This course builds directly on Intro to Python and introduces students to the essential tools and workflows for applying Python in data science.
Who Should Attend:
This course is ideal for professionals across federal and commercial sectors looking to begin their data science journey. The course is especially beneficial for:
- Aspiring data scientists
- Analysts looking to automate tasks
- Cybersecurity and intelligence professionals upskilling into technical roles
- Technical managers seeking to better understand the tools their teams use
Scope Statement:
This course builds directly on Intro to Python and introduces learners to the essential tools and workflows for applying Python in data science. Learners will learn how to handle, analyze, and visualize data using industry-standard Python libraries. The emphasis is on hands-on practice with real datasets to bridge the gap between coding basics and applied data science.
Learners Will:
- Introduction to NumPy for numerical computation
- Introduction to pandas for working with datasets (loading, cleaning, exploring data)
- Introduction to statistics and hypothesis testing using SciPy
- Basic data visualization using matplotlib and seaborn
- Understanding and applying descriptive statistics in Python
- Working with CSV/Excel files and exploring structured data
- Writing simple Python scripts that answer data-driven questions
Course Features:
- Taught by experienced instructors from IntelliGenesis LLC
- Designed to meet mission-ready federal workforce needs
- Interactive environment using Jupyter Notebook
- Focused on clarity, simplicity, and practical application
- Prepares learners for more advanced AI, ML, and data engineering courses
Knowledge & Technology Competencies:
- Basic familiarity with Python syntax and programming concepts
- Experience executing and editing Jupyter Notebooks
- Familiarity with variables, data types, functions, loops, and basic Python programming
- Basic familiarity with navigating files and working with course-provided notebooks
- A PC or laptop capable of accessing the supplied virtual environment
- Reliable internet access
- Ability to interact with the supplied virtual machine or container environment
- A modern web browser for accessing the hosted Jupyter environment and course materials
No prior experience with NumPy, pandas, SciPy, matplotlib, seaborn, or formal data science workflows is required.
All required software, Python libraries, virtual environment resources, exercise notebooks, starter files, and datasets are provided.
For organizations purchasing this course, a minimum of 10 participants is required, with a maximum capacity of 25 participants.
