Free online courseData Science

Course Description

Turn raw data into clear answers and confident decisions with this free online Data Science course in the Technology and Programming category, focused on Artificial Intelligence and Machine Learning foundations. You will learn to work in R and RStudio from the ground up, developing the habits that make analysis reproducible, organized, and easy to share. Instead of getting stuck on setup or confusing tools, you will build practical fluency step by step, so you can spend more time solving problems and less time fighting your environment.

Along the way, you will practice importing, cleaning, and managing datasets, including common workflows that connect spreadsheets to analysis-ready tables. You will gain the ability to select, sort, reshape, and combine data intelligently, understand data types, and write scripts that keep your work consistent. This is the backbone of modern data science and a key skill for anyone moving toward analytics, machine learning, or AI-adjacent roles.

You will also develop strong statistical intuition using R, moving from probability and distributions to hypothesis testing and inference. By working through common test scenarios and learning how to validate assumptions, you will be able to interpret results responsibly and avoid frequent mistakes that lead to misleading conclusions. Concepts like confidence intervals, bootstrapping, and permutation-based reasoning will help you handle real-world uncertainty with more rigor.

Data science is communication as much as computation, so the course emphasizes visualization and interpretation. You will create and customize charts and plots, refine them for readability, and learn how to present relationships and patterns in a way that stakeholders can understand. This capability is essential when translating analysis into business actions, research findings, or product decisions.

Finally, you will progress into modeling with linear regression, including multiple predictors, categorical variables, dummy encoding, interaction effects, and model-checking practices. These skills provide a practical bridge toward machine learning, preparing you to think in terms of features, outcomes, and evaluation. If you are building a portfolio, preparing for interviews, or transitioning into a data-focused career, this course gives you a solid, hands-on foundation to confidently work with data end to end.