Introductory and Intermediate Python for Data Science
Overview
Instructor(s): Richard Lawrence
Time: Sessions 3 and 4 — Friday, November 8 and 15, 2024 — 10:00AM-4:00PM CT (with a one hour lunch break)
Location: Blocker 220
Prerequisite(s): None
This course covers the most important core components of Python programming at the introductory level. Students will learn fundamental programming concepts such as variables, data structures, flow control, and object-oriented programming. Topics and exercises are selected to be relevant for scientific research applications.
This course also covers a selection of scientific programming tools commonly used in Python programming at the intermediate level. Students will learn research techniques such as manipulating and visualizing data, exploring functions, modeling, and retrieving data from the internet. Topics and exercises are selected to be relevant for data science applications. Tools are drawn primarily from the libraries NumPy, SciPy, Matplotlib, and Pandas.
Learning takes place using the Google Colab integrated development environment.
The CC* SWEETER project is supported by a National Science Foundation (NSF) award number 1925764.
Course Materials
Previous Semester Materials
- Course Notes (Spring 2022): PDF
- Jupyter Notebook (Spring 2022): Intro_Python_fall21_Portal.ipynb
- Colab Jupyter Notebook (Spring 2022): Intro_Python_spring2022_Colab.ipynb
- Course Notes (Fall 2021): PDF
- Jupyter Notebook (Fall 2021): Intro_Python_fall21_Portal.ipynb
- Colab Jupyter Notebook (Fall 2021): Intro Python fall21 Colab.ipynb
- Access Instructions (Spring 2021): PDF
- Course Notes (Spring 2021): PDF
- Course Files (Spring 2021): GitHub
- Course Files (Fall 2020): JupyterNotebook
- Notebook Access Instructions: PDF
- Spring 2020 Setup Instructions
- Software Carpentry: Programming with Python (Spring 2020)
- Course Files (Fall 2019): tgz
- Notebook Access Instructions: PDF
Learning Objectives and Agenda
In this course, participants will:
- Use Google Colaboratory to develop python applications
- Sessions 1 & 2:
Practice concepts of programming: Comments, Data Types, Operators, Variables, Functions, Tuples, Multi-line Statements, Control Structures, Loops, Conditionals, Lists, Dictionaries, Methods, Modules - Sessions 3 & 4:
Practice concepts of data science: Arrays, Data Frames, Plotting, Data Manipulation, Web Scraping - Obtain a microcredential upon passing all quizzes
This course's topics are organized roughly as follows:
- Using Google Colaboratory
- Comments
- Data Types
- Operators
- Variables
- Functions
- Tuples
- Multi-line Statements
- Control Structures
- Loops
- Conditionals
- Lists
- Dictionaries
- Methods
- Modules
- Arrays
- Data Frames
- Plotting
- Data Manipulation
Note: During the class sessions, students will do Python exercises using Google Colaboratory and take quizzes along the way to earn a microcredential.