Intro to AAP course

Welcome to my Advanced Analytics with Python (aap) course. We’ll do several things the first week of class:

  • course overview and logistics

  • make sure you get your “analytics development environment” setup on your computer

  • review some Python, Jupyter Lab best practices, and basic use of git and GitHub

Readings and Other Resources

Python Data Science Handbook

We will be using Jupyter notebooks extensively in this course. Read through Chapter 1 in PDSH (and the associated Jupyter notebooks) which covers both the IPython shell and Jupyter notebook. Most things in that chapter are relevant to both. I discuss how to obtain all of the Jupyter notebooks for PDSH in the Setting up your analytical machine page.

Warning

Once you’ve installed Anaconda Python, you do NOT need to do any installation related to basic use of Jupyter notebooks and Jupyter Lab. They are included as part of Anaconda.

StackOverflow

StackOverflow is THE number one Q&A site for all things programming. There are tags for every conceivable programming language. It is essential that you learn how to ask good questions on sites like this or when asking questions of me or in our Help Forum.

Activities

Preview of this class

I’ve give you an idea of the kinds of things we’ll learn and do during this course. I also want to set the tone for the course. Spoiler alert:

  • Programming is super fun, especially when it’s challenging and is focused on solving real problems. But it’s even fun when using it just to solve puzzles.

  • Like many scientists, business analytics folks can really benefit from learning some basic software engineering.

  • Analytics is NOT just machine learning. Depending on the problem, you may need some combination of data wrangling, data analysis/viz, simulation, optimization and statistical/ML models.

Class logistics

We’ll review syllabus and course websites (Moodle and main). See Moodle for screencast.

Setting up your analytics development environment

All of the details for getting your computer ready for the aap class can be found on the Setting up your analytical machine page.

If you haven’t already done these things, you need to do them now.

Review of Python and analytics

As mentioned on the home page, this course is a follow on to my MIS 4470/5470 - Practical Computing for Data Analytics course. In that course we learned to use both R and Python for various analytical computing tasks. So, I certainly know that unless you have continued to use these computational and analytical tools, you will have forgotten many details. Nevertheless, you should be well positioned to quickly relearn things as neeeded throughout this course. The MIS 4470/5470 website is a great resource for efficiently reviewing the Python techniques and statistical modeling techniques that provide the background for this course.

Both the PDSH and WWToP books are terrific resources for reviewing specific topics. Even better than using the books is using the Jupyter notebooks that accompany the texts. I’ll often point you to specific notebooks and sections of the texts for a refresher on certain topics.

Explore

This section will typically have links related to the topic, … or not. Have fun exploring and learning more.