Data Analysis Courses

Analyze and communicate insights by preparing, querying, and visualizing data with Excel, SQL, and Python to support data-driven decisions.

Turn data into decisions using Excel, SQL basics, and audience-ready Tableau dashboards that tell clear stories.

Transform raw data into executive-ready insights using Excel, SQL fundamentals, and polished Power BI dashboards.

Analyze, clean, and visualize data using R and SQL to perform end-to-end statistical analysis and communicate insights effectively.

Analyze complex datasets and build predictive models by applying statistics and machine learning to deliver end-to-end data science solutions.

Build end-to-end analytics skills with Python and SQL—cleaning data, visualizing insights, and delivering business answers.

Build foundational data literacy skills and learn how AI tools can support everyday data work, from communicating insights to working more effectively with data.

Build Python foundations, automation, and web-friendly patterns to prepare for backend projects and API integrations.

Build and train neural networks with TensorFlow, from fundamentals to practical models that make real predictions.

Build and train deep learning models from scratch, including GPTs, by applying neural networks, optimization, and modern transformer techniques.

Train predictive models in Python, evaluate performance, and apply machine learning to real datasets for insights.

Master Tableau basics—cleaning, modeling, and building interactive dashboards that surface patterns and insights fast.

Develop data literacy while using Excel to organize, analyze, and visualize information with practical, business-ready techniques.

Master core Power BI workflows—cleaning data, modeling relationships, and crafting interactive reports for business stakeholders.

Clean, transform, and prepare messy datasets by applying core data cleaning techniques in Python to support reliable analysis.

Apply probability, sampling, and hypothesis testing in R to make confident, data-driven decisions with practice projects.

Apply probability, sampling, and hypothesis testing in Python to make confident, data-driven decisions with practice projects.

Collect, clean, and merge data from APIs and websites using Python, requests, and BeautifulSoup for analysis.