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)

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