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Course #:WA2703

Introduction to Python Development Training

This 3 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.

Audience

Developers and/or Data Analysts

Duration

Three days

Outline of Introduction to Python Development Training

Chapter 1 - 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
  • 1.10 Summary

Chapter 2 - 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
  • 2.9 Summary

Chapter 3 - 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
  • 3.14 Summary

Chapter 4 - 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
  • 4.11 Summary

Chapter 5 – 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
  • 5.10 Summary

Chapter 6 - 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

Chapter 7 - 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
  • 7.13 Summary

Chapter 8 - 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
  • 8.9 Summary

Chapter 9 - 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
  • 9.9 Summary

Chapter 10. 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

Chapter 11 - 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
  • 11.10 Summary

Chapter 12 – 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

Chapter 13 - Python for Data Science

  • 13.1 In-Class Discussion
  • 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
  • 13.24 Summary

Chapter 14. 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

Chapter 15 - Pulling Data from a Database to a Flat file

  • 15.1 DB2
  • 15.2 MS SQLServer
  • 15.3 Homework: Pull data from both DB2 and SQLServer inside of Mayo using Python.

Chapter 16 - 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)
We regularly offer classes in these and other cities. Atlanta, Austin, Baltimore, Calgary, Chicago, Cleveland, Dallas, Denver, Detroit, Houston, Jacksonville, Miami, Montreal, New York City, Orlando, Ottawa, Philadelphia, Phoenix, Pittsburgh, Seattle, Toronto, Vancouver, Washington DC.
US Inquiries / 1.877.517.6540
Canadian Inquiries / 1.877.812.8887