Python For Data Analysts

Welcome! This is a set of notes and links to tutorials that I have curated as part of my journey in learning Python. I am sharing these with the intent of guiding others through a crash course in using Python for data analysis. The target audience consists of [1] data scientists with experience using other languages (e.g. Matlab or R) and [2] people with some technical knowledge who want to collaborate more effectively with data scientists (e.g. scientists and business analytics professionals). My approach is a pragmatic introduction to Python modules useful for data analysis, and is light on the computer science and statistics/data science theory.

For those wanting more depth in data science methods and computing, Google has a machine learning course that will extend what you learn here. If you have little experience with statistics, Khan Academy's course on statistics and probability is a solid way to start thinking statistically.

Data science occupies a niche somewhere between applied statistics/math and computer science. Where statisticians' toolboxes include a variety of mathematical models and probability theory, data scientists' primary tools are algorithms. Data science in business and industry typically involves large-scale integration and automation of computational solutions. As such, data scientists often work closely with software developers or even do some development work themselves. However, data science uses a separate set of skills and workflows from software development, more strongly resembling statistics. This curriculum emphasizes the data science way of doing things.

The first chapter contains my original writing to frame the rest of the course. The remaining chapters each focus on one Python module and guide you through a sequence of tutorials, mostly from the official documentation.

Contents

1. Hello World: Python Setup/How Computers Run Code

2. pandas: Structuring Data For Analysis

3. statsmodels: Traditional Statistics

About Your Host

K.A. Flagg, Ph.D., is a data scientist formerly of Vizion and Atrium, and an alumnus of the Statistics program at Montana State University. Flagg nearly studied computer science before realizing that encompassed far more than video game development, and now has experience in R package development, and has dabbled in C/C++ software development as well as Salesforce APEX development.

Back to kflagg.github.io