Help Icon


Course Title
Python Developer


Whether you’re new to programming or just want to learn a new language, this in-depth program will teach you the ins and outs of Python programming. Learn how Python works and what it’s good for, and gain an understanding of Python’s place in the wider programming world. Learn more advanced methods and how to work with iPhone Notebook, the Collections Module, regular expressions, databases, CSV files, JSON, and XML. This program is entirely online and is completed at your own pace.


  • Gain an understanding of programming in Python
  • Work with iPhone Notebook, the Collections Module, regular expressions, databases, CSV files, JSON, and XML
  • Discover advanced sorting, how to write object-oriented code in Python, and how to test and debug your Python code


Introduction to Python

I. Python Basics

A. Running Python

B. Hello, World!

C. Literals

D. Python Comments

E. Data Types

F. Variables

G. Writing a Python Module

H. print() Function

I. Named Arguments

J. Collecting User Input

K. Getting Help

II. Functions and Modules

A. Defining Functions

B. Variable Scope

C. Global Variables

D. Function Parameters

E. Returning Values

F. Importing Modules

III. Math

A. Arithmetic Operators

B. Modulus and Floor Division

C. Assignment Operators

D. Built-in Math Functions

E. The math Module

F. The random Module

G. Seeding

IV. Python Strings

A. Quotation Marks and Special Characters

B. String Indexing

C. Slicing Strings

D. Concatenation and Repetition

E. Common String Methods

F. String Formatting

G. Built-in String Functions

V. Iterables: Sequences, Dictionaries, and Sets

A. Definitions

B. Sequences

C. Unpacking Sequences

D. Dictionaries

E. The len() Function

F. Sets

G. *args and **kwargs

VI. Flow Control

A. Conditional Statements

B. The is and is not Operators

C. Python's Ternary Operator

D. Loops in Python

E. The enumerate() Function

F. Generators

G. List Comprehensions

VII. File Processing

A. Opening Files

B. The os and os.path Modules

VIII. Exception Handling

A. Wildcard except Clauses

B. Getting Information on Exceptions

C. The else Clause

D. The finally Clause

E. Using Exceptions for Flow Control

F. Exception Hierarchy

IX. Dates and Times

A. Understanding Time

B. The time Module

C. The datetime Module

X. Running Python Scripts from the Command Line

A. sys.argv

XI. Introduction to Python Final Exam

Advanced Python

I. IPython Notebook

A. Getting Started with IPython Notebook

B. Creating Your First IPython Notebook

C. IPython Notebook Modes

D. Useful Shortcut Keys

E. Markdown

F. Magic Commands

G. Getting Help

II. Advanced Python Concepts

A. Advanced List Comprehensions

B. Collections Module

C. Mapping and Filtering

D. Lambda Functions

E. Advanced Sorting

F. Unpacking Sequences in Function Calls

G. Modules and Packages

III. Regular Expressions

A. Regular Expression Syntax

B. Python's Handling of Regular Expressions

IV. Working with Data

A. Databases


C. Getting Data from the Web




V. Classes and Objects

A. Creating Classes

B. Attributes, Methods and Properties

C. Extending Classes

D. Documenting Classes

E. Static, Class, Abstract Methods

F. Decorators

VI. Testing and Debugging

A. Creating Simulations

B. Testing for Performance

C. The unittest Module

VII. Unicode and Encoding

A. Encoding and Decoding Files in Python

B. Converting a File from cp1252 to UTF-8

VIII. Advanced Python Final Exam

Python Data Analysis with NumPy and pandas

I. NumPy

A. One-dimensional Arrays

B. Multi-dimensional Arrays

C. Getting Basic Information about an Array

D. NumPy Arrays Compared to Python Lists

E. Universal Functions

F. Modifying Parts of an Array

G. Adding a Row Vector to All Rows

H. Random Sampling

II. Pandas

A. Series and DataFrames

B. Accessing Elements from a Series

C. Series Alignment

D. Comparing One Series with Another

E. Element-wise Operations

F. Creating a DataFrame from NumPy Array

G. Creating a DataFrame from Series

H. Creating a DataFrame from a CSVl

I. Getting Columns and Rows

J. Cleaning Data

K. Combining Row and Column Selection

L. Scalar Data: at[] and iat[]

M. Boolean Selection

N. Plotting with matplotlib

III. Python Data Analysis with NumPy and pandas Final Exam

Python Programmer Final Exam

Python Programmer Final Project

Method of Instruction



Class participation