Learn the critical elements of Data Science, from visualization to databases to Python and more, in just 6 weeks!
InstructorKirill Eremenko
The curriculum spans several critical domains including Statistics, Machine Learning, and specialized Deep Learning topics like Computer Vision and NLP.
Instruction is organized into distinct sequences for Tableau, Python, and Cloud applications.
Editorial course preview
These 3 complementary views highlight concrete, legible examples from the course presentation.
A stylized technical diagram illustrates the course structure by connecting key concepts like Python and Machine Learning within a circular, high-tech interface.
An instructor stands beside a visual aid titled 'Success in Data Science,' which displays a schematic graph with connected nodes and markers on a grid background.
The image introduces the course instructor, Hadelin de Ponteves, displaying his name and title as a Machine Learning Expert in white text overlaid on the video frame.
Learner signals suggest the content can be presented rapidly, acting more as a collection of notions than deep technical training.
The curriculum lacks explicit projects, labs, or coding exercises to reinforce the theoretical concepts.
Some signals indicate the course provides a structure of what to learn rather than providing clear, direct instruction.
The curriculum aligns with the stated goal of introducing various data science tools and concepts to beginners.