I. Core Python: 3 weeks

Week 1

1. Introduction to Python Programming

• Introduction
• Variables and Assignment Statements
• Arithmetic
• Function print and an Intro to Single- and Double-Quoted Strings
• Triple-Quoted Strings
• Getting Input from the User
• Decision Making: The if Statement and Comparison Operators
• Objects and Dynamic Typing
• Intro to Data Science: Basic Descriptive Statistics

2. Control Statements and Program Development

• Introduction
• Algorithms
• Pseudocode
• Control Statements
• if Statement
• if…else and if…elif…else Statements
• while Statement
• for Statement: Iterables, Lists and Iterators & Built-In range Function
• Augmented Assignments
• Program Development: Sequence-Controlled Repetition: Requirements Statement, Pseudocode for the Algorithm, Coding the Algorithm in Python & Introduction to Formatted Strings
• Program Development: Sentinel-Controlled Repetition
• Program Development: Nested Control Statements
• Built-In Function range: A Deeper Look
• Using Type Decimal for Monetary Amounts
• break and continue Statements
• Boolean Operators and, or and not
• Intro to Data Science: Measures of Central Tendency—Mean, Median and Mode

3. Functions

• Introduction
• Defining Functions
• Functions with Multiple Parameters
• Random-Number Generation
• Case Study: A Game of Chance
• Python Standard Library
• math Module Functions
• Using IPython Tab Completion for Discovery
• Default Parameter Values
• Keyword Arguments
• Arbitrary Argument Lists
• Methods: Functions That Belong to Objects
• Scope Rules
• import
• Passing Arguments to Functions
• Function-Call Stack
• Functional-Style Programming
• Intro to Data Science: Measures of Dispersion

4. Sequences: Lists and Tuples

• Introduction
• Lists
• Tuples
• Unpacking Sequences
• Sequence Slicing
• del Statement
• Passing Lists to Functions
• Sorting Lists
• Searching Sequences
• Other List Methods
• Simulating Stacks with Lists

• List Comprehensions
• Generator Expressions
• Filter, Map and Reduce
• Other Sequence Processing Functions
• Two-Dimensional Lists
• Intro to Data Science: Simulation and Static Visualizations

Week 2

5. Dictionaries and Sets

• Introduction
• Dictionaries: Creating a Dictionary, Iterating through a Dictionary, Basic Dictionary Operations, Dictionary Methods keys and values, Dictionary Comparisons, Dictionary Method update, & Dictionary Comprehensions
• Sets: Comparing Sets, Mathematical Set Operations, Mutable Set Operators and Methods, & Set Comprehensions
• Intro to Data Science: Dynamic Visualizations

6. Array-Oriented Programming with NumPy

• Introduction
• Creating arrays from Existing Data
• array Attributes
• Filling arrays with Specific Values
• Creating arrays from Ranges
• List vs. array Performance: Introducing %timeit
• array Operators
• NumPy Calculation Methods
• Universal Functions
• Indexing and Slicing
• Views: Shallow Copies
• Deep Copies
• Reshaping and Transposing
• Intro to Data Science: pandas Series and DataFrames

7. Strings: A Deeper Look

• Introduction
• Formatting Strings: Presentation Types, Field Widths and Alignment, Numeric Formatting, & String’s format Method
• Concatenating and Repeating Strings
• Stripping Whitespace from Strings
• Changing Character Case
• Comparison Operators for Strings
• Searching for Substrings
• Replacing Substrings
• Splitting and Joining Strings
• Characters and Character-Testing Methods
• Raw Strings
• Introduction to Regular Expressions: re Module and Function fullmatch, Replacing Substrings and Splitting Strings, & Other Search Functions; Accessing Matches
• Intro to Data Science: Pandas, Regular Expressions and Data Munging

8. Files and Exceptions

• Introduction
• Files
• Text-File Processing: Writing to a Text File, Reading Data from a Text File
• Updating Text Files
• Serialization with JSON
• Focus on Security: pickle Serialization and Deserialization
• Additional Notes Regarding Files
• Handling Exceptions: Division by Zero and Invalid Input, try Statements, Catching Multiple Exceptions in One except Clause, What Exceptions Does a Function or Method Raise?, & What Code Should Be Placed in a try Suite?
• finally Clause
• Explicitly Raising an Exception
• (Optional) Stack Unwinding and Tracebacks
• Intro to Data Science: Working with CSV Files

Week 3

9. Object-Oriented Programming

• Introduction
• A Custom Class: Composition: Object References as Members of Classes
• Controlling Access to Attributes
• Properties for Data Access
• Simulating “Private” Attributes
• Case Study: Card Shuffling and Dealing Simulation
• Inheritance: Base Classes and Subclasses
• Building an Inheritance Hierarchy; Introducing Polymorphism
• Duck Typing and Polymorphism
• Operator Overloading
• Exception Class Hierarchy and Custom Exceptions
• Named Tuples
• A Brief Intro to Python 3.7’s New Data Classes: Creating a Data Class, Using the Data Class, Data Class Advantages over Named Tuples, & Data Class Advantages over Traditional Classes
• Unit Testing with Docstrings and doctest
• Namespaces and Scopes
• Intro to Data Science: Time Series and Simple Linear Regression