Working in a hands-on learning environment, guided by our expert team, attendees will learn about and explore: 

• Core Python Proficiency: By the close of the course, participants will have a firm grasp on the foundational elements of Python, such as variables, data types, and flow control, empowering them to write scripts and build simple programs with confidence. 
• Analytical Problem-Solving: Utilizing libraries such as NumPy and SciPy, students will develop the ability to perform complex mathematical operations and statistical analyses, significantly amplifying their analytical capabilities for tasks such as data modeling or optimization problems. 
• Data Manipulation Mastery: By the end of the course, participants will be proficient in employing Pandas to clean, transform, and analyze data sets, enabling them to make data-driven decisions effectively. 
• Automated Workflow Development: Students will acquire the ability to construct automated scripts using Python's Standard Library, optimizing repetitive tasks and thereby enhancing operational efficiency in their organizations. 
• Advanced Data Visualization: Upon course completion, learners will be equipped to utilize Matplotlib and other Python libraries to craft intricate visual representations of data, facilitating clearer and more impactful reporting and presentations. 
• Error-Resilient Coding: Attendees will learn best practices for implementing robust error and exception handling techniques, leading to the creation of more stable and secure Python applications. 
• Modular Programming Proficiency: By mastering Python functions, modules, and packages, students will be adept at developing modular and maintainable code, a key skill for scalability and collaborative programming projects. 

This introductory-level course is geared for technical professionals new to Python. Roles include data analysts, developers, engineers or anyone tasked with utilizing Python for data analytics tasks. Familiarity with basic scripting skills is recommended, as this course does not teach general scripting basics. 

Please note that this list of topics is based on our standard course offering, evolved from typical industry uses and trends. We will work with you to tune this course and level of coverage to target the skills you need most. Course agenda, topics and labs are subject to adjust during live delivery in response to student skill level, interests and participation.  

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