1. Data Science with Python
University of Cincinnati
Philip Bohun

2. Day 1 Introduction Overview of Python Data Munging/rangling
Data Visualization
Regression

3. Introduction Goals: Setup and run a Python environment
Understand strengths and weaknesses of Python
Basic data manipulation with Python
Do basic data wrangling/manipulation
Use packages for data analysis

4. Data Science Is it A or B? → classification
Is this weird? → anomoly detection
How much/many? → regression
How is this organized? → clustering
What should I do next? → reinforcement learning

5. Python Overview Setup Environment Python is an interpreted language
Python interpreter
Text editor
Python is an interpreted language
White space is important
[in class exercise]

6. Variables, Functions, Modules
Variable: named object that can change value
Literal: an unchangeable string or number
Function: reusable block of code
Module: reusable group of functions and variables
[in class exercise]

7. Control Flow Control flow allows us to make decisions in code
Types of control flow
Sequence
Selection
Iteration
[in class exercise]

8. File I/O Files can be opened an closed Files can be text or binary
[in class exercise]

9. Simple Database Interaction
Python has a built in library to create and interact with SQLite databases
Great for small and local projects
[in class exercise]

10. Data Types and Data Structures
Major data types in Python are:
Numeric
Sequences
Sets
Mappings
[in class exercise]

11. Data Munging/Wrangling
Dates and times are complicated
Dealing with empty values (null,NaN, etc.)
Handling strings and unstructured data
[in class exercise]

12. Data Visualization
Visualizing data can be important for EDA as well as communicating results
There are tools specifically designed for data visualization
We will cover the basics
[in class exercise]

13. Regression
Regressions allow us to answer the question how much or how many
There are many types of regressions
Let’s look at some simple regressions
[in class exercise]

14. Day 2
Program Design
Modules
Machine Learning

15. Program Design
Problem decomposition is one of the most important parts of program design
Rule of thumb: functions should be < 30 lines
A function should do one thing well
[in class exercise]

16. Algorithm Basics Algorithms determine how much work your programs do
If not using a library function for something, always search for the best algorithm
Don’t start with code, it’s best to solve the problem, then write code
[in class example]

17. Modules
Code that is reusable and covers a single topic should be gathered into a module
Mixing concerns in software leads to unnecessary complexity making software difficult to test and debug
[in class exercise]

18. Machine Learning Machine learning solves problems of optimization
It is very useful for searching very large possibility spaces
Also useful when approximate answers are useful
[in class lab]

19. Machine Learning
[ FULL PROJECT ]