#!/usr/bin/env python
# coding: utf-8

# # Introduction to Python for Data Science
# 
# Gus Powers & Jay Cunningham
# 
# September 11, 13, 18, 20

# ## Introductions

# ## Gus Powers
# <table><tr>
#   <td><img width="300" src="images/gus.jpg"></td>
#   <td>
#     <h3><strong>Lead Data Scientist</strong> at 84.51&deg;</h3>
#     <ul>
#       <li>Creating and maintaining data science tools for internal use</li>
#       <li>Python, Bash (shell), & R</li>
#     </ul>
#     <h4>Academic</h4>
#     <ul>
#       <li>BS, Chemistry, Thomas More College</li>
#       <li>MS, Chemistry, University of Cincinnati</li>
#       <li>MS, Business Analytics, University of Cincinnati</li>
#     </ul>
#     <h4>Contact</h4>
#     <ul>
#       <li>GitHub: <a href="https://github.com/augustopher">augustopher</a></li>
#       <li>LinkedIn: <a href="https://www.linkedin.com/in/august-powers-09717481/">August Powers</a></li>
#       <li>Email: <a href="mailto:guspowers0@gmail.com">guspowers0@gmail.com</a></li>
#     </ul>
#     </td>
# </tr></table>

# ## Jay Cunningham
# <table><tr>
#   <td><img width="300" src="images/jay.jpg"></td>
#   <td>
#     <h3><strong>Lead Data Scientist</strong> at 84.51&deg;</h3>
#     <ul>
#       <li>Researching and developing forecasting models</li>
#       <li>Machine learning, Python</li>
#     </ul>
#     <h4>Academic</h4>
#     <ul>
#         <li>BA, Mathematics, University of Kentucky</li>
#         <li>MA, Economics, University of North Carolina (Greensboro)</li>
#     </ul>
#     <h4>Contact</h4>
#     <ul>
#       <li>LinkedIn: <a href="https://www.linkedin.com/mwlite/in/jay-cunningham-60612886">Jay Cunningham</a></li>
#       <li>Email: <a href="mailto:james@notbadafterall.com">james@notbadafterall.com</a></li>
#     </ul>
#     </td>
# </tr></table>

# ## Brad Boehmke
# <table><tr>
#     <td><img width="300" src="images/brad.jpg"></td>
#     <td>
#       <h4><strong>Director, Data Science</strong> at 84.51&deg;</h4>
#       <ul>
#         <li>Productionizing models and science solutions</li>
#         <li>R&D and protogyping new solutions</li>
#         <li>Python, R, & MLOps toolchain</li>
#       </ul>
#       <h4>Academic</h4>
#       <ul>
#           <li>BS, Kinesiology, North Dakota State University</li>
#           <li>MS, Cost Analytics, Air Force Institute of Technology</li>
#           <li>PhD, Logistics, Air Force Institute of Technology</li>
#       </ul>
#       <h4>Contact</h4>
#       <ul>
#           <li>Website: <a href="http://bradleyboehmke.github.io/">bradleyboehmke.github.io</a></li>
#           <li>GitHub: <a href="https://github.com/bradleyboehmke">bradleyboehmke</a></li>
#           <li>Twitter: <a href="https://twitter.com/bradleyboehmke">@bradleyboehmke</a></li>
#           <li>LinkedIn: <a href="https://www.linkedin.com/in/brad-boehmke-ph-d-9b0a257">Brad Boehmke, PhD</a></li>
#           <li>Email: <a href="mailto:bradleyboehmke@gmail.com">bradleyboehmke@gmail.com</a></li>
#       </ul>
#     </td>
# </tr></table>

# ## Ethan Swan
# <table><tr>
#   <td><img width="300" src="images/ethan.jpg"></td>
#   <td>
#     <h4><strong>Senior Backend Engineer</strong> at ReviewTrackers</h4>
#     <ul>
#       <li>Rest API development</li>
#       <li>Putting ML models in production</li>
#       <li>Python, Go, Ruby, & ReactJS (JavaScript)</li>
#     </ul>
#     <h4>Academic</h4>
#     <ul>
#         <li>BS, Computer Science, University of Notre Dame</li>
#         <li>MBA, Business Analytics, University of Notre Dame</li>
#     </ul>
#     <h4>Contact</h4>
#     <ul>
#         <li>Website: <a href="https://ethanswan.com">ethanswan.com</a></li>
#         <li>GitHub: <a href="https://github.com/eswan18/">eswan18</a></li>
#         <li>Twitter: <a href="https://twitter.com/eswan18">@eswan18</a></li>
#         <li>LinkedIn: <a href="https://linkedin.com/in/ethanpswan">Ethan Swan</a></li>
#         <li>Email: <a href="mailto:ethanpswan@gmail.com">ethanpswan@gmail.com</a></li>
#     </ul>
#     </td>
# </tr></table>

# ### Around The Room
# 
# We'll go around the room. Please share:
# 
# 1. Your name
# 2. Your job or field
# 3. How you use Python now or would like to in the future

# ## Course

# ### Defining Data Science
# 
# <center>
# <img src="images/data-science.png" alt="data-science.png" width="900" height="900">
# </center>

# ### Data Science and Technology
# 
# <center>
# <img src="images/data-science-and-tech.png" alt="data-science-and-tech.png" width="1100" height="1100">
# </center>

# ### Applied Data Science
# 
# <center>
# <img src="images/applied-data-science.gif" alt="applied-data-science.gif" width="1200" height="1200">
# </center>

# ## Course Objectives
# 
# The following are the primary learning objectives of this course:

# 1. Develop comprehensive skills in the importing/exporting, wrangling, aggregating and joining of data using Python.
# 2. Establish a mental model of the Python programming language to enable future self-learning.
# 3. Build awareness and basic skills in the core data science area of data visualization.

# ## Course Agenda

# | Day       | Topic                                                                          |     Time      |
# | :--------:| :----------------------------------------------------------------------------- | :-----------: |
# | __Day 1__ | Introductions                                                                  | 12:30 - 12:45 |
# |           | Python and Jupyter Overview                                                    | 12:45 - 1:15  |
# |           | Fundamentals                                                                   |  1:15 - 2:00  |
# |           | Break                                                                          |  2:00 - 2:15  |
# |           | Packages, Modules, Methods, Functions                                          |  2:15 - 3:00  |
# |           | Importing Data                                                                 |  3:00 - 3:45  |
# |           | Q&A                                                                            |  3:45 - 4:15  |
# | __Day 2__ | Q&A                                                                            | 12:45 - 1:15  |
# |           | Selecting and Filtering Data                                                   |  1:15 - 2:00  |
# |           | Working with Columns                                                           |  2:00 - 2:45  |
# |           | Break                                                                          |  2:45 - 3:00  |
# |           | Case Study, pt. 1                                                              |  3:00 - 3:45  |
# |           | Q&A                                                                            |  3:45 - 4:15  |

# | Day       | Topic                                                                          |     Time      |
# | :--------:| :----------------------------------------------------------------------------- | :-----------: |
# | __Day 3__ | Q&A                                                                            | 12:45 - 1:15  |
# |           | Review                                                                         |  1:15 - 1:30  |
# |           | Summarizing Data                                                               |  1:30 - 2:15  |
# |           | Break                                                                          |  2:15 - 2:30  |
# |           | Summarizing Grouped Data                                                       |  2:30 - 3:00  |
# |           | Joining Data                                                                   |  3:00 - 3:45  |
# |           | Q&A                                                                            |  3:45 - 4:15  |
# | __Day 4__ | Q&A                                                                            | 12:45 - 1:15  |
# |           | Exporting Data                                                                 |  1:15 - 1:45  |
# |           | Visualizing Data                                                               |  1:45 - 2:45  |
# |           | Break                                                                          |  2:45 - 3:00  |
# |           | Case Study, pt. 2                                                              |  3:00 - 3:45  |
# |           | Q&A                                                                            |  3:45 - 4:15  |

# ## Technologies

# ### Binder
# 
# * We've developed this class using a product named [Binder](https://mybinder.org/).
# * As a result, this course requires *zero* setup on your part.
# * There are two core techologies within the Binder repository: Python and Jupyter.
# 
# *We will cover more on this shortly.*

# ### Python
# 
# * Python is the programming language we'll be learning in this class.
# * We are using Python 3.11, the newest version of Python, for the entirety of this class.
# * The core libaries we will be using are `pandas` and `seaborn`.

# ### Jupyter
# 
# * Jupyter is the integrated development environment (IDE) we will be using.
# * This is where we will write and run our Python code.
# 

# ## Course Material

# * All of the material for this course can be reached from our [GitHub](https://github.com/uc-python/intro-python-datasci) repository.

# * 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)
#  and opening it via Anaconda Navigator and JupyterLab.

# ### Slides *are* notebooks

# * We will be teaching using slides.

# * These slides are created from the notebooks in the course repository -- so you can follow along and run the code in your notebook.

# ### Source Code

# * Source code for the training can be found on [GitHub](https://github.com/uc-python/intro-python-datasci).

# * This repository is public so you can clone (download) and/or refer to the materials at any point in the future.

# ## Questions
# 
# Are there any questions before moving on?