{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Introduction to Python for Data Science\n", "\n", "Gus Powers & Jay Cunningham\n", "\n", "September 11, 13, 18, 20" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Introductions" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Gus Powers\n", "\n", " \n", " \n", "
\n", "

Lead Data Scientist at 84.51°

\n", "
    \n", "
  • Creating and maintaining data science tools for internal use
    • \n", "
  • Python, Bash (shell), & R
    • \n", "
\n", "

Academic

\n", "
    \n", "
  • BS, Chemistry, Thomas More College
    • \n", "
  • MS, Chemistry, University of Cincinnati
    • \n", "
  • MS, Business Analytics, University of Cincinnati
    • \n", "
\n", "

Contact

\n", " \n", "
" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Jay Cunningham\n", "\n", " \n", " \n", "
\n", "

Lead Data Scientist at 84.51°

\n", "
    \n", "
  • Researching and developing forecasting models
    • \n", "
  • Machine learning, Python
    • \n", "
\n", "

Academic

\n", "
    \n", "
  • BA, Mathematics, University of Kentucky
    • \n", "
  • MA, Economics, University of North Carolina (Greensboro)
    • \n", "
\n", "

Contact

\n", " \n", "
" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "## Brad Boehmke\n", "\n", " \n", " \n", "
\n", "

Director, Data Science at 84.51°

\n", "
    \n", "
  • Productionizing models and science solutions
    • \n", "
  • R&D and protogyping new solutions
    • \n", "
  • Python, R, & MLOps toolchain
    • \n", "
\n", "

Academic

\n", "
    \n", "
  • BS, Kinesiology, North Dakota State University
    • \n", "
  • MS, Cost Analytics, Air Force Institute of Technology
    • \n", "
  • PhD, Logistics, Air Force Institute of Technology
    • \n", "
\n", "

Contact

\n", " \n", "
" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "## Ethan Swan\n", "\n", " \n", " \n", "
\n", "

Senior Backend Engineer at ReviewTrackers

\n", "
    \n", "
  • Rest API development
    • \n", "
  • Putting ML models in production
    • \n", "
  • Python, Go, Ruby, & ReactJS (JavaScript)
    • \n", "
\n", "

Academic

\n", "
    \n", "
  • BS, Computer Science, University of Notre Dame
    • \n", "
  • MBA, Business Analytics, University of Notre Dame
    • \n", "
\n", "

Contact

\n", " \n", "
" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Around The Room\n", "\n", "We'll go around the room. Please share:\n", "\n", "1. Your name\n", "2. Your job or field\n", "3. How you use Python now or would like to in the future" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Course" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Defining Data Science\n", "\n", "
\n", "\"data-science.png\"\n", "
" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Data Science and Technology\n", "\n", "
\n", "\"data-science-and-tech.png\"\n", "
" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Applied Data Science\n", "\n", "
\n", "\"applied-data-science.gif\"\n", "
" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Course Objectives\n", "\n", "The following are the primary learning objectives of this course:" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "1. Develop comprehensive skills in the importing/exporting, wrangling, aggregating and joining of data using Python.\n", "2. Establish a mental model of the Python programming language to enable future self-learning.\n", "3. Build awareness and basic skills in the core data science area of data visualization." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Course Agenda" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "| Day | Topic | Time |\n", "| :--------:| :----------------------------------------------------------------------------- | :-----------: |\n", "| __Day 1__ | Introductions | 12:30 - 12:45 |\n", "| | Python and Jupyter Overview | 12:45 - 1:15 |\n", "| | Fundamentals | 1:15 - 2:00 |\n", "| | Break | 2:00 - 2:15 |\n", "| | Packages, Modules, Methods, Functions | 2:15 - 3:00 |\n", "| | Importing Data | 3:00 - 3:45 |\n", "| | Q&A | 3:45 - 4:15 |\n", "| __Day 2__ | Q&A | 12:45 - 1:15 |\n", "| | Selecting and Filtering Data | 1:15 - 2:00 |\n", "| | Working with Columns | 2:00 - 2:45 |\n", "| | Break | 2:45 - 3:00 |\n", "| | Case Study, pt. 1 | 3:00 - 3:45 |\n", "| | Q&A | 3:45 - 4:15 |" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "| Day | Topic | Time |\n", "| :--------:| :----------------------------------------------------------------------------- | :-----------: |\n", "| __Day 3__ | Q&A | 12:45 - 1:15 |\n", "| | Review | 1:15 - 1:30 |\n", "| | Summarizing Data | 1:30 - 2:15 |\n", "| | Break | 2:15 - 2:30 |\n", "| | Summarizing Grouped Data | 2:30 - 3:00 |\n", "| | Joining Data | 3:00 - 3:45 |\n", "| | Q&A | 3:45 - 4:15 |\n", "| __Day 4__ | Q&A | 12:45 - 1:15 |\n", "| | Exporting Data | 1:15 - 1:45 |\n", "| | Visualizing Data | 1:45 - 2:45 |\n", "| | Break | 2:45 - 3:00 |\n", "| | Case Study, pt. 2 | 3:00 - 3:45 |\n", "| | Q&A | 3:45 - 4:15 |" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Technologies" ] }, { "cell_type": "markdown", "metadata": { "cell_style": "center", "slideshow": { "slide_type": "slide" } }, "source": [ "### Binder\n", "\n", "* We've developed this class using a product named [Binder](https://mybinder.org/).\n", "* As a result, this course requires *zero* setup on your part.\n", "* There are two core techologies within the Binder repository: Python and Jupyter.\n", "\n", "*We will cover more on this shortly.*" ] }, { "cell_type": "markdown", "metadata": { "cell_style": "split", "slideshow": { "slide_type": "slide" } }, "source": [ "### Python\n", "\n", "* Python is the programming language we'll be learning in this class.\n", "* We are using Python 3.11, the newest version of Python, for the entirety of this class.\n", "* The core libaries we will be using are `pandas` and `seaborn`." ] }, { "cell_type": "markdown", "metadata": { "cell_style": "split", "slideshow": { "slide_type": "fragment" } }, "source": [ "### Jupyter\n", "\n", "* Jupyter is the integrated development environment (IDE) we will be using.\n", "* This is where we will write and run our Python code.\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Course Material" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "* All of the material for this course can be reached from our [GitHub](https://github.com/uc-python/intro-python-datasci) repository." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "* You can either access this material through [Binder](https://mybinder.org/v2/gh/uc-python/intro-python-datasci/master) or by [downloading the material](https://github.com/uc-python/intro-python-datasci/archive/master.zip)\n", " and opening it via Anaconda Navigator and JupyterLab." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Slides *are* notebooks" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "* We will be teaching using slides." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "* These slides are created from the notebooks in the course repository -- so you can follow along and run the code in your notebook." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Source Code" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "* Source code for the training can be found on [GitHub](https://github.com/uc-python/intro-python-datasci)." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "* This repository is public so you can clone (download) and/or refer to the materials at any point in the future." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Questions\n", "\n", "Are there any questions before moving on?" ] } ], "metadata": { "celltoolbar": "Slideshow", "kernelspec": { "display_name": "uc-python", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.4" }, "rise": { "autolaunch": true, "transition": "none" } }, "nbformat": 4, "nbformat_minor": 4 }