This course serves as an introduction to the data science principles required to tackle real-world, data-rich problems in business & academia, including:
Data acquisition, cleaning, & aggregation
Exploratory data analysis & visualization
Model creation & validation
Basic statistical & mathematical foundations for data science
Who the course is designed for:
You have a strong desire to learn data science through top-quality instruction, a basic understanding of data analysis techniques & an interest in improving their ability to tackle data-rich problems in a systematic, principled way. This course provides structure & accountability to ensure you stay on track, finish strong, & achieve your desired outcomes.
An understanding of problems solvable with data science & an ability to attack them from a statistical perspective.
An understanding of when to use supervised & unsupervised statistical learning methods on labeled & unlabeled data-rich problems.
The ability to create data analytical pipelines & applications in Python.
Familiarity with the Python data science ecosystem & the various tools needed to continue developing as a data scientist.
What you'll receive upon completion:
Certificate of completion
3.3 Continuing Education Units