Introduction to Python

This 4 day course is designed to provide Developers and/or Data Analysts a gentle immersive hands-on introduction to the Python programming language; it also introduces Python for Data Science.

Introduction to Python

• 1.1 What is Python
• 1.2 Uses of Python
• 1.3 Installing Python
• 1.4 Python Package Manager (PIP)
• 1.5 Using the Python Shell
• 1.6 Python Code Conventions
• 1.7 Importing Modules
• 1.8 The Help(object) Command
• 1.9 The Help Prompt

Python Scripts

• 2.1 Executing Python Code
• 2.2 Python Scripts
• 2.3 Writing Scripts
• 2.4 Running Python Scripts
• 2.5 Self Executing Scripts
• 2.6 Accepting Command-Line Parameters
• 2.7 Accepting Interactive Input
• 2.8 Retrieving Environment Settings

Data Types and Variables

• 3.1 Creating Variables
• 3.2 Displaying Variables
• 3.3 Basic Concatenation
• 3.4 Data Types
• 3.5 Strings
• 3.6 Strings as Arrays
• 3.7 String Methods
• 3.8 Combining Strings and Numbers
• 3.9 Numeric Types
• 3.10 Integer Types
• 3.11 Floating Point Types
• 3.12 Boolean Types
• 3.13 Checking Data Type

Python Collections

• 4.1 Python Collections
• 4.2 List Type
• 4.3 Modifying Lists
• 4.4 Sorting a List
• 4.5 Tuple Type
• 4.6 Python Sets
• 4.7 Modifying Sets
• 4.8 Dictionary (Map) Type
• 4.9 Dictionary Methods
• 4.10 Sequences

Data Type Conversions in Python

• 5.1 Data Type Conversions
• 5.2 Conversions from other Types to Integer
• 5.3 Conversions from other Types to Float
• 5.4 Conversions from other Types to String
• 5.5 Conversions from other Types to Boolean
• 5.6 Converting Between Set, List and Tuple Data Structures
• 5.7 Modifying Tuples
• 5.8 Combining Set, List and Tuple Data Structures
• 5.9 Creating Dictionaries from other Data Structures

Python Objects

• 6.1 Scope and Namespace
• 6.2 Introduction to Objects
• 6.3 Class variables (self)
• 6.4 Methods
• 6.5 Inheritance
• 6.6 Introduction to creating Packages
• 6.7 Virtual Environments
• 6.8 Testing – a real example where we use an object

Control Statements and Looping

• 7.1 If Statement
• 7.2 elif Keyword
• 7.3 Boolean Conditions
• 7.4 Single Line If Statements
• 7.5 For-in Loops
• 7.6 Looping over an Index
• 7.7 Range Function
• 7.8 Nested Loops
• 7.9 While Loops
• 7.10 Exception Handling
• 7.11 Built-in Exceptions
• 7.12 Exceptions thrown by Built-In Functions

Reading and Writing Text Files

• 8.1 Opening a File
• 8.2 Writing a File
• 8.3 Reading a File
• 8.4 Appending to a File
• 8.5 File Operations Using the With Statement
• 8.6 File and Directory Operations
• 8.7 Reading JSON
• 8.8 Writing JSON

Functions in Python

• 9.1 Defining Functions
• 9.2 Using Functions
• 9.3 Function Parameters
• 9.4 Named Parameters
• 9.5 Variable Length Parameter List
• 9.6 How Parameters are Passed
• 9.7 Variable Scope
• 9.8 Returning Values

Python Modules and Code Reuse

• 10.1 Code Organization in Python
• 10.2 Python Modules
• 10.3 Python Packages
• 10.4 Import Statements
• 10.5 The Package Initialization File

Functional Programming Primer

• 11.1 What is Functional Programming
• 11.2 Benefits of Functional Programming
• 11.3 Functions as Data
• 11.4 Using Map Function
• 11.5 Using Filter Function
• 11.6 Lambda expressions
• 11.7 List.sort() Using Lambda Expression
• 11.8 Difference Between Simple Loops and map/filter Type Functions
• 11.9 Additional Functions

Python Standard Library

• 12.1 Brief Tour of the Standard Library — Part I
• 12.1.1. Operating System Interface
• 12.1.2. File Wildcards
• 12.1.3. Command Line Arguments
• 12.1.4. Error Output Redirection and Program Termination
• 12.1.5. String Pattern Matching
• 12.1.6. Mathematics
• 12.1.7. Internet Access
• 12.1.8. Dates and Times
• 12.1.9. Data Compression
• 12.1.10. Performance Measurement
• 12.1.11. Quality Control
• 12.1.12. Batteries Included
• 12.2 Brief Tour of the Standard Library — Part II
• 12.2.1. Output Formatting
• 12.2.2. Templating
• 12.2.3. Working with Binary Data Record Layouts
• 12.2.4. Multi-threading
• 12.2.5. Logging
• 12.2.6. Weak References
• 12.2.7. Tools for Working with Lists
• 12.2.8. Decimal Floating Point Arithmetic

Python for Data Science

• 13.2 Importing Modules
• 13.3 Listing Methods in a Module
• 13.4 Creating Your Own Modules
• 13.5 Random Numbers
• 13.6 Zipping Lists
• 13.7 List Comprehension
• 13.8 Python Data Science-Centric Libraries
• 13.9 NumPy
• 13.10 NumPy Arrays
• 13.11 Select NumPy Operations
• 13.12 SciPy
• 13.13 pandas
• 13.14 Creating a pandas DataFrame
• 13.15 Fetching and Sorting Data
• 13.16 Scikit-learn
• 13.17 Matplotlib
• 13.18 Python Dev Tools and REPLs
• 13.19 IPython
• 13.20 Jupyter
• 13.21 Jupyter Operation Modes
• 13.22 Jupyter Common Commands
• 13.23 Anaconda

Exception Handling, Error Logging and Debugging

• 14.1 Handling exception with try
• 14.2 Error hierarchy
• 14.3 Catch / throw
• 14.4 Stepping through code in VS Code

Pulling Data from a Database to a Flat file

• 15.1 DB2
• 15.2 MS SQLServer

Test Driven Development

• 16.1 Unit testing vs functional testing vs integration testing
• 16.2 Basic unit testing framework example
• 16.3 Testing – a real example (using an object)