 4: Classes begin
• February 4–February 22: Late Registration ($10.00 late registration fee per course applies)
• March 1: Last day to drop
• April 5: Last day to change status (credit or audit)
• May 17: Classes end

Required Materials

Each student is encouraged to bring their own laptop to each class.

Programing without a computer would be an exceptional feat.

Please install the following programs BEFORE the first class:

1. The Anaconda Scientific Python Distribution

   The Anaconda installer will automatically install most of the software we will use during the course, including Jupyter Notebooks.

2. The PyCharm Integrated Development Environment (IDE)

   The very nice folks at JetBrains have given us free licenses for the Professional Edition of PyCharm Integrated Development Environment (IDE), the best (in my humble opinion) Python Integrated Development Environment (IDE).

   If you have a .edu email address, please install PyCharm Integrated Development Environment (IDE) Professional using this link.

   If not, a installation link will be distributed to you by email and made available on Slack.

   Before the first class, please watch the Getting Started with PyCharm video series.

   Before the second class, please watch the PyCharm In-Depth VCS video series.

During the first few classes, we will set up accounts on:

1. DataCamp

   Since Fall 2017, the very nice folks at DataCamp have been generously supporting our class via their DataCamp for the Classroom initiative.

   This program give us free 6 month access to DataCamp's awesome Data Visualization📊, Machine Learning🤖, and Data Science learning materials.

   We will discuss the most interesting examples from DataCamp during class and point out others to be reviewed outside of class.

2. PluralSight

   Thanks to the DataCamp-PluralSight partnership, we can get free 6 month access to Web Development, Object-Oriented Programming, and Test-Driven Development learning materials on PluralSight.

3. GitHub

   All of the course materials are available on GitHub. Before accessing the course materials repo, you should know that

   • it is likely to be under constant development throughout the semester and
   • you are not expected to work through everything contained therein!

Optional Materials

1. A UNIX-like system

   If you use Windows 10, please try to set up the Windows subsystem for Linux. If you use MacOS or Linux, you are all set.

2. GitHub student pack

   GitHub is offering some free awesome resources to students, that might be of interest to you, depending on your background:

Schedule

#	Date	Title	Lead	Webex Recording
1	2019-02-05	Integrated Development Environments	Martin	20190205
2	2019-02-12	Python Basics	Martin	20190212
3	2019-02-19	Git and GitHub	Martin	20190219
4	2019-02-26	Import Modules and Run Scripts	Martin	20190226
5	2019-03-05	GitHub Pages and HTML Slides	Martin	20190305
6	2019-03-12	Functions, type hints, docstrings	Martin	20190312
7	2019-03-19	Booleans and Conditionals	Jinping, Michael & Nick	20190319
8	2019-03-26	Loops (& Loop Alternatives)	Jinping, Michael & Nick (& Martin)
9	2019-04-02	Pandas and DataFrames	Jinping, Michael & Nick
10	2019-04-09	Data Visualization	Jinping, Michael & Nick	20190409
11	2019-04-16	Machine Learning	Martin
12	2019-04-23	Genomics	Bari Ballew
13	2019-04-30	Object-Oriented Programming	Nick
14	2019-05-07	Student Presentations
15	2019-05-14	Student Presentations

Homework

This semester we are continuing our free-form approach to homework assignments. The due dates below are guidelines. By the end of the semester, you must complete at least one career track or at least two skills tracks on DataCamp. The DataCamp career tracks include the Python Path on PluralSight. Pick DataCamp if you want to focus on Data Analysis and Machine Learning. Choose PluralSight if are interested in Object-Oriented Programming and Test-Driven Development.

This will take 28-67 hours total to complete, depending on which you choose to do.

DataCamp Career Tracks (complete at least 1):

• PluralSight Python Path, 30 hours, 8 courses
• DataCamp Python Programmer, 36 hours, 10 courses
• DataCamp Data Analyst with Python, 47 hours, 13 Courses
• DataCamp Data Scientist with Python, 67 hours, 20 Courses

DataCamp Skills Tracks (complete at least 2):

• Python Programming, 15 hours, 4 courses
• Importing & Cleaning Data with Python, 13 hours, 4 courses
• Data Manipulation with Python, 16 hours, 4 courses
• Machine Learning, 16 hours, 4 courses

Please start on your chosen track(s) on DataCamp or PluralSight as soon as possible and work towards the certificate(s) throughout the semester. This will require substantial work! Do not wait until the end of the semester!

Please use the schedule below as a guide to which DataCamp and PluralSight chapters/lessons correspond to what is covered in class.

1. BEFORE Class 1

   • Install Anaconda Scientific Python Distribution
   • Install PyCharm

2. BEFORE Class 2

   • Python Basics
   • Python: Getting Started
   • Python Fundamentals
   • Chapters 01-05 in Whirlwind Tour of Python
   • Chapter 02 in Python for Data Analysis

3. BEFORE Class 4

   • Python Lists
   • Functions and Packages
   • Python – Beyond the Basics
   • Chapter 08 & 13 in Whirlwind Tour of Python
   • Chapter 03 in Python for Data Analysis

4. BEFORE Class 7

   • Logic, Control Flow and Filtering
   • The Python Developer's Toolkit
   • Chapter 06, 07, & 09 in Whirlwind Tour of Python

5. BEFORE Class 8

   • Loops
   • Chapter 10 & 11 in Whirlwind Tour of Python

6. BEFORE Class 9

   • Dictionaries & Pandas
   • Chapter 03 in Python Data Science Handbook
   • Chapter 05-12 in Python for Data Analysis
   • Django Fundamentals

7. BEFORE Class 10

   • Introduction to Data Visualization with Python
   • Data Visualization with Seaborn
   • Chapter 04 in Python Data Science Handbook
   • Advanced Python

8. BEFORE Class 11

   • NumPy
   • Chapter 02 in Python Data Science Handbook
   • Chapter 04 in Python for Data Analysis
   • Full Stack Web Development with Python (WEB2PY)

9. BEFORE Class 12

   • Getting Started with Machine Learning in Python
   • Predicting with Decision Trees
   • Improving your Predictions through Random Forests
   • Chapter 05 in Python Data Science Handbook
   • Testing Django Applications

10. WORK ON FINAL PROJECTS Depending on your final project, you might find the following topics useful:

    • Error handling
    • Comprehensions
    • Iterators and Generators
    • Regular Expressions

Recommended Books

There is no required textbook for this course.

We do, however, highly recommend Python for Data Science and its companion text A Whirlwind Tour of Python by Jake Vanderplas. Both of these books are available free on GitHub in Jupyter Notebook form. The code for Python for Data Analysis by Wes McKinney is also on GitHub but the text is only available in the printed copy of the book. For maximum enjoyment, consider working through the relevant chapters before coming to class.

We will link to relevant online resources throughout the course.

If you would like additional material on the basics, the following resources may be useful:

• Python for Biologists by Martin Jones.
• Learn Python the Hard Way (ebook freely available from the author) by Zed A. Shaw; a video course is also available.
• Think Python (ebook freely available from the author) by Allen B. Downey.
• Python for Everybody: Exploring Data in Python 3 (ebook freely available from the author) by Charles Severance.
• The Hitchhiker’s Guide to Python by Kenneth Reitz and Tanya Schlusser.
• Automate the Boring Stuff with Python by Al Sweigart.
• Introduction to Machine Learning with Python by Andreas Mueller and Sarah Guido.

For more information about Python, please see the official Python Software Foundation website.

Grading

The emphasis of the course is on learning and mastering the skills covered. We hope that everyone will be able to complete one of the Python tracks on DataCamp or PluralSight and submit a final project via GitHub. If some of the material appears unclear, please ask for clarification.

Completion of the Python tracks on DataCamp or PluralSight will be graded in a binary manner (completed/incomplete).

Grading the final project will be done using the following rubric:

• Project description / Specification

  • Goals unclear, difficulty demonstrating functionality (1-3)
  • Goals for the project and functionality are discussed but difficult to follow (4-6)
  • Goals for the project and functionality are discussed (7-9)
  • Goals for the project and functionality are logically presented and clearly communicated (10-12)

• Documentation

  • Only comments embedded in the code (1-3)
  • Objects and methods have docstrings (4-6)
  • Objects and methods have docstrings, additional standalone documentation (7-9)
  • Objects and methods have docstrings, extensive standalone documentation with example usage (10-12)

• Readability

  • The code is poorly organized and very difficult to read (1-3)
  • The code is readable, but challenging to understand (4-6)
  • The code is fairly easy to read (7-9)
  • The code is well organized and very easy to read (10-12)

• Reusability

  • The code is not organized for reusability (1-3)
  • Some parts of the code could be reused (4-6)
  • Most of the code could be reused (7-9)
  • Each part of the code, and the whole, could be reused (10-12)

• Performance

  • Program does not run (1-6)
  • Program runs, but does not produce correct output (7-12)
  • Program runs, produces correct output under most conditions (13-18)
  • Program runs, produces correct output with robust error checking (19-24)

Course Materials

Course materials are available in the course GitHub repository.