Applied Python for Data Science & Engineering

Course Overview

Getting Started with the Python Environment 

• Starting Python 
• Using the interpreter 
• Running a Python script 
• Editors and IDEs 

Variables and Values 

• Using variables 
• Builtin functions 
• String data 
• Numeric data 
• Converting types 

Basic input and output 

• Writing to the screen 
• String formatting 
• Command line arguments 
• Reading the keyboard 

Flow Control 

• About flow control 
• The if statement 
• Relational and Boolean values 
• while loops 
• Exiting from loops 

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 

Working with files 

• File I/O overview 
• Opening a text file 
• Reading a text file 
• Writing to a text file 

Dictionaries and Sets 

• About dictionaries 
• Creating dictionaries 
• Getting values 
• Iterating through a dictionary 
• About sets 
• Creating sets 
• Working with sets 

Functions, modules, and packages 

• Returning values 
• Types of function parameters 
• Variable scoping 
• Documentation best practices 
• Creating and importing modules 
• Organizing modules into packages 

Virtual Environments 

• Why do we need virtual environments 
• Creating an environment 
• Activating and deactivating 
• Replicating an environment 
• Tools for environments 

Exception handling and logging 

• About exceptions 
• Using try/catch/else/finally 
• Handling multiple exceptions 
• Logging setup 
• Basic logging 

Introduction to Python Classes 

• Defining classes 
• Constructors 
• Instance methods and data 
• Attributes 
• Inheritance 
• Multiple inheritance 

Excel spreadsheets 

• The openpyxl module 
• Reading an existing spreadsheet 
• Creating a spreadsheet from scratch 
• Modifying an existing spreadsheet 

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 

iPython and Jupyterlab 

• iPython features & iPython "magic" commands 
• iPython configuration 
• Creating Jupyter notebooks 
• Managing notebooks with Jupyterlab 

Intro to NumPy 

• NumPy basics 
• Creating arrays 
• Indexing and slicing 
• Large number sets 
• Transforming data 
• SciPy overview 

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 

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:  

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 

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 

Using GPT Tools for Record Analysis in Data Science 

• Introduction to GPT 
• Setting Up GPT Tools 
• Analyzing Text Data  
• Generating Insights  
• Practical Applications 

Prerequisites

Familiarity with basic scripting skills is recommended, as this course does not teach general scripting basics.