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, 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 designed for technical professionals who are new to Python and want to use it for data analysis and data science workflows. Typical roles include data analysts, engineers, developers, and researchers transitioning from tools such as Excel or SQL.

Getting Started with the Python Environment 

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

iPython and Jupyterlab

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

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 

Intro to Pandas 

• Pandas overview 
• Series and Dataframes 
• Reading and writing data 
• Data summaries 
• Data alignment and reshaping 
• Selecting and indexing 
• Basic Data Plotting

Matplotlib 

• Creating a basic plot 
• Commonly used plots 
• Ad hoc data visualization 
• Leveraging Seaborn for better plots
• Exporting images 

 Intro to NumPy

• NumPy basics 
• Reading Data
• Creating arrays 
• Indexing and slicing 
• Large number sets 
• Transforming data