Python Programming Workshop: A Hands-on Tutorial

This tutorial introduces Python programming for data analytics. It covers Python language, Jupyter environment, and common extension packages such as numpy and pandas for data analytics through combination of lectures and hands-on labs. It will first use Jupyter Notebook as the environment to learn basic Python operations and then briefly introduce Python programming principles including variable types, data structures, decision and loop controls, and functions.  The workshop will then focus on the use of numpy and pandas packages for data analytics. It will show how to load and explore the package contents, how to operate and visualize arrays in numpy and time series in pandas, and how to load custom data files into data frames, and how to explore and visualize data in a data frame.

Objectives:

1. Understand the basic elements of Python programming, including how to create different types of data objects such as lists, matrices, dictionaries, sets, tuples, arrays, and data frames and how to create custom functions
2. Gain hands-on skills on Jupyter interactive environment including how to load package and how to load structured data from data files
3. Gain hands-on experience on how to examine, summarize, and visualize data in arrays, time series, and data frames

 Prerequisites:

1. Download the current version of Python from https://www.python.org/downloads/ or Miniconda with Python 3.9 from Miniconda3-py39_23.5.2-0-Windows-x86_64.exe 
2. Download the presentation and data files. 

Biography

Liping Liu is Professor of Management and Information Systems at the University of Akron. His research interests are in the areas of e-business, systems development, machine learning, uncertainty in artificial intelligence, decision making and has published three books on requirements modeling and belief functions. Dr. Liu has strong programming and teaching interests in data analytics and systems design, development, and integration. He has been studying Bayesian learning and artificial intelligence for 30 years and developed a linear model-based inference system, LMOS.org. He is currently teaching R Programming and Advanced Data Analytics courses for the graduate Data Analytics program at the University of Akron.