Carl H. Lindner College of BusinessCarl H. Lindner College of BusinessUniversity of Cincinnati

Carl H. Lindner College of Business

Data Science with Python

Introduction to Data Science with Python        Feb 22 & 23


Python is one of the most popular programming languages in use today and has become very popular in recent years because of libraries available for numerical computation, scientific computing, data visualization, and machine learning. This course will familiarize students with the use of Python for data science and the common libraries to perform typical data science operations.


  • Basic familiarity with Python syntax
  • Familiarity with basic probability and statistics 
  • Familiarity with data science

Course Fee:  $750 includes breakfasts, lunches, refreshments and free parking for both days.

Course Location:
U-Square. Room 359
225 Calhoun Street
Cincinnati. OH 45219   (google map link)



To Acquire the Pre-Requisites, Refer to These Useful Links:

Goals – Upon completion students will be able to:

  1. Understand Python file i/o, data structures, and database interaction
  2. Use python modules, such as numpy and scipy to analyze and display data
  3. Perform basic data wrangling and manipulation with Python
  4. Use scikit learn to perform machine learning

Required Software:

Course Outline: (may change slightly)



Introduction (45min)
-Overview of course goals
-About the Python language
-Setup programming environment
-Testing the environment

Python Review (90 min)
-Variables, functions, modules
-File input/output
-Database creation, connection, querying
-Data types and data structures

Data Munging/Wrangling (30 min)
-Time, timestamps, and dates
-Null, NaN, and other non-useful values
-Strings, enumerated values, and unstructured data

Data Visualization (60 min)
-Introduction to pandas and matplotlib
-Plot types
-Plot formatting

Classification (90 min)
-Overview of classification algorithms
-Support vector machine classification in Python
-Dimension reduction with principle component analysis in Python
-Lab: Nearest neighbors

Regression (90 min)
-Overview of regression algorithms
-Ordinary least squares in Python
-Linear regression in Python
-Lab: linear regression


Program Design (90 min)
-Decomposing a problem
-Partitioning solution code
-Basics of algorithms

Modules (45 min)
-Creating your own modules
-Deciding how to organize modules

Dealing with Large Datasets (90 min)
Knowing the limits of your computer
-How to process datasets larger than the available RAM
-Tips for writing fast code

Machine Learning (90 min)
-Principles of machine learning
-When machine learning is useful
-Lab: face recognition

Machine Learning Full Project (120 min)
-Define problem
-Evaluate algorithms
-Improve results
-Display results


INSTRUCTOR:  Philip Bohun is a software engineer who enjoys combining his interest in physics, computer science, and electronics within his work. Philip has a background in modeling and simulation and has spent the majority of his career developing mission planning software for satellites and aircraft, as well as software for indoor active shooter detection, social network analysis, and RFID tracking. He has worked on projects for General Electric, NASA, the U.S. Army, and the U.S. Air Force. He also spends time developing small instructional software programs. He currently works as an independent contractor providing software engineering and development support to companies.