Applied Python for Data Science and Engineering

1. Getting Started with the Python Environment
   
   • Starting Python
   • Using the interpreter
   • Running a Python script
   • Editors and IDEs
2. Variables and Values
   
   • Using variables
   • Builtin functions
   • String data
   • Numeric data
   • Converting types
3. Basic input and output
   
   • Writing to the screen
   • String formatting
   • Command line arguments
   • Reading the keyboard
4. Flow Control
   
   • About flow control
   • The if statement
   • Relational and Boolean values
   • while loops
   • Exiting from loops
5. Array types
   
   • Sequence types in general
   • Lists and list methods
   • Tuples
   • Indexing and slicing
   • Iterating through a sequence
   • Sequence functions, keywords, and operators
   • List comprehensions and generators
6. Working with files
   
   • File I/O overview
   • Opening a text file
   • Reading a text file
   • Writing to a text file
7. Dictionaries and Sets
   
   • About dictionaries
   • Creating dictionaries
   • Getting values
   • Iterating through a dictionary
   • About sets
   • Creating sets
   • Working with sets
8. Functions, modules, and packages
   
   • Returning values
   • Types of function parameters
   • Variable scoping
   • Documentation best practices
   • Creating and importing modules
   • Organizing modules into packages
9. Virtual Environments
   
   • Why do we need virtual environments
   • Creating an environment
   • Activating and deactivating
   • Replicating an environment
   • Tools for environments
10. Exception handling and logging
    
    • About exceptions
    • Using try/catch/else/finally
    • Handling multiple exceptions
    • Logging setup
    • Basic logging
11. Introduction to Python Classes
    
    • Defining classes
    • Constructors
    • Instance methods and data
    • Attributes
    • Inheritance
    • Multiple inheritance
12. Excel spreadsheets
    
    • The openpyxl module
    • Reading an existing spreadsheet
    • Creating a spreadsheet from scratch
    • Modifying an existing spreadsheet
13. Serializing Data
    
    • Using ElementTree
    • Creating a new XML document
    • Parsing XML
    • Finding by tags and XPath
    • Parsing JSON into Python
    • Parsing Python into JSON
    • Working with CSV
14. iPython and Jupyterlab
    
    • iPython features & iPython "magic" commands
    • iPython configuration
    • Creating Jupyter notebooks
    • Managing notebooks with Jupyterlab
15. Intro to NumPy
    
    • NumPy basics
    • Creating arrays
    • Indexing and slicing
    • Large number sets
    • Transforming data
    • SciPy overview
16. Intro to Pandas
    
    • Pandas overview
    • Series and Dataframes
    • Reading and writing data
    • Data summaries
    • Data alignment and reshaping
    • Selecting and indexing
    • Merging and joining data sets
    • Plotting data
17. Matplotlib
    
    • Creating a basic plot
    • Commonly used plots
    • Ad hoc data visualization
    • Advanced usage
    • Exporting images

Optional Topics or Day Five:
For Dedicated / Private Classes:

1. Introduction to AI with Python for Data Analysis
   
   • Overview of AI Libraries
   • Setting Up Your Environment:
   • Understanding AI Models
   • Creating Your First Model
   • Evaluating Model Performance
2. Practical AI Projects in Python
   
   • Set up a Python project for AI applications.
   • Data Handling
   • Model Development
   • Test and validate your AI model's effectiveness.
   • Applying Your Model
3. Using GPT Tools for Record Analysis in Data Science
   
   • Introduction to GPT
   • Setting Up GPT Tools
   • Analyzing Text Data
   • Generating Insights
   • Practical Applications

This course combines expert led instructor-led presentation with practical demonstrations with hands-on programming exercises, challenge labs, use case exploration and engaging activities. Student machines are required.